diff --git a/process/caller.py b/process/caller.py index 676f6f0272..444f167037 100644 --- a/process/caller.py +++ b/process/caller.py @@ -260,12 +260,16 @@ def _call_models_once(self, xc: np.ndarray) -> None: # Toroidal field coil model - self.models.tfcoil.run() + # Toroidal field coil resistive model + if ft.tfcoil_variables.i_tf_sup != 1: + self.models.resistive_tf_coil.run(output=False) # Toroidal field coil superconductor model if ft.tfcoil_variables.i_tf_sup == 1: self.models.sctfcoil.run(output=False) + self.models.build.portsz() + # Poloidal field and central solenoid model self.models.pfcoil.run() diff --git a/process/io/plot_proc.py b/process/io/plot_proc.py index 88fed5559f..5f054266fb 100644 --- a/process/io/plot_proc.py +++ b/process/io/plot_proc.py @@ -52,7 +52,7 @@ ) from process.impurity_radiation import read_impurity_file from process.objectives import OBJECTIVE_NAMES -from process.tfcoil import TF_TYPES +from process.superconducting_tf_coil import SUPERCONDUCTING_TF_TYPES if os.name == "posix" and "DISPLAY" not in os.environ: mpl.use("Agg") @@ -2849,7 +2849,9 @@ def plot_magnetics_info(axis, mfile_data, scan): i_tf_sc_mat = 0 if i_tf_sc_mat > 0: - tftype = TF_TYPES[int(mfile_data.data["i_tf_sc_mat"].get_scan(scan))] + tftype = SUPERCONDUCTING_TF_TYPES[ + int(mfile_data.data["i_tf_sc_mat"].get_scan(scan)) + ] else: tftype = "Resistive Copper" diff --git a/process/main.py b/process/main.py index db5c7e3a6f..c95b612ad5 100644 --- a/process/main.py +++ b/process/main.py @@ -86,11 +86,12 @@ from process.plasma_profiles import PlasmaProfile from process.power import Power from process.pulse import Pulse +from process.resistive_tf_coil import ResistiveTFCoil from process.scan import Scan -from process.sctfcoil import Sctfcoil from process.stellarator import Neoclassics, Stellarator from process.structure import Structure -from process.tfcoil import TFcoil +from process.superconducting_tf_coil import SuperconductingTFCoil +from process.tf_coil import TFCoil from process.utilities.f2py_string_patch import string_to_f2py_compatible from process.vacuum import Vacuum from process.water_use import WaterUse @@ -657,8 +658,9 @@ def __init__(self): self.power = Power() self.cryostat = Cryostat() self.build = Build() - self.sctfcoil = Sctfcoil() - self.tfcoil = TFcoil(build=self.build, sctfcoil=self.sctfcoil) + self.sctfcoil = SuperconductingTFCoil() + self.tfcoil = TFCoil(build=self.build) + self.resistive_tf_coil = ResistiveTFCoil() self.divertor = Divertor() self.structure = Structure() self.plasma_geom = PlasmaGeom() diff --git a/process/output.py b/process/output.py index afb31eab12..16cc4f67d9 100644 --- a/process/output.py +++ b/process/output.py @@ -61,8 +61,9 @@ def write(models, _outfile): # Cryostat build models.cryostat.cryostat_output() - # Toroidal field coil model - models.tfcoil.output() + # Toroidal field coil resistive model + if ft.tfcoil_variables.i_tf_sup != 1: + models.resistive_tf_coil.run(output=True) # Toroidal field coil superconductor model if ft.tfcoil_variables.i_tf_sup == 1: diff --git a/process/resistive_tf_coil.py b/process/resistive_tf_coil.py new file mode 100644 index 0000000000..a643bef834 --- /dev/null +++ b/process/resistive_tf_coil.py @@ -0,0 +1,886 @@ +import logging + +import numba +import numpy as np + +from process.fortran import ( + build_variables, + constants, + error_handling, + pfcoil_variables, + physics_variables, + sctfcoil_module, + tfcoil_variables, +) +from process.tf_coil import TFCoil + +logger = logging.getLogger(__name__) + +EPS = np.finfo(1.0).eps +RMU0 = constants.rmu0 + + +class ResistiveTFCoil(TFCoil): + def __init__(self): + self.outfile = constants.nout + + def run(self, output: bool): + """Run main tfcoil subroutine without outputting.""" + self.iprint = 0 + self.tf_global_geometry() + self.tf_current() + self.coilshap() + self.res_tf_internal_geom() + self.tf_res_heating() + + if physics_variables.itart == 0 and tfcoil_variables.i_tf_shape == 1: + tfcoil_variables.tfind = self.tfcind( + build_variables.dr_tf_inboard, + tfcoil_variables.xarc, + tfcoil_variables.yarc, + ) + else: + tfcoil_variables.tfind = ( + (build_variables.hmax + build_variables.dr_tf_outboard) + * RMU0 + / constants.pi + * np.log( + build_variables.r_tf_outboard_mid / build_variables.r_tf_inboard_mid + ) + ) + + # Total TF coil stored magnetic energy [J] + sctfcoil_module.estotft = ( + 0.5e0 * tfcoil_variables.tfind * tfcoil_variables.c_tf_total**2 + ) + + # Total TF coil stored magnetic energy [Gigajoule] + tfcoil_variables.estotftgj = 1.0e-9 * sctfcoil_module.estotft + + self.tf_field_and_force() + + # Calculate TF coil areas and masses + self.tf_coil_area_and_masses() + + # Do stress calculations (writes the stress output) + if output: + tfcoil_variables.n_rad_per_layer = 500 + + try: + ( + sig_tf_r_max, + sig_tf_t_max, + sig_tf_z_max, + sig_tf_vmises_max, + s_shear_tf_peak, + deflect, + eyoung_axial, + eyoung_trans, + eyoung_wp_axial, + eyoung_wp_trans, + poisson_wp_trans, + radial_array, + s_shear_cea_tf_cond, + poisson_wp_axial, + sig_tf_r, + sig_tf_smeared_r, + sig_tf_smeared_t, + sig_tf_smeared_z, + sig_tf_t, + s_shear_tf, + sig_tf_vmises, + sig_tf_z, + str_tf_r, + str_tf_t, + str_tf_z, + n_radial_array, + n_tf_bucking, + tfcoil_variables.sig_tf_wp, + sig_tf_case, + sig_tf_cs_bucked, + str_wp, + casestr, + insstrain, + sig_tf_wp_av_z, + ) = self.stresscl( + int(tfcoil_variables.n_tf_stress_layers), + int(tfcoil_variables.n_rad_per_layer), + int(tfcoil_variables.n_tf_wp_layers), + int(tfcoil_variables.i_tf_bucking), + float(build_variables.r_tf_inboard_in), + build_variables.dr_bore, + build_variables.hmax, + pfcoil_variables.f_z_cs_tf_internal, + build_variables.dr_cs, + build_variables.i_tf_inside_cs, + build_variables.dr_tf_inboard, + build_variables.dr_cs_tf_gap, + pfcoil_variables.i_pf_conductor, + pfcoil_variables.j_cs_flat_top_end, + pfcoil_variables.j_cs_pulse_start, + pfcoil_variables.c_pf_coil_turn_peak_input, + pfcoil_variables.n_pf_coils_in_group, + pfcoil_variables.ld_ratio_cst, + pfcoil_variables.r_out_cst, + pfcoil_variables.f_a_cs_steel, + tfcoil_variables.eyoung_steel, + tfcoil_variables.poisson_steel, + tfcoil_variables.eyoung_cond_axial, + tfcoil_variables.poisson_cond_axial, + tfcoil_variables.eyoung_cond_trans, + tfcoil_variables.poisson_cond_trans, + tfcoil_variables.eyoung_ins, + tfcoil_variables.poisson_ins, + tfcoil_variables.thicndut, + tfcoil_variables.eyoung_copper, + tfcoil_variables.poisson_copper, + tfcoil_variables.i_tf_sup, + tfcoil_variables.eyoung_res_tf_buck, + sctfcoil_module.r_wp_inner, + sctfcoil_module.tan_theta_coil, + sctfcoil_module.theta_coil, + sctfcoil_module.r_wp_outer, + sctfcoil_module.a_tf_steel, + sctfcoil_module.a_case_front, + sctfcoil_module.a_case_nose, + tfcoil_variables.tfinsgap, + tfcoil_variables.tinstf, + tfcoil_variables.n_tf_turn, + int(tfcoil_variables.i_tf_turns_integer), + sctfcoil_module.t_cable, + sctfcoil_module.t_cable_radial, + tfcoil_variables.dhecoil, + tfcoil_variables.fcutfsu, + tfcoil_variables.thwcndut, + sctfcoil_module.t_lat_case_av, + sctfcoil_module.t_wp_toroidal_av, + sctfcoil_module.a_tf_ins, + tfcoil_variables.aswp, + tfcoil_variables.acond, + sctfcoil_module.awpc, + tfcoil_variables.eyoung_al, + tfcoil_variables.poisson_al, + tfcoil_variables.fcoolcp, + tfcoil_variables.n_tf_graded_layers, + tfcoil_variables.c_tf_total, + tfcoil_variables.casthi, + tfcoil_variables.i_tf_stress_model, + sctfcoil_module.vforce_inboard_tot, + tfcoil_variables.i_tf_tresca, + tfcoil_variables.acasetf, + tfcoil_variables.vforce, + tfcoil_variables.acndttf, + ) + + tfcoil_variables.sig_tf_case = ( + tfcoil_variables.sig_tf_case + if tfcoil_variables.sig_tf_case is None + else sig_tf_case + ) + + tfcoil_variables.sig_tf_cs_bucked = ( + tfcoil_variables.sig_tf_cs_bucked + if tfcoil_variables.sig_tf_cs_bucked is None + else sig_tf_cs_bucked + ) + + tfcoil_variables.str_wp = ( + tfcoil_variables.str_wp if tfcoil_variables.str_wp is None else str_wp + ) + + tfcoil_variables.casestr = ( + tfcoil_variables.casestr + if tfcoil_variables.casestr is None + else casestr + ) + + tfcoil_variables.insstrain = ( + tfcoil_variables.insstrain + if tfcoil_variables.insstrain is None + else insstrain + ) + + if output: + self.out_stress( + sig_tf_r_max, + sig_tf_t_max, + sig_tf_z_max, + sig_tf_vmises_max, + s_shear_tf_peak, + deflect, + eyoung_axial, + eyoung_trans, + eyoung_wp_axial, + eyoung_wp_trans, + poisson_wp_trans, + radial_array, + s_shear_cea_tf_cond, + poisson_wp_axial, + sig_tf_r, + sig_tf_smeared_r, + sig_tf_smeared_t, + sig_tf_smeared_z, + sig_tf_t, + s_shear_tf, + sig_tf_vmises, + sig_tf_z, + str_tf_r, + str_tf_t, + str_tf_z, + n_radial_array, + n_tf_bucking, + sig_tf_wp_av_z, + ) + except ValueError as e: + if e.args[1] == 245 and e.args[2] == 0: + error_handling.report_error(245) + tfcoil_variables.sig_tf_case = 0.0e0 + tfcoil_variables.sig_tf_wp = 0.0e0 + + def res_tf_internal_geom(self): + """ + Author : S. Kahn + Resisitve TF turn geometry, equivalent to winding_pack subroutines + + """ + sctfcoil_module.r_wp_inner = ( + build_variables.r_tf_inboard_in + tfcoil_variables.thkcas + ) + sctfcoil_module.r_wp_outer = ( + build_variables.r_tf_inboard_out - tfcoil_variables.casthi + ) + + # Conductor layer radial thickness at centercollumn top [m] + if physics_variables.itart == 1: + sctfcoil_module.dr_tf_wp_top = ( + build_variables.r_cp_top + - tfcoil_variables.casthi + - tfcoil_variables.thkcas + - build_variables.r_tf_inboard_in + ) + + # Number of turns + # Set by user (no turn structure by default, i.e. tfcoil_variables.n_tf_turn = 1 ) + if ( + abs(tfcoil_variables.n_tf_turn) + < np.finfo(float(tfcoil_variables.n_tf_turn)).eps + ): + tfcoil_variables.n_tf_turn = 1.0e0 + + # Total mid-plane cross-sectional area of winding pack, [m2] + # including the surrounding ground-wall insulation layer + sctfcoil_module.awpc = ( + np.pi + * (sctfcoil_module.r_wp_outer**2 - sctfcoil_module.r_wp_inner**2) + / tfcoil_variables.n_tf_coils + ) + + # Area of the front case, the plasma-facing case of the inner TF coil [m2] + sctfcoil_module.a_case_front = ( + np.pi + * ( + (sctfcoil_module.r_wp_outer + tfcoil_variables.casthi) ** 2 + - sctfcoil_module.r_wp_outer**2 + ) + / tfcoil_variables.n_tf_coils + ) + + # WP mid-plane cross-section excluding ground insulation per coil [m2] + sctfcoil_module.awptf = np.pi * ( + (sctfcoil_module.r_wp_outer - tfcoil_variables.tinstf) ** 2 + - (sctfcoil_module.r_wp_inner + tfcoil_variables.tinstf) ** 2 + ) / tfcoil_variables.n_tf_coils - 2.0e0 * tfcoil_variables.tinstf * ( + tfcoil_variables.dr_tf_wp - 2.0e0 * tfcoil_variables.tinstf + ) + + # Ground insulation cross-section area per coil [m2] + sctfcoil_module.a_ground_ins = sctfcoil_module.awpc - sctfcoil_module.awptf + + # Exact mid-plane cross-section area of the conductor per TF coil [m2] + a_tf_cond = np.pi * ( + ( + sctfcoil_module.r_wp_outer + - tfcoil_variables.tinstf + - tfcoil_variables.thicndut + ) + ** 2 + - ( + sctfcoil_module.r_wp_inner + + tfcoil_variables.tinstf + + tfcoil_variables.thicndut + ) + ** 2 + ) / tfcoil_variables.n_tf_coils - ( + tfcoil_variables.dr_tf_wp + - 2.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.thicndut) + ) * 2.0e0 * ( + tfcoil_variables.tinstf + + tfcoil_variables.thicndut * tfcoil_variables.n_tf_turn + ) + a_tf_cond = a_tf_cond * (1.0e0 - tfcoil_variables.fcoolcp) + + # Inter turn insulation area per coil [m2] + tfcoil_variables.aiwp = sctfcoil_module.awptf - a_tf_cond / ( + 1.0e0 - tfcoil_variables.fcoolcp + ) + + # Total insulation cross-section per coil [m2] + sctfcoil_module.a_tf_ins = tfcoil_variables.aiwp + sctfcoil_module.a_ground_ins + + # Insulation fraction [-] + sctfcoil_module.f_tf_ins = ( + tfcoil_variables.n_tf_coils + * sctfcoil_module.a_tf_ins + / tfcoil_variables.tfareain + ) + + # Total cross-sectional area of the bucking cylindre and the outer support + # support structure per coil [m2] + # physics_variables.itart = 1 : Only valid at mid-plane + tfcoil_variables.acasetf = ( + tfcoil_variables.tfareain / tfcoil_variables.n_tf_coils + ) - sctfcoil_module.awpc + + # Current per turn + tfcoil_variables.cpttf = tfcoil_variables.c_tf_total / ( + tfcoil_variables.n_tf_turn * tfcoil_variables.n_tf_coils + ) + + # Exact current density on TF oubard legs + tfcoil_variables.cdtfleg = tfcoil_variables.c_tf_total / ( + (1.0e0 - tfcoil_variables.fcoolcp) + * ( + tfcoil_variables.tftort + - 2.0e0 + * ( + tfcoil_variables.n_tf_turn * tfcoil_variables.thicndut + + tfcoil_variables.tinstf + ) + ) + * ( + build_variables.dr_tf_outboard + - 2.0e0 * (tfcoil_variables.thicndut + tfcoil_variables.tinstf) + ) + ) + + # Reporting negative WP areas issues + if sctfcoil_module.awpc < 0.0e0: + error_handling.fdiags[0] = sctfcoil_module.awpc + error_handling.fdiags[0] = tfcoil_variables.dr_tf_wp + error_handling.report_error(99) + + elif sctfcoil_module.awptf < 0.0e0: + error_handling.fdiags[0] = sctfcoil_module.awptf + error_handling.report_error(101) + + def tf_res_heating(self) -> None: + """ + Calculate resistive heating for resistive magnets. + + This method calculates the resistive heating for resistive magnets. + It considers the following scenarios: + - Clamped joints in superconductors might have resistive power losses on the joints. + - Sliding joints might have a region of high resistivity. + + Notes: + - The copper resisitivty is set to be that for GLIDCOP AL-15 at 20°C for copper (i_tf_sup = 0). + - The coefficient of resistivity is set to be that of pure copper + + References: + - https://www.spotweldingconsultants.com/GlidCop_AL_15.pdf + + - https://cirris.com/temperature-coefficient-of-copper/ + """ + + # Resistivity of the Glidcop copper centerpost + if tfcoil_variables.i_tf_sup == 0: + tfcoil_variables.rho_cp = ( + # 1.86 is the resitivity at `20°C` for GLIDCOP AL-15 + # 0.00393 is the coefficient of resistivity for copper + tfcoil_variables.frhocp + * (1.86e0 + 0.00393e0 * (tfcoil_variables.temp_cp_average - 293.15e0)) + * 1.0e-8 + ) + + # Resistivity of the aluminium centerpost + if tfcoil_variables.i_tf_sup == 2: + tfcoil_variables.rho_cp = tfcoil_variables.frhocp * ( + 2.00016e-14 * tfcoil_variables.temp_cp_average**3 + - 6.75384e-13 * tfcoil_variables.temp_cp_average**2 + + 8.89159e-12 * tfcoil_variables.temp_cp_average + ) + + # Calculations dedicated for configurations with CP + if physics_variables.itart == 1: + # Tricky trick to make the leg / CP tempearture the same + if ( + abs(tfcoil_variables.temp_tf_legs_outboard + 1.0e0) + < np.finfo(float(tfcoil_variables.temp_tf_legs_outboard)).eps + ): + sctfcoil_module.is_leg_cp_temp_same = 1 + tfcoil_variables.temp_tf_legs_outboard = ( + tfcoil_variables.temp_cp_average + ) + + # Leg resistivity (different leg temperature as separate cooling channels) + if tfcoil_variables.i_tf_sup == 0: + tfcoil_variables.rho_tf_leg = ( + tfcoil_variables.frholeg + * ( + 1.86e0 + + 0.00393e0 + * (tfcoil_variables.temp_tf_legs_outboard - 293.15e0) + ) + * 1.0e-8 + ) + elif tfcoil_variables.i_tf_sup == 2: + tfcoil_variables.rho_tf_leg = tfcoil_variables.frholeg * ( + 2.00016e-14 * tfcoil_variables.temp_tf_legs_outboard**3 + - 6.75384e-13 * tfcoil_variables.temp_tf_legs_outboard**2 + + 8.89159e-12 * tfcoil_variables.temp_tf_legs_outboard + ) + + # Tricky trick to make the leg / CP tempearture the same + if sctfcoil_module.is_leg_cp_temp_same == 1: + tfcoil_variables.temp_tf_legs_outboard = -1.0e0 + + # Centrepost resisitivity and conductor/insulation volume + + ( + tfcoil_variables.a_cp_cool, + tfcoil_variables.vol_cond_cp, + tfcoil_variables.p_cp_resistive, + sctfcoil_module.vol_ins_cp, + sctfcoil_module.vol_case_cp, + sctfcoil_module.vol_gr_ins_cp, + ) = self.cpost( + build_variables.r_tf_inboard_in, + build_variables.r_tf_inboard_out, + build_variables.r_cp_top, + sctfcoil_module.h_cp_top, + build_variables.hmax + build_variables.dr_tf_outboard, + tfcoil_variables.thkcas, + tfcoil_variables.casthi, + tfcoil_variables.tinstf, + tfcoil_variables.thicndut, + tfcoil_variables.n_tf_turn, + tfcoil_variables.c_tf_total, + tfcoil_variables.rho_cp, + tfcoil_variables.fcoolcp, + tfcoil_variables.n_tf_coils, + ) + + # Leg cross-section areas + # Rem : For physics_variables.itart = 1, these quantitire corresponds to the outer leg only + # --- + # Leg ground insulation area per coil [m2] + sctfcoil_module.a_leg_gr_ins = tfcoil_variables.a_tf_leg_outboard - ( + tfcoil_variables.tftort - 2.0e0 * tfcoil_variables.tinstf + ) * (build_variables.dr_tf_outboard - 2.0e0 * tfcoil_variables.tinstf) + + # Outboard leg turns insulation area per coil [m2] + sctfcoil_module.a_leg_ins = 2.0e0 * tfcoil_variables.thicndut * ( + tfcoil_variables.tftort - 2.0e0 * tfcoil_variables.tinstf + ) + 2.0e0 * tfcoil_variables.thicndut * tfcoil_variables.n_tf_turn * ( + build_variables.dr_tf_outboard + - 2.0e0 * (tfcoil_variables.thicndut + tfcoil_variables.tinstf) + ) # toroidal direction + radial direction + + # Exact TF outboard leg conductor area per coil [m2] + sctfcoil_module.a_leg_cond = (1.0e0 - tfcoil_variables.f_a_tf_cool_outboard) * ( + tfcoil_variables.a_tf_leg_outboard + - sctfcoil_module.a_leg_gr_ins + - sctfcoil_module.a_leg_ins + ) + # --- + + if physics_variables.itart == 1: + # Outer leg resistive power loss + # --- + # TF outboard leg's resistance calculation (per leg) [ohm] + tfcoil_variables.res_tf_leg = ( + tfcoil_variables.rho_tf_leg + * tfcoil_variables.len_tf_coil + / sctfcoil_module.a_leg_cond + ) + + # TF outer leg resistive power (TOTAL) [W] + tfcoil_variables.p_tf_leg_resistive = ( + tfcoil_variables.res_tf_leg + * (tfcoil_variables.c_tf_total / tfcoil_variables.n_tf_coils) ** 2 + ) * tfcoil_variables.n_tf_coils + # --- + + # Sliding joints resistive heating + # --- + if tfcoil_variables.i_cp_joints != 0: + # Number of contact area per joint (all legs) + n_contact_tot = ( + tfcoil_variables.n_tf_joints_contact + * np.round(tfcoil_variables.n_tf_turn) + * np.round(tfcoil_variables.n_tf_coils) + ) + + # Area of joint contact (all legs) + a_joints = ( + build_variables.dr_tf_outboard + * tfcoil_variables.th_joint_contact + * n_contact_tot + ) + + # Total joints resistive power losses + tfcoil_variables.pres_joints = ( + tfcoil_variables.n_tf_joints + * tfcoil_variables.rho_tf_joints + * tfcoil_variables.c_tf_total**2 + / a_joints + ) + else: + # Joints resistance to be evaluated for SC + tfcoil_variables.pres_joints = 0.0e0 + + # --- + + # Case of a resistive magnet without joints + # *** + else: + # TF resistive powers + tfcoil_variables.p_cp_resistive = ( + tfcoil_variables.rho_cp + * tfcoil_variables.c_tf_total**2 + * tfcoil_variables.len_tf_coil + / (sctfcoil_module.a_leg_cond * tfcoil_variables.n_tf_coils) + ) + + # tfcoil_variables.p_cp_resistive containts the the total resistive power losses + tfcoil_variables.p_tf_leg_resistive = 0.0e0 + + # No joints if physics_variables.itart = 0 + tfcoil_variables.pres_joints = 0.0e0 + + @staticmethod + @numba.njit(cache=True) + def cpost( + r_tf_inboard_in, + r_tf_inboard_out, + r_cp_top, + ztop, + hmaxi, + cas_in_th, + cas_out_th, + gr_ins_th, + ins_th, + n_tf_turn, + curr, + rho, + fcool, + n_tf_coils, + ): + """ + author: P J Knight, CCFE, Culham Science Centre + Calculates the volume and resistive power losses of a TART centrepost + This routine calculates the volume and resistive power losses + of a TART centrepost. It is assumed to be tapered - narrowest at + the midplane and reaching maximum thickness at the height of the + plasma. Above/below the plasma, the centrepost is cylindrical. + The shape of the taper is assumed to be an arc of a circle. + P J Knight, CCFE, Culham Science Centre + 21/10/96 PJK Initial version + 08/05/12 PJK Initial F90 version + 16/10/12 PJK Added constants; removed argument pi + 26/06/14 PJK Added error handling + 12/11/19 SK Using fixed cooling cross-section area along the CP + 26/11/19 SK added the coolant area, the conuctor/isulator/outer casing volume + 30/11/20 SK added the ground outer ground insulation volume + F/MI/PJK/LOGBOOK12, pp.33,34 + """ + yy_ins = np.zeros((101,)) # Exact conductor area (to be integrated) + yy_cond = np.zeros((101,)) # Turn insulation area (to be integrated) + yy_gr_ins = np.zeros((101,)) # Outter ground insulation area (to be integrated) + yy_casout = np.zeros((101,)) # Outter case area (to be integrated) + + rtop = r_cp_top - cas_out_th - gr_ins_th + + # Conductor outer radius at CP mid-plane [m] + rmid = r_tf_inboard_out - cas_out_th - gr_ins_th + + # Conductor inner radius [m] + r_tfin_inleg = r_tf_inboard_in + cas_in_th + gr_ins_th + # -# + + # Error traps + # ------------ + # if rtop <= 0.0e0: + # error_handling.fdiags[0] = rtop + # error_handling.report_error(115) + + # if ztop <= 0.0e0: + # error_handling.fdiags[0] = ztop + # error_handling.report_error(116) + + # if rmid <= 0.0e0: + # error_handling.fdiags[0] = rmid + # error_handling.report_error(117) + + # if build_variables.hmax <= 0.0e0: + # error_handling.fdiags[0] = build_variables.hmax + # error_handling.report_error(118) + + # if (fcool < 0.0e0) or (fcool > 1.0e0): + # error_handling.fdiags[0] = fcool + # error_handling.report_error(119) + + # if rtop < rmid: + # error_handling.fdiags[0] = rtop + # error_handling.fdiags[1] = rmid + # error_handling.report_error(120) + + # if build_variables.hmax < ztop: + # error_handling.fdiags[0] = build_variables.hmax + # error_handling.fdiags[1] = ztop + # error_handling.report_error(121) + + # ------------ + + # Mid-plane area calculations + # --------------------------- + # Total number of CP turns + n_turns_tot = n_tf_coils * n_tf_turn + + # Area of the innner TF central hole [m2] + a_tfin_hole = np.pi * r_tfin_inleg**2 + + # Mid-plane outer casing cross-section area [m2] + a_casout = np.pi * ( + (rmid + gr_ins_th + cas_out_th) ** 2 - (rmid + gr_ins_th) ** 2 + ) + + # Mid-plane outter ground insulation thickness [m2] + a_cp_gr_ins = ( + np.pi * ((rmid + gr_ins_th) ** 2 - rmid**2) + + 2.0e0 * gr_ins_th * (rmid - r_tfin_inleg) * n_tf_coils + ) + + # Mid-plane turn layer cross-section area [m2] + a_cp_ins = ( + np.pi + * ((r_tfin_inleg + ins_th) ** 2 - r_tfin_inleg**2) # Inner layer volume + + np.pi * (rmid**2 - (rmid - ins_th) ** 2) # Outter layer volume + + 2.0e0 * n_turns_tot * ins_th * (rmid - r_tfin_inleg - 2.0e0 * ins_th) + ) # inter turn separtion + + # Cooling pipes cross-section per coil [m2] + a_cp_cool = fcool * ( + (np.pi * rmid**2 - a_tfin_hole - a_cp_ins) / n_tf_coils + - 2.0e0 * gr_ins_th * (rmid - r_tfin_inleg) + ) # Wedge ground insulation + # --------------------------- + + # Trivial solutions + # ------------------ + if np.abs(fcool) < EPS: + vol_cond_cp = 0.0e0 + respow = 0.0e0 + vol_case_cp = 0.0e0 + vol_gr_ins_cp = 0.0e0 + vol_ins_cp = 0.0e0 + # error_handling.report_error(122) + return ( + a_cp_cool, + vol_cond_cp, + respow, + vol_ins_cp, + vol_case_cp, + vol_gr_ins_cp, + ) + + if np.abs(rmid - rtop) < EPS: + # Exact conductor cross-section + a_cond_midplane = ( + np.pi * rmid**2 - a_tfin_hole - n_tf_coils * a_cp_cool - a_cp_ins + ) + + # Volumes and resisitive losses calculations + vol_cond_cp = 2.0e0 * hmaxi * a_cond_midplane + vol_ins_cp = 2.0e0 * hmaxi * a_cp_ins + vol_gr_ins_cp = 2.0e0 * hmaxi * a_cp_gr_ins + respow = 2.0e0 * hmaxi * curr**2 * rho / a_cond_midplane + vol_case_cp = 2.0e0 * hmaxi * a_casout + + return ( + a_cp_cool, + vol_cond_cp, + respow, + vol_ins_cp, + vol_case_cp, + vol_gr_ins_cp, + ) + + # ------------------ + + # Find centre of circle (RC,0) defining the taper's arc + # (r1,z1) is midpoint of line joining (rmid,0) and (rtop,ztop) + # Rem : The taper arc is defined using the outer radius of the + # conductor including turn unsulation + # ------------------------------------------------------------- + r1 = 0.5e0 * (rmid + rtop) + z1 = 0.5e0 * ztop + + x = (r1 - rmid) ** 2 + z1**2 + y = ztop**2 / ((rtop - rmid) ** 2 + ztop**2) + + rc = rmid + np.sqrt(x / (1.0e0 - y)) + # ------------------------------------------------------------- + + # Find volume of tapered section of centrepost, and the resistive + # power losses, by integrating along the centrepost from the midplane + # -------------------------------------------------------------------- + # Calculate centrepost radius and cross-sectional areas at each Z + dz = 0.01e0 * ztop + + for ii in range(101): + z = ii * dz + z = np.fmin(np.array(z), ztop) + + r = rc - np.sqrt((rc - rmid) ** 2 - z * z) + + # if r <= 0.0e0: + # error_handling.fdiags[0] = r + # error_handling.fdiags[1] = rc + # error_handling.fdiags[2] = rmid + # error_handling.fdiags[3] = z + + # error_handling.report_error(123) + + # Insulation cross-sectional area at z + yy_ins[ii] = ( + np.pi * ((r_tfin_inleg + ins_th) ** 2 - r_tfin_inleg**2) + + np.pi * (r**2 - (r - ins_th) ** 2) # Inner layer volume + + 2.0e0 # Outter layer volume + * ins_th + * (r - r_tfin_inleg - 2.0e0 * ins_th) + * n_turns_tot + ) # inter turn layers + + # Conductor cross-sectional area at z + yy_cond[ii] = ( + np.pi * r**2 + - a_tfin_hole + - n_tf_coils * a_cp_cool + - yy_ins[ii] + - 2.0e0 * n_tf_coils * gr_ins_th * (r - r_tfin_inleg) + ) # Wedge ground insulation + + # Outer ground insulation area at z + yy_gr_ins[ii] = np.pi * ( + (r + gr_ins_th) ** 2 - r**2 + ) + 2.0e0 * n_tf_coils * gr_ins_th * (r - r_tfin_inleg) + + # Outer casing Cross-sectional area at z + yy_casout[ii] = np.pi * ( + (r + gr_ins_th + cas_out_th) ** 2 - (r + gr_ins_th) ** 2 + ) + + # Perform integrals using trapezium rule + sum1 = 0.0e0 + sum2 = 0.0e0 + sum3 = 0.0e0 + sum4 = 0.0e0 + sum5 = 0.0e0 + for ii in range(1, 100): + sum1 = sum1 + yy_cond[ii] + sum2 = sum2 + 1.0e0 / yy_cond[ii] + sum3 = sum3 + yy_ins[ii] + sum4 = sum4 + yy_casout[ii] + sum5 = sum5 + yy_gr_ins[ii] + + sum1 = 0.5e0 * dz * (yy_cond[0] + yy_cond[100] + 2.0e0 * sum1) + sum2 = 0.5e0 * dz * (1.0e0 / yy_cond[0] + 1.0e0 / yy_cond[100] + 2.0e0 * sum2) + sum3 = 0.5e0 * dz * (yy_ins[0] + yy_ins[100] + 2.0e0 * sum3) + sum4 = 0.5e0 * dz * (yy_casout[0] + yy_casout[100] + 2.0e0 * sum4) + sum5 = 0.5e0 * dz * (yy_gr_ins[0] + yy_gr_ins[100] + 2.0e0 * sum5) + + # Turn insulation layer cross section at CP top [m2] + a_cp_ins = ( + np.pi * ((r_tfin_inleg + ins_th) ** 2 - r_tfin_inleg**2) + + np.pi * (rtop**2 - (rtop - ins_th) ** 2) # Inner layer volume + + 2.0e0 # Outter layer volume + * ins_th + * (rtop - r_tfin_inleg - 2.0e0 * ins_th) + * n_turns_tot + ) # turn separtion layers + + # Ground insulation layer cross-section at CP top [m2] + a_cp_gr_ins = ( + np.pi * ((rtop + gr_ins_th) ** 2 - rtop**2) + + 2.0e0 * gr_ins_th * (rtop - r_tfin_inleg) * n_tf_coils + ) + + # Outer casing cross-section area at CP top [m2] + a_casout = np.pi * ( + (rmid + gr_ins_th + cas_out_th) ** 2 - (rmid + gr_ins_th) ** 2 + ) + + # Centrepost volume (ignoring coolant fraction) [m3] + vol_cond_cp = 2.0e0 * sum1 + 2.0e0 * ( # Tapered section + hmaxi - ztop + ) * ( # Straight section vertical height + np.pi * rtop**2 + - a_tfin_hole + - a_cp_ins + - n_tf_coils * a_cp_cool + - 2.0e0 * n_tf_coils * gr_ins_th * (rtop - r_tfin_inleg) + ) # subtracting ground insulation wedge separation + + # Resistive power losses in taped section (variable radius section) [W] + res_taped = rho * curr**2 * sum2 + + # Centrepost insulator volume [m3] + vol_ins_cp = 2.0e0 * (sum3 + (hmaxi - ztop) * a_cp_ins) + + # Ground insulation volume [m3] + vol_gr_ins_cp = 2.0e0 * ( + sum5 + + (hmaxi - ztop) * a_cp_gr_ins + + hmaxi * np.pi * (r_tfin_inleg**2 - (r_tfin_inleg - gr_ins_th) ** 2) + ) + + # CP casing volume [m3] + vol_case_cp = 2.0e0 * ( + sum4 + + (hmaxi - ztop) * a_casout + + hmaxi + * np.pi + * ( + (r_tfin_inleg - gr_ins_th) ** 2 + - (r_tfin_inleg - gr_ins_th - cas_in_th) ** 2 + ) + ) + + # Resistive power losses in cylindrical section (constant radius) [W] + res_cyl = ( + rho + * curr**2 + * ( + (hmaxi - ztop) + / ( + np.pi * rtop**2 + - a_tfin_hole + - a_cp_ins + - n_tf_coils * a_cp_cool + - 2.0e0 * n_tf_coils * gr_ins_th * (rtop - r_tfin_inleg) + ) + ) + ) # ground insulation separation + + # Total CP resistive power [W] + respow = 2.0e0 * (res_cyl + res_taped) + + return ( + a_cp_cool, + vol_cond_cp, + respow, + vol_ins_cp, + vol_case_cp, + vol_gr_ins_cp, + ) diff --git a/process/sctfcoil.py b/process/sctfcoil.py deleted file mode 100644 index 273fae1944..0000000000 --- a/process/sctfcoil.py +++ /dev/null @@ -1,7450 +0,0 @@ -import copy -import json -import logging - -import numba -import numpy as np -from scipy import optimize - -import process.superconductors as superconductors -from process import process_output as po -from process.fortran import ( - build_variables, - constants, - divertor_variables, - error_handling, - fwbs_variables, - global_variables, - numerics, - pfcoil_variables, - physics_variables, - rebco_variables, - sctfcoil_module, - tfcoil_variables, -) -from process.utilities.f2py_string_patch import f2py_compatible_to_string - -logger = logging.getLogger(__name__) - - -RMU0 = constants.rmu0 -EPS = np.finfo(1.0).eps - - -class Sctfcoil: - def __init__(self): - self.outfile = constants.nout - - def run(self, output: bool): - """ - Routine to call the superconductor module for the TF coils - """ - tfes = sctfcoil_module.estotft / tfcoil_variables.n_tf_coils - # Cross-sectional area per turn - aturn = tfcoil_variables.c_tf_total / ( - tfcoil_variables.jwptf - * tfcoil_variables.n_tf_coils - * tfcoil_variables.n_tf_turn - ) - - if tfcoil_variables.i_tf_sc_mat == 6: - (tfcoil_variables.jwdgcrt, tfcoil_variables.tmargtf) = self.supercon_croco( - aturn, - tfcoil_variables.bmaxtfrp, - tfcoil_variables.cpttf, - tfcoil_variables.tftmp, - output=output, - ) - - tfcoil_variables.vtfskv = ( - self.croco_voltage() / 1.0e3 - ) # TFC Quench voltage in kV - - else: - ( - tfcoil_variables.jwdgcrt, - vdump, - tfcoil_variables.tmargtf, - ) = self.supercon( - tfcoil_variables.acstf, - aturn, - tfcoil_variables.bmaxtfrp, - tfcoil_variables.vftf, - tfcoil_variables.fcutfsu, - tfcoil_variables.cpttf, - tfcoil_variables.jwptf, - tfcoil_variables.i_tf_sc_mat, - tfcoil_variables.fhts, - tfcoil_variables.tdmptf, - tfes, - tfcoil_variables.tftmp, - tfcoil_variables.tmaxpro, - tfcoil_variables.bcritsc, - tfcoil_variables.tcritsc, - output=output, - ) - - tfcoil_variables.vtfskv = vdump / 1.0e3 # TFC Quench voltage in kV - - def croco_voltage(self) -> float: - if f2py_compatible_to_string(tfcoil_variables.quench_model) == "linear": - sctfcoil_module.time2 = tfcoil_variables.tdmptf - croco_voltage = ( - 2.0e0 - / sctfcoil_module.time2 - * (sctfcoil_module.estotft / tfcoil_variables.n_tf_coils) - / tfcoil_variables.cpttf - ) - elif f2py_compatible_to_string(tfcoil_variables.quench_model) == "exponential": - sctfcoil_module.tau2 = tfcoil_variables.tdmptf - croco_voltage = ( - 2.0e0 - / sctfcoil_module.tau2 - * (sctfcoil_module.estotft / tfcoil_variables.n_tf_coils) - / tfcoil_variables.cpttf - ) - else: - return 0.0 - - return croco_voltage - - def supercon_croco(self, aturn, bmax, iop, thelium, output: bool): - """TF superconducting CroCo conductor using REBCO tape - author: M Kovari, CCFE, Culham Science Centre - bmax : input real : Peak field at conductor (T) - iop : input real : Operating current per turn (A) - thelium : input real : He temperature at peak field point (K) - iprint : input integer : Switch for printing (1 = yes, 0 = no) - outfile : input integer : Fortran output unit identifier - jwdgcrt : output real : Critical winding pack current density (A/m2) - tmarg : output real : Temperature margin (K) - """ - - j_crit_sc: float = 0.0 - # Find critical current density in superconducting cable, j_crit_cable - j_crit_sc, _ = superconductors.jcrit_rebco(thelium, bmax) - # tfcoil_variables.acstf : Cable space - inside area (m2) - # Set new rebco_variables.croco_od - # allowing for scaling of rebco_variables.croco_od - rebco_variables.croco_od = ( - tfcoil_variables.t_conductor / 3.0e0 - - tfcoil_variables.thwcndut * (2.0e0 / 3.0e0) - ) - sctfcoil_module.conductor_acs = ( - 9.0e0 / 4.0e0 * np.pi * rebco_variables.croco_od**2 - ) - tfcoil_variables.acstf = sctfcoil_module.conductor_acs - sctfcoil_module.conductor_area = ( - tfcoil_variables.t_conductor**2 - ) # does this not assume it's a sqaure??? - - sctfcoil_module.conductor_jacket_area = ( - sctfcoil_module.conductor_area - sctfcoil_module.conductor_acs - ) - tfcoil_variables.acndttf = sctfcoil_module.conductor_jacket_area - - sctfcoil_module.conductor_jacket_fraction = ( - sctfcoil_module.conductor_jacket_area / sctfcoil_module.conductor_area - ) - ( - sctfcoil_module.croco_strand_area, - sctfcoil_module.croco_strand_critical_current, - sctfcoil_module.conductor_copper_area, - sctfcoil_module.conductor_copper_fraction, - sctfcoil_module.conductor_copper_bar_area, - sctfcoil_module.conductor_hastelloy_area, - sctfcoil_module.conductor_hastelloy_fraction, - sctfcoil_module.conductor_helium_area, - sctfcoil_module.conductor_helium_fraction, - sctfcoil_module.conductor_solder_area, - sctfcoil_module.conductor_solder_fraction, - sctfcoil_module.conductor_rebco_area, - sctfcoil_module.conductor_rebco_fraction, - sctfcoil_module.conductor_critical_current, - ) = superconductors.croco( - j_crit_sc, - sctfcoil_module.conductor_area, - rebco_variables.croco_od, - rebco_variables.croco_thick, - ) - - rebco_variables.coppera_m2 = iop / sctfcoil_module.conductor_copper_area - - icrit = sctfcoil_module.conductor_critical_current - j_crit_cable = ( - sctfcoil_module.croco_strand_critical_current - / sctfcoil_module.croco_strand_area - ) - - # Critical current density in winding pack - # aturn : Area per turn (i.e. entire jacketed conductor with insulation) (m2) - jwdgcrt = icrit / aturn - # Ratio of operating / critical current - iooic = iop / icrit - # Operating current density - jwdgop = iop / aturn - # Actual current density in superconductor, - # which should be equal to jcrit(thelium+tmarg) - - # when we have found the desired value of tmarg - jsc = iooic * j_crit_sc - - # Temperature margin - current_sharing_t = superconductors.current_sharing_rebco(bmax, jsc) - tmarg = current_sharing_t - thelium - tfcoil_variables.temp_margin = ( - tmarg # Only used in the availabilty routine - see comment to Issue #526 - ) - - if output: # Output ---------------------------------- - total = ( - sctfcoil_module.conductor_copper_area - + sctfcoil_module.conductor_hastelloy_area - + sctfcoil_module.conductor_solder_area - + sctfcoil_module.conductor_jacket_area - + sctfcoil_module.conductor_helium_area - + sctfcoil_module.conductor_rebco_area - ) - - if tfcoil_variables.temp_margin <= 0.0e0: - logger.warning( - f"""Negative TFC temperature margin - temp_margin: {tfcoil_variables.temp_margin} - bmax: {bmax}""" - ) - - po.oheadr(self.outfile, "Superconducting TF Coils") - po.ovarin(self.outfile, "Superconductor switch", "(isumat)", 6) - po.ocmmnt( - self.outfile, "Superconductor used: REBCO HTS tape in CroCo strand" - ) - - po.ovarre( - self.outfile, - "Thickness of REBCO layer in tape (m)", - "(rebco_thickness)", - rebco_variables.rebco_thickness, - ) - po.ovarre( - self.outfile, - "Thickness of copper layer in tape (m)", - "(copper_thick )", - rebco_variables.copper_thick, - ) - po.ovarre( - self.outfile, - "Thickness of Hastelloy layer in tape (m) ", - "(hastelloy_thickness)", - rebco_variables.hastelloy_thickness, - ) - - po.ovarre( - self.outfile, - "Mean width of tape (m)", - "(tape_width)", - rebco_variables.tape_width, - "OP ", - ) - po.ovarre( - self.outfile, - "Outer diameter of CroCo copper tube (m) ", - "(croco_od)", - rebco_variables.croco_od, - "OP ", - ) - po.ovarre( - self.outfile, - "Inner diameter of CroCo copper tube (m) ", - "(croco_id)", - rebco_variables.croco_id, - "OP ", - ) - po.ovarre( - self.outfile, - "Thickness of CroCo copper tube (m) ", - "(croco_thick)", - rebco_variables.croco_thick, - ) - - po.ovarre( - self.outfile, - "Thickness of each HTS tape ", - "(tape_thickness)", - rebco_variables.tape_thickness, - "OP ", - ) - po.ovarre( - self.outfile, - "Thickness of stack of rebco_variables.tapes (m) ", - "(stack_thickness)", - rebco_variables.stack_thickness, - "OP ", - ) - po.ovarre( - self.outfile, - "Number of rebco_variables.tapes in strand", - "(tapes)", - rebco_variables.tapes, - "OP ", - ) - po.oblnkl(self.outfile) - po.ovarre( - self.outfile, - "Area of REBCO in strand (m2)", - "(rebco_area)", - rebco_variables.rebco_area, - "OP ", - ) - po.ovarre( - self.outfile, - "Area of copper in strand (m2)", - "(copper_area)", - rebco_variables.copper_area, - "OP ", - ) - po.ovarre( - self.outfile, - "Area of hastelloy substrate in strand (m2) ", - "(hastelloy_area)", - rebco_variables.hastelloy_area, - "OP ", - ) - po.ovarre( - self.outfile, - "Area of solder in strand (m2) ", - "(solder_area)", - rebco_variables.solder_area, - "OP ", - ) - po.ovarre( - self.outfile, - "Total: area of CroCo strand (m2) ", - "(croco_strand_area)", - sctfcoil_module.croco_strand_area, - "OP ", - ) - if ( - abs( - sctfcoil_module.croco_strand_area - - ( - rebco_variables.rebco_area - + rebco_variables.copper_area - + rebco_variables.hastelloy_area - + rebco_variables.solder_area - ) - ) - > 1e-6 - ): - po.ocmmnt(self.outfile, "ERROR: Areas in CroCo strand do not add up") - logger.warning("Areas in CroCo strand do not add up - see OUT.DAT") - - po.oblnkl(self.outfile) - po.ocmmnt(self.outfile, "Cable information") - po.ovarin( - self.outfile, - "Number of CroCo strands in the cable (fixed) ", - "", - 6, - "OP ", - ) - po.ovarre( - self.outfile, - "Total area of cable space (m2)", - "(acstf)", - tfcoil_variables.acstf, - "OP ", - ) - - po.oblnkl(self.outfile) - po.ocmmnt( - self.outfile, - "Conductor information (includes jacket, not including insulation)", - ) - po.ovarre( - self.outfile, - "Width of square conductor (cable + steel jacket) (m)", - "(t_conductor)", - tfcoil_variables.t_conductor, - "OP ", - ) - po.ovarre( - self.outfile, - "Area of conductor (m2)", - "(area)", - sctfcoil_module.conductor_area, - "OP ", - ) - po.ovarre( - self.outfile, - "REBCO area of conductor (mm2)", - "(rebco_area)", - sctfcoil_module.conductor_rebco_area, - "OP ", - ) - po.ovarre( - self.outfile, - "Area of central copper bar (mm2)", - "(copper_bar_area)", - sctfcoil_module.conductor_copper_bar_area, - "OP ", - ) - po.ovarre( - self.outfile, - "Total copper area of conductor, total (mm2)", - "(copper_area)", - sctfcoil_module.conductor_copper_area, - "OP ", - ) - po.ovarre( - self.outfile, - "Hastelloy area of conductor (mm2)", - "(hastelloy_area)", - sctfcoil_module.conductor_hastelloy_area, - "OP ", - ) - po.ovarre( - self.outfile, - "Solder area of conductor (mm2)", - "(solder_area)", - sctfcoil_module.conductor_solder_area, - "OP ", - ) - po.ovarre( - self.outfile, - "Jacket area of conductor (mm2)", - "(jacket_area)", - sctfcoil_module.conductor_jacket_area, - "OP ", - ) - po.ovarre( - self.outfile, - "Helium area of conductor (mm2)", - "(helium_area)", - sctfcoil_module.conductor_helium_area, - "OP ", - ) - if abs(total - sctfcoil_module.conductor_area) > 1e-8: - po.ovarre( - self.outfile, - "ERROR: conductor areas do not add up:", - "(total)", - total, - "OP ", - ) - logger.warning(f"conductor areas do not add up. total: {total}") - - po.ovarre( - self.outfile, - "Critical current of CroCo strand (A)", - "(croco_strand_critical_current)", - sctfcoil_module.croco_strand_critical_current, - "OP ", - ) - po.ovarre( - self.outfile, - "Critical current of conductor (A) ", - "(conductor_critical_current)", - sctfcoil_module.conductor_critical_current, - "OP ", - ) - - if global_variables.run_tests == 1: - po.oblnkl(self.outfile) - po.ocmmnt( - self.outfile, - "PROCESS TF Coil peak field fit. Values for t, z and y:", - ) - po.oblnkl(self.outfile) - po.ovarre( - self.outfile, - "Dimensionless winding pack width", - "(tf_fit_t)", - sctfcoil_module.tf_fit_t, - "OP ", - ) - po.ovarre( - self.outfile, - "Dimensionless winding pack radial thickness", - "(tf_fit_z)", - sctfcoil_module.tf_fit_z, - "OP ", - ) - po.ovarre( - self.outfile, - "Ratio of actual peak field to nominal axisymmetric peak field", - "(tf_fit_y)", - sctfcoil_module.tf_fit_y, - "OP ", - ) - - po.oblnkl(self.outfile) - po.ovarre( - self.outfile, - "Helium temperature at peak field (= superconductor temperature) (K)", - "(thelium)", - thelium, - ) - po.ovarre( - self.outfile, - "Critical current density in superconductor (A/m2)", - "(j_crit_sc)", - j_crit_sc, - "OP ", - ) - po.ovarre( - self.outfile, - "Critical current density in cable (A/m2)", - "(j_crit_cable)", - j_crit_cable, - "OP ", - ) - po.ovarre( - self.outfile, - "Critical current density in winding pack (A/m2)", - "(jwdgcrt)", - jwdgcrt, - "OP ", - ) - po.ovarre( - self.outfile, - "Actual current density in winding pack (A/m2)", - "(jwdgop)", - jwdgop, - "OP ", - ) - - po.ovarre( - self.outfile, - "Minimum allowed temperature margin in superconductor (K)", - "(tmargmin_tf)", - tfcoil_variables.tmargmin_tf, - ) - - po.ovarre( - self.outfile, - "Actual temperature margin in superconductor (K)", - "(tmarg)", - tmarg, - "OP ", - ) - po.ovarre( - self.outfile, - "Current sharing temperature (K)", - "(current_sharing_t)", - current_sharing_t, - "OP ", - ) - po.ovarre(self.outfile, "Critical current (A)", "(icrit)", icrit, "OP ") - po.ovarre( - self.outfile, - "Actual current (A)", - "(cpttf)", - tfcoil_variables.cpttf, - "OP ", - ) - po.ovarre( - self.outfile, - "Actual current / critical current", - "(iooic)", - iooic, - "OP ", - ) - - return jwdgcrt, tmarg - - def supercon( - self, - acs, - aturn, - bmax, - fhe, - fcu, - iop, - jwp, - isumat, - fhts, - tdmptf, - tfes, - thelium, - tmax, - bcritsc, - tcritsc, - output: bool, - ): - """Routine to calculate the TF superconducting conductor properties - author: P J Knight, CCFE, Culham Science Centre - author: J Galambos, ORNL - author: R Kemp, CCFE, Culham Science Centre - author: M Kovari, CCFE, Culham Science Centre - author: J Miller, ORNL - acs : input real : Cable space - inside area (m2) - aturn : input real : Area per turn (i.e. entire jacketed conductor) (m2) - bmax : input real : Peak field at conductor (T) - fhe : input real : Fraction of cable space that is for He cooling - fcu : input real : Fraction of conductor that is copper - iop : input real : Operating current per turn (A) - jwp : input real : Actual winding pack current density (A/m2) - isumat : input integer : Switch for conductor type: - 1 = ITER Nb3Sn, standard parameters, - 2 = Bi-2212 High Temperature Superconductor, - 3 = NbTi, - 4 = ITER Nb3Sn, user-defined parameters - 5 = WST Nb3Sn parameterisation - 7 = Durham Ginzburg-Landau Nb-Ti parameterisation - 8 = Durham Ginzburg-Landau critical surface model for REBCO - 9 = Hazelton experimental data + Zhai conceptual model for REBCO - fhts : input real : Adjustment factor (<= 1) to account for strain, - radiation damage, fatigue or AC losses - tdmptf : input real : Dump time (sec) - tfes : input real : Energy stored in one TF coil (J) - thelium : input real : He temperature at peak field point (K) - tmax : input real : Max conductor temperature during quench (K) - bcritsc : input real : Critical field at zero temperature and strain (T) (isumat=4 only) - tcritsc : input real : Critical temperature at zero field and strain (K) (isumat=4 only) - iprint : input integer : Switch for printing (1 = yes, 0 = no) - outfile : input integer : Fortran output unit identifier - jwdgpro : output real : Winding pack current density from temperature - rise protection (A/m2) - jwdgcrt : output real : Critical winding pack current density (A/m2) - vd : output real : Discharge voltage imposed on a TF coil (V) - tmarg : output real : Temperature margin (K) - This routine calculates the superconductor properties for the TF coils. - It was originally programmed by J. Galambos 1991, from algorithms provided - by J. Miller. -

The routine calculates the critical current density (winding pack) - and also the protection information (for a quench). - NOT used for the Croco conductor - - N.B. critical current density for a super conductor (j_crit_sc) - is for the superconducting strands/tape, not including copper. - Critical current density for a cable (j_crit_cable) acounts for - both the fraction of the cable taken up by helium coolant channels, - and the cable conductor copper fraction - i.e., the copper in the - superconducting strands AND any addtional copper, such as REBCO - tape support. - """ - tdump = tdmptf - - # Helium channel - fhetot = ( - fhe - + (np.pi / 4.0e0) - * tfcoil_variables.dhecoil - * tfcoil_variables.dhecoil - / acs - ) - - # Guard against negative conductor fraction fcond - # Kludge to allow solver to continue and hopefully be constrained away - # from this point - if fhetot > 0.99: - fhetot = 0.99 - - # Conductor fraction (including central helium channel) - fcond = 1.0e0 - fhetot - - if tfcoil_variables.i_str_wp == 0: - strain = tfcoil_variables.str_tf_con_res - else: - strain = tfcoil_variables.str_wp - - # Find critical current density in the superconducter (j_crit_sc) - # and the superconducting cable (j_crit_cable) - if isumat == 1: # ITER Nb3Sn critical surface parameterization - bc20m = 32.97e0 - tc0m = 16.06e0 - # If strain limit achieved, throw a warning and use the lower strain - if abs(strain) > 0.5e-2: - error_handling.fdiags[0] = strain - error_handling.report_error(261) - strain = np.sign(strain) * 0.5e-2 - - # j_crit_sc returned by superconductors.itersc is the critical current density in the - # superconductor - not the whole strand, which contains copper - j_crit_sc, _, _ = superconductors.itersc(thelium, bmax, strain, bc20m, tc0m) - # j_crit_cable = j_crit_sc * non-copper fraction of conductor * conductor fraction of cable - j_crit_cable = j_crit_sc * (1.0e0 - fcu) * fcond - # Critical current in cable - icrit = j_crit_cable * acs - - # Strand critical current calculation for costing in $/kAm - # = Superconducting filaments jc * (1 - strand copper fraction) - tfcoil_variables.j_crit_str_tf = j_crit_sc * (1.0e0 - fcu) - - elif isumat == 2: # Bi-2212 high temperature superconductor parameterization - # Current density in a strand of Bi-2212 conductor - # N.B. jcrit returned by superconductors.bi2212 is the critical current density - # in the strand, not just the superconducting portion. - # The parameterization for j_crit_cable assumes a particular strand - # composition that does not require a user-defined copper fraction, - # so this is irrelevant in this model - jstrand = jwp * aturn / (acs * fcond) - - j_crit_cable, tmarg = superconductors.bi2212(bmax, jstrand, thelium, fhts) - j_crit_sc = j_crit_cable / (1.0e0 - fcu) - # Critical current in cable - icrit = j_crit_cable * acs * fcond - - # Strand critical current calulation for costing in $ / kAm - # Copper in the strand is already accounted for - tfcoil_variables.j_crit_str_tf = j_crit_sc - - elif isumat == 3: # NbTi data - bc20m = 15.0e0 - tc0m = 9.3e0 - c0 = 1.0e10 - j_crit_sc, _ = superconductors.jcrit_nbti(thelium, bmax, c0, bc20m, tc0m) - # j_crit_cable = j_crit_sc * non-copper fraction of conductor * conductor fraction of cable - j_crit_cable = j_crit_sc * (1.0e0 - fcu) * fcond - # Critical current in cable - icrit = j_crit_cable * acs - - # Strand critical current calulation for costing in $ / kAm - # = superconducting filaments jc * (1 -strand copper fraction) - tfcoil_variables.j_crit_str_tf = j_crit_sc * (1.0e0 - fcu) - - elif isumat == 4: # ITER Nb3Sn parameterization, but user-defined parameters - bc20m = bcritsc - tc0m = tcritsc - # If strain limit achieved, throw a warning and use the lower strain - if abs(strain) > 0.5e-2: - error_handling.fdiags[0] = strain - error_handling.report_error(261) - strain = np.sign(strain) * 0.5e-2 - - j_crit_sc, _, _ = superconductors.itersc(thelium, bmax, strain, bc20m, tc0m) - # j_crit_cable = j_crit_sc * non-copper fraction of conductor * conductor fraction of cable - j_crit_cable = j_crit_sc * (1.0e0 - fcu) * fcond - # Critical current in cable - icrit = j_crit_cable * acs - - # Strand critical current calulation for costing in $ / kAm - # = superconducting filaments jc * (1 -strand copper fraction) - tfcoil_variables.j_crit_str_tf = j_crit_sc * (1.0e0 - fcu) - - elif isumat == 5: # WST Nb3Sn parameterisation - bc20m = 32.97e0 - tc0m = 16.06e0 - # If strain limit achieved, throw a warning and use the lower strain - if abs(strain) > 0.5e-2: - error_handling.fdiags[0] = strain - error_handling.report_error(261) - strain = np.sign(strain) * 0.5e-2 - - # j_crit_sc returned by superconductors.itersc is the critical current density in the - # superconductor - not the whole strand, which contains copper - j_crit_sc, _, _ = superconductors.wstsc(thelium, bmax, strain, bc20m, tc0m) - # j_crit_cable = j_crit_sc * non-copper fraction of conductor * conductor fraction of cable - j_crit_cable = j_crit_sc * (1.0e0 - fcu) * fcond - # Critical current in cable - icrit = j_crit_cable * acs - - # Strand critical current calulation for costing in $ / kAm - # = superconducting filaments jc * (1 -strand copper fraction) - tfcoil_variables.j_crit_str_tf = j_crit_sc * (1.0e0 - fcu) - - elif isumat == 6: # "REBCO" 2nd generation HTS superconductor in CrCo strand - raise ValueError( - "sctfcoil.supercon has been called but tfcoil_variables.i_tf_sc_mat=6" - ) - - elif isumat == 7: # Durham Ginzburg-Landau Nb-Ti parameterisation - bc20m = tfcoil_variables.b_crit_upper_nbti - tc0m = tfcoil_variables.t_crit_nbti - j_crit_sc, _, _ = superconductors.gl_nbti( - thelium, bmax, strain, bc20m, tc0m - ) - # j_crit_cable = j_crit_sc * non-copper fraction of conductor * conductor fraction of cable - j_crit_cable = j_crit_sc * (1.0e0 - fcu) * fcond - # Critical current in cable - icrit = j_crit_cable * acs - - # Strand critical current calulation for costing in $ / kAm - # = superconducting filaments jc * (1 -strand copper fraction) - tfcoil_variables.j_crit_str_tf = j_crit_sc * (1.0e0 - fcu) - - elif isumat == 8: # Durham Ginzburg-Landau critical surface model for REBCO - bc20m = 430 - tc0m = 185 - # If strain limit achieved, throw a warning and use the lower strain - if abs(strain) > 0.7e-2: - error_handling.fdiags[0] = strain - error_handling.report_error(261) - strain = np.sign(strain) * 0.7e-2 - - j_crit_sc, _, _ = superconductors.gl_rebco( - thelium, bmax, strain, bc20m, tc0m - ) - # A0 calculated for tape cross section already - # j_crit_cable = j_crit_sc * non-copper fraction of conductor * conductor fraction of cable - j_crit_cable = j_crit_sc * (1.0e0 - fcu) * fcond - # Critical current in cable (copper added at this stage in HTS cables) - icrit = j_crit_cable * acs - - # Strand critical current calulation for costing in $ / kAm - # Already includes buffer and support layers so no need to include fcu here - tfcoil_variables.j_crit_str_tf = j_crit_sc - - elif ( - isumat == 9 - ): # Hazelton experimental data + Zhai conceptual model for REBCO - bc20m = 138 - tc0m = 92 - # If strain limit achieved, throw a warning and use the lower strain - if abs(strain) > 0.7e-2: - error_handling.fdiags[0] = strain - error_handling.report_error(261) - strain = np.sign(strain) * 0.7e-2 - - # 'high current density' as per parameterisation described in Wolf, - # and based on Hazelton experimental data and Zhai conceptual model; - # see subroutine for full references - j_crit_sc, _, _ = superconductors.hijc_rebco( - thelium, bmax, strain, bc20m, tc0m - ) - # j_crit_cable = j_crit_sc * non-copper fraction of conductor * conductor fraction of cable - j_crit_cable = j_crit_sc * (1.0e0 - fcu) * fcond - # Critical current in cable (copper added at this stage in HTS cables) - icrit = j_crit_cable * acs - - # Strand critical current calulation for costing in $ / kAm - # = superconducting filaments jc * (1 -strand copper fraction) - tfcoil_variables.j_crit_str_tf = j_crit_sc * (1.0e0 - fcu) - - else: - error_handling.idiags[0] = isumat - error_handling.report_error(105) - - # Critical current density in winding pack - # aturn : Area per turn (i.e. entire jacketed conductor with insulation) (m2) - jwdgcrt = icrit / aturn - # Ratio of operating / critical current - iooic = iop / icrit - # Operating current density - jwdgop = iop / aturn - # Actual current density in superconductor, which should be equal to jcrit(thelium+tmarg) - # when we have found the desired value of tmarg - jsc = iooic * j_crit_sc - - if iooic <= 0e0: - logger.warning( - f"""Negative Iop/Icrit for TF coil - jsc: {jsc} - iooic: {iooic} - j_crit_sc: {j_crit_sc} - Check conductor dimensions. Cable space area acs likely gone negative. acs: {acs} - This is likely because t_cable_radial or t_cable_toroidal has gone negative: - t_cable_radial: {sctfcoil_module.t_cable_radial} - t_cable_toroidal: {sctfcoil_module.t_cable_toroidal} - """ - ) - - # REBCO measurements from 2 T to 14 T, extrapolating outside this - if (isumat == 8) and (tfcoil_variables.bmaxtfrp >= 14): - error_handling.report_error(266) - - # Temperature margin (already calculated in superconductors.bi2212 for isumat=2) - - if ( - (isumat == 1) - or (isumat == 3) - or (isumat == 4) - or (isumat == 5) - or (isumat == 7) - or (isumat == 8) - or (isumat == 9) - ): # Find temperature at which current density margin = 0 - if isumat == 3: - arguments = (isumat, jsc, bmax, strain, bc20m, tc0m, c0) - else: - arguments = (isumat, jsc, bmax, strain, bc20m, tc0m) - - another_estimate = 2 * thelium - t_zero_margin, root_result = optimize.newton( - superconductors.current_density_margin, - thelium, - fprime=None, - args=arguments, - # args=(isumat, jsc, bmax, strain, bc20m, tc0m,), - tol=1.0e-06, - maxiter=50, - fprime2=None, - x1=another_estimate, - rtol=1.0e-6, - full_output=True, - disp=True, - ) - # print(root_result) # Diagnostic for newton method - tmarg = t_zero_margin - thelium - tfcoil_variables.temp_margin = tmarg - - # Find the current density limited by the protection limit - # (N.B. Unclear of this routine's relevance for Bi-2212 (isumat=2), due - # to presence of fcu argument, which is not used for this model above) - - tfcoil_variables.jwdgpro, vd = self.protect( - iop, tfes, acs, aturn, tdump, fcond, fcu, thelium, tmax - ) - - if output: # Output -------------------------- - if tmarg <= 0.0e0: - logger.warning( - """Negative TFC temperature margin - tmarg: {tmarg} - bmax: {bmax} - jcrit0: {jcrit0} - jsc: {jsc} - """ - ) - - po.oheadr(self.outfile, "Superconducting TF Coils") - po.ovarin(self.outfile, "Superconductor switch", "(isumat)", isumat) - - if isumat == 1: - po.ocmmnt(self.outfile, "Superconductor used: Nb3Sn") - po.ocmmnt(self.outfile, " (ITER Jcrit model, standard parameters)") - po.ovarre( - self.outfile, - "Critical field at zero temperature and strain (T)", - "(bc20m)", - bc20m, - ) - po.ovarre( - self.outfile, - "Critical temperature at zero field and strain (K)", - "(tc0m)", - tc0m, - ) - if isumat == 2: - po.ocmmnt(self.outfile, "Superconductor used: Bi-2212 HTS") - if isumat == 3: - po.ocmmnt(self.outfile, "Superconductor used: NbTi") - po.ovarre( - self.outfile, - "Critical field at zero temperature and strain (T)", - "(bc20m)", - bc20m, - ) - po.ovarre( - self.outfile, - "Critical temperature at zero field and strain (K)", - "(tc0m)", - tc0m, - ) - if isumat == 4: - po.ocmmnt(self.outfile, "Superconductor used: Nb3Sn") - po.ocmmnt(self.outfile, " (ITER Jcrit model, user-defined parameters)") - po.ovarre( - self.outfile, - "Critical field at zero temperature and strain (T)", - "(bc20m)", - bc20m, - ) - po.ovarre( - self.outfile, - "Critical temperature at zero field and strain (K)", - "(tc0m)", - tc0m, - ) - if isumat == 5: - po.ocmmnt(self.outfile, "Superconductor used: Nb3Sn") - po.ocmmnt(self.outfile, " (WST Nb3Sn critical surface model)") - po.ovarre( - self.outfile, - "Critical field at zero temperature and strain (T)", - "(bc20m)", - bc20m, - ) - po.ovarre( - self.outfile, - "Critical temperature at zero field and strain (K)", - "(tc0m)", - tc0m, - ) - if isumat == 7: - po.ocmmnt(self.outfile, "Superconductor used: Nb-Ti") - po.ocmmnt( - self.outfile, " (Durham Ginzburg-Landau critical surface model)" - ) - po.ovarre( - self.outfile, - "Critical field at zero temperature and strain (T)", - "(bc20m)", - bc20m, - ) - po.ovarre( - self.outfile, - "Critical temperature at zero field and strain (K)", - "(tc0m)", - tc0m, - ) - if isumat == 8: - po.ocmmnt(self.outfile, "Superconductor used: REBCO") - po.ocmmnt( - self.outfile, " (Durham Ginzburg-Landau critical surface model)" - ) - po.ovarre( - self.outfile, - "Critical field at zero temperature and strain (T)", - "(bc20m)", - bc20m, - ) - po.ovarre( - self.outfile, - "Critical temperature at zero field and strain (K)", - "(tc0m)", - tc0m, - ) - if isumat == 9: - po.ocmmnt(self.outfile, "Superconductor used: REBCO") - po.ocmmnt( - self.outfile, - " (Hazelton experimental data + Zhai conceptual model)", - ) - po.ovarre( - self.outfile, - "Critical field at zero temperature and strain (T)", - "(bc20m)", - bc20m, - ) - po.ovarre( - self.outfile, - "Critical temperature at zero field and strain (K)", - "(tc0m)", - tc0m, - ) - - if global_variables.run_tests == 1: - po.oblnkl(self.outfile) - po.ocmmnt( - self.outfile, - "PROCESS TF Coil peak field fit. Values for t, z and y:", - ) - po.oblnkl(self.outfile) - po.ovarre( - self.outfile, - "Dimensionless winding pack width", - "(tf_fit_t)", - sctfcoil_module.tf_fit_t, - "OP ", - ) - po.ovarre( - self.outfile, - "Dimensionless winding pack radial thickness", - "(tf_fit_z)", - sctfcoil_module.tf_fit_z, - "OP ", - ) - po.ovarre( - self.outfile, - "Ratio of peak field with ripple to nominal axisymmetric peak field", - "(tf_fit_y)", - sctfcoil_module.tf_fit_y, - "OP ", - ) - - po.oblnkl(self.outfile) - po.ovarre( - self.outfile, - "Helium temperature at peak field (= superconductor temperature) (K)", - "(thelium)", - thelium, - ) - po.ovarre( - self.outfile, - "Total helium fraction inside cable space", - "(fhetot)", - fhetot, - "OP ", - ) - po.ovarre(self.outfile, "Copper fraction of conductor", "(fcutfsu)", fcu) - po.ovarre( - self.outfile, - "Residual manufacturing strain on superconductor", - "(str_tf_con_res)", - tfcoil_variables.str_tf_con_res, - ) - po.ovarre( - self.outfile, - "Self-consistent strain on superconductor", - "(str_wp)", - tfcoil_variables.str_wp, - ) - po.ovarre( - self.outfile, - "Critical current density in superconductor (A/m2)", - "(j_crit_sc)", - j_crit_sc, - "OP ", - ) - po.ovarre( - self.outfile, - "Critical current density in cable (A/m2)", - "(j_crit_cable)", - j_crit_cable, - "OP ", - ) - po.ovarre( - self.outfile, - "Critical current density in winding pack (A/m2)", - "(jwdgcrt)", - jwdgcrt, - "OP ", - ) - po.ovarre( - self.outfile, - "Actual current density in winding pack (A/m2)", - "(jwdgop)", - jwdgop, - "OP ", - ) - - po.ovarre( - self.outfile, - "Minimum allowed temperature margin in superconductor (K)", - "(tmargmin_tf)", - tfcoil_variables.tmargmin_tf, - ) - po.ovarre( - self.outfile, - "Actual temperature margin in superconductor (K)", - "(tmarg)", - tmarg, - "OP ", - ) - po.ovarre(self.outfile, "Critical current (A)", "(icrit)", icrit, "OP ") - po.ovarre( - self.outfile, - "Actual current (A)", - "(cpttf)", - tfcoil_variables.cpttf, - "OP ", - ) - po.ovarre( - self.outfile, - "Actual current / critical current", - "(iooic)", - iooic, - "OP ", - ) - - return jwdgcrt, vd, tmarg - - def sctfcoil(self, output: bool): - """TF coil module - author: P J Knight, CCFE, Culham Science Centre - author: J Galambos, FEDC/ORNL - author: R Kemp, CCFE, Culham Science Centre - author: J Morris, CCFE, Culham Science Centre - author: S Kahn, CCFE, Culham Science Centre - This subroutine calculates various parameters for a TF coil set. - The primary outputs are coil size, shape, weight, stress and and fields. - It is a variant from the original FEDC/Tokamak systems code. - """ - peaktfflag = 0 - - self.tf_global_geometry() - - # Calculation of the TF current from bt - self.tf_current() - - # Conductor section internal geometry - # --- - # Superconducting magnets - if tfcoil_variables.i_tf_sup == 1: - self.sc_tf_internal_geom( - tfcoil_variables.i_tf_wp_geom, - tfcoil_variables.i_tf_case_geom, - tfcoil_variables.i_tf_turns_integer, - ) - - # Resitive magnets - else: - self.res_tf_internal_geom() - - # --- - - # Coil vertical geometry - self.coilshap() - - # TF resistive heating (res TF only) - if tfcoil_variables.i_tf_sup != 1: - self.tf_res_heating() - - # Vertical force - self.tf_field_and_force() - - # TF coil inductance - # --- - if physics_variables.itart == 0 and tfcoil_variables.i_tf_shape == 1: - tfcoil_variables.tfind = self.tfcind( - build_variables.dr_tf_inboard, - tfcoil_variables.xarc, - tfcoil_variables.yarc, - ) - else: - tfcoil_variables.tfind = ( - (build_variables.hmax + build_variables.dr_tf_outboard) - * RMU0 - / constants.pi - * np.log( - build_variables.r_tf_outboard_mid / build_variables.r_tf_inboard_mid - ) - ) - - # Total TF coil stored magnetic energy [J] - sctfcoil_module.estotft = ( - 0.5e0 * tfcoil_variables.tfind * tfcoil_variables.c_tf_total**2 - ) - - # Total TF coil stored magnetic energy [Gigajoule] - tfcoil_variables.estotftgj = 1.0e-9 * sctfcoil_module.estotft - # --- - - # Calculate TF coil areas and masses - self.tf_coil_area_and_masses() - - # Peak field including ripple - # Rem : as resistive magnets are axisymmetric, no inboard ripple is present - if tfcoil_variables.i_tf_sup == 1: - tfcoil_variables.bmaxtfrp, peaktfflag = self.peak_tf_with_ripple( - tfcoil_variables.n_tf_coils, - tfcoil_variables.wwp1, - tfcoil_variables.dr_tf_wp - - 2.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.tfinsgap), - sctfcoil_module.r_wp_centre, - tfcoil_variables.bmaxtf, - ) - else: - tfcoil_variables.bmaxtfrp = tfcoil_variables.bmaxtf - - # Do stress calculations (writes the stress output) - if output: - tfcoil_variables.n_rad_per_layer = 500 - - try: - ( - sig_tf_r_max, - sig_tf_t_max, - sig_tf_z_max, - sig_tf_vmises_max, - s_shear_tf_peak, - deflect, - eyoung_axial, - eyoung_trans, - eyoung_wp_axial, - eyoung_wp_trans, - poisson_wp_trans, - radial_array, - s_shear_cea_tf_cond, - poisson_wp_axial, - sig_tf_r, - sig_tf_smeared_r, - sig_tf_smeared_t, - sig_tf_smeared_z, - sig_tf_t, - s_shear_tf, - sig_tf_vmises, - sig_tf_z, - str_tf_r, - str_tf_t, - str_tf_z, - n_radial_array, - n_tf_bucking, - tfcoil_variables.sig_tf_wp, - sig_tf_case, - sig_tf_cs_bucked, - str_wp, - casestr, - insstrain, - sig_tf_wp_av_z, - ) = self.stresscl( - int(tfcoil_variables.n_tf_stress_layers), - int(tfcoil_variables.n_rad_per_layer), - int(tfcoil_variables.n_tf_wp_layers), - int(tfcoil_variables.i_tf_bucking), - float(build_variables.r_tf_inboard_in), - build_variables.dr_bore, - build_variables.hmax, - pfcoil_variables.f_z_cs_tf_internal, - build_variables.dr_cs, - build_variables.i_tf_inside_cs, - build_variables.dr_tf_inboard, - build_variables.dr_cs_tf_gap, - pfcoil_variables.i_pf_conductor, - pfcoil_variables.j_cs_flat_top_end, - pfcoil_variables.j_cs_pulse_start, - pfcoil_variables.c_pf_coil_turn_peak_input, - pfcoil_variables.n_pf_coils_in_group, - pfcoil_variables.ld_ratio_cst, - pfcoil_variables.r_out_cst, - pfcoil_variables.f_a_cs_steel, - tfcoil_variables.eyoung_steel, - tfcoil_variables.poisson_steel, - tfcoil_variables.eyoung_cond_axial, - tfcoil_variables.poisson_cond_axial, - tfcoil_variables.eyoung_cond_trans, - tfcoil_variables.poisson_cond_trans, - tfcoil_variables.eyoung_ins, - tfcoil_variables.poisson_ins, - tfcoil_variables.thicndut, - tfcoil_variables.eyoung_copper, - tfcoil_variables.poisson_copper, - tfcoil_variables.i_tf_sup, - tfcoil_variables.eyoung_res_tf_buck, - sctfcoil_module.r_wp_inner, - sctfcoil_module.tan_theta_coil, - sctfcoil_module.theta_coil, - sctfcoil_module.r_wp_outer, - sctfcoil_module.a_tf_steel, - sctfcoil_module.a_case_front, - sctfcoil_module.a_case_nose, - tfcoil_variables.tfinsgap, - tfcoil_variables.tinstf, - tfcoil_variables.n_tf_turn, - int(tfcoil_variables.i_tf_turns_integer), - sctfcoil_module.t_cable, - sctfcoil_module.t_cable_radial, - tfcoil_variables.dhecoil, - tfcoil_variables.fcutfsu, - tfcoil_variables.thwcndut, - sctfcoil_module.t_lat_case_av, - sctfcoil_module.t_wp_toroidal_av, - sctfcoil_module.a_tf_ins, - tfcoil_variables.aswp, - tfcoil_variables.acond, - sctfcoil_module.awpc, - tfcoil_variables.eyoung_al, - tfcoil_variables.poisson_al, - tfcoil_variables.fcoolcp, - tfcoil_variables.n_tf_graded_layers, - tfcoil_variables.c_tf_total, - tfcoil_variables.casthi, - tfcoil_variables.i_tf_stress_model, - sctfcoil_module.vforce_inboard_tot, - tfcoil_variables.i_tf_tresca, - tfcoil_variables.acasetf, - tfcoil_variables.vforce, - tfcoil_variables.acndttf, - ) - - tfcoil_variables.sig_tf_case = ( - tfcoil_variables.sig_tf_case - if tfcoil_variables.sig_tf_case is None - else sig_tf_case - ) - - tfcoil_variables.sig_tf_cs_bucked = ( - tfcoil_variables.sig_tf_cs_bucked - if tfcoil_variables.sig_tf_cs_bucked is None - else sig_tf_cs_bucked - ) - - tfcoil_variables.str_wp = ( - tfcoil_variables.str_wp if tfcoil_variables.str_wp is None else str_wp - ) - - tfcoil_variables.casestr = ( - tfcoil_variables.casestr - if tfcoil_variables.casestr is None - else casestr - ) - - tfcoil_variables.insstrain = ( - tfcoil_variables.insstrain - if tfcoil_variables.insstrain is None - else insstrain - ) - - if output: - self.out_stress( - sig_tf_r_max, - sig_tf_t_max, - sig_tf_z_max, - sig_tf_vmises_max, - s_shear_tf_peak, - deflect, - eyoung_axial, - eyoung_trans, - eyoung_wp_axial, - eyoung_wp_trans, - poisson_wp_trans, - radial_array, - s_shear_cea_tf_cond, - poisson_wp_axial, - sig_tf_r, - sig_tf_smeared_r, - sig_tf_smeared_t, - sig_tf_smeared_z, - sig_tf_t, - s_shear_tf, - sig_tf_vmises, - sig_tf_z, - str_tf_r, - str_tf_t, - str_tf_z, - n_radial_array, - n_tf_bucking, - sig_tf_wp_av_z, - ) - except ValueError as e: - if e.args[1] == 245 and e.args[2] == 0: - error_handling.report_error(245) - tfcoil_variables.sig_tf_case = 0.0e0 - tfcoil_variables.sig_tf_wp = 0.0e0 - - if output: - self.outtf(peaktfflag) - - def protect(self, aio, tfes, acs, aturn, tdump, fcond, fcu, tba, tmax): - """Finds the current density limited by the protection limit - author: P J Knight, CCFE, Culham Science Centre - author: J Miller, ORNL - aio : input real : Operating current (A) - tfes : input real : Energy stored in one TF coil (J) - acs : input real : Cable space - inside area (m2) - aturn : input real : Area per turn (i.e. entire cable) (m2) - tdump : input real : Dump time (sec) - fcond : input real : Fraction of cable space containing conductor - fcu : input real : Fraction of conductor that is copper - tba : input real : He temperature at peak field point (K) - tmax : input real : Max conductor temperature during quench (K) - ajwpro : output real : Winding pack current density from temperature - rise protection (A/m2) - vd : output real : Discharge voltage imposed on a TF coil (V) - This routine calculates maximum conductor current density which - limits the peak temperature in the winding to a given limit (tmax). - It also finds the dump voltage. -

These calculations are based on Miller's formulations. - """ - # Integration coefficients p1,p2,p3 - p1 = ( - 0.0e0, - 0.8e0, - 1.75e0, - 2.4e0, - 2.7e0, - 2.95e0, - 3.1e0, - 3.2e0, - 3.3e0, - 3.4e0, - 3.5e0, - ) - p2 = ( - 0.0e0, - 0.05e0, - 0.5e0, - 1.4e0, - 2.6e0, - 3.7e0, - 4.6e0, - 5.3e0, - 5.95e0, - 6.55e0, - 7.1e0, - ) - p3 = ( - 0.0e0, - 0.05e0, - 0.5e0, - 1.4e0, - 2.6e0, - 3.7e0, - 4.6e0, - 5.4e0, - 6.05e0, - 6.8e0, - 7.2e0, - ) - - # Dump voltage - - vd = 2.0e0 * tfes / (tdump * aio) - - # Current density limited by temperature rise during quench - - tav = 1.0e0 + (tmax - tba) / 20.0e0 - n_o = int(tav) - n_p = n_o + 1 - n_p = min(n_p, 11) - - ai1 = 1.0e16 * (p1[n_o - 1] + (p1[n_p - 1] - p1[n_o - 1]) * (tav - n_o)) - ai2 = 1.0e16 * (p2[n_o - 1] + (p2[n_p - 1] - p2[n_o - 1]) * (tav - n_o)) - ai3 = 1.0e16 * (p3[n_o - 1] + (p3[n_p - 1] - p3[n_o - 1]) * (tav - n_o)) - - aa = vd * aio / tfes - bb = (1.0e0 - fcond) * fcond * fcu * ai1 - cc = (fcu * fcond) ** 2 * ai2 - dd = (1.0e0 - fcu) * fcu * fcond**2 * ai3 - ajcp = np.sqrt(aa * (bb + cc + dd)) - ajwpro = ajcp * (acs / aturn) - - return ajwpro, vd - - def tf_global_geometry(self): - """Subroutine for calculating the TF coil geometry - This includes: - - Overall geometry of coil (radii and toroidal planes area) - - Winding Pack NOT included - """ - - sctfcoil_module.theta_coil = np.pi / tfcoil_variables.n_tf_coils - sctfcoil_module.tan_theta_coil = np.tan(sctfcoil_module.theta_coil) - - # TF coil inboard legs mid-plane cross-section area (WP + casing ) [m2] - if tfcoil_variables.i_tf_case_geom == 0: - # Circular front case - tfcoil_variables.tfareain = np.pi * ( - build_variables.r_tf_inboard_out**2 - build_variables.r_tf_inboard_in**2 - ) - else: - # Straight front case - tfcoil_variables.tfareain = ( - tfcoil_variables.n_tf_coils - * np.sin(sctfcoil_module.theta_coil) - * np.cos(sctfcoil_module.theta_coil) - * build_variables.r_tf_inboard_out**2 - - np.pi * build_variables.r_tf_inboard_in**2 - ) - - # Vertical distance from the midplane to the top of the tapered section [m] - if physics_variables.itart == 1: - sctfcoil_module.h_cp_top = ( - build_variables.z_plasma_xpoint_upper + tfcoil_variables.dztop - ) - # --- - - # Outer leg geometry - # --- - # Mid-plane inner/out radial position of the TF coil outer leg [m] - - sctfcoil_module.r_tf_outboard_in = ( - build_variables.r_tf_outboard_mid - build_variables.dr_tf_outboard * 0.5e0 - ) - sctfcoil_module.r_tf_outboard_out = ( - build_variables.r_tf_outboard_mid + build_variables.dr_tf_outboard * 0.5e0 - ) - - # TF coil width in toroidal direction at inboard leg outer edge [m] - # *** - # Sliding joints geometry - if physics_variables.itart == 1 and tfcoil_variables.i_tf_sup != 1: - tfcoil_variables.tftort = ( - 2.0e0 * build_variables.r_cp_top * np.sin(sctfcoil_module.theta_coil) - ) - - # Default thickness, initially written for DEMO SC magnets - elif physics_variables.itart == 1 and tfcoil_variables.i_tf_sup == 1: - tfcoil_variables.tftort = ( - 2.0e0 - * build_variables.r_tf_inboard_out - * np.sin(sctfcoil_module.theta_coil) - ) - else: - tfcoil_variables.tftort = ( - 2.0e0 - * build_variables.r_tf_inboard_out - * np.sin(sctfcoil_module.theta_coil) - ) - - # Area of rectangular cross-section TF outboard leg [m2] - tfcoil_variables.a_tf_leg_outboard = ( - tfcoil_variables.tftort * build_variables.dr_tf_outboard - ) - # --- - - def tf_current(self): - """ - Calculation of the maximum B field and the corresponding TF current - """ - if tfcoil_variables.casthi_is_fraction: - tfcoil_variables.casthi = ( - tfcoil_variables.casthi_fraction * build_variables.dr_tf_inboard - ) - - # Case thickness of side wall [m] - if tfcoil_variables.tfc_sidewall_is_fraction: - tfcoil_variables.casths = ( - tfcoil_variables.casths_fraction - * (build_variables.r_tf_inboard_in + tfcoil_variables.thkcas) - * np.tan(np.pi / tfcoil_variables.n_tf_coils) - ) - - # Radial position of peak toroidal field [m] - if tfcoil_variables.i_tf_sup == 1: - # SC : conservative assumption as the radius is calculated with the - # WP radial distances defined at the TF middle (cos) - tfcoil_variables.rbmax = ( - build_variables.r_tf_inboard_out * np.cos(sctfcoil_module.theta_coil) - - tfcoil_variables.casthi - - tfcoil_variables.tinstf - - tfcoil_variables.tfinsgap - ) - else: - # Resistive coils : No approx necessary as the symmetry is cylindrical - # The turn insulation th (tfcoil_variables.thicndut) is also subtracted too here - tfcoil_variables.rbmax = ( - build_variables.r_tf_inboard_out - - tfcoil_variables.casthi - - tfcoil_variables.thicndut - - tfcoil_variables.tinstf - ) - - # Calculation of the maximum B field on the magnet [T] - tfcoil_variables.bmaxtf = ( - physics_variables.bt * physics_variables.rmajor / tfcoil_variables.rbmax - ) - - # Total current in TF coils [A] - # rem SK : ritcf is no longer an input - tfcoil_variables.c_tf_total = ( - tfcoil_variables.bmaxtf * tfcoil_variables.rbmax * 5.0e6 - ) - - # Current per TF coil [A] - sctfcoil_module.tfc_current = ( - tfcoil_variables.c_tf_total / tfcoil_variables.n_tf_coils - ) - - # Global inboard leg average current in TF coils [A/m2] - tfcoil_variables.oacdcp = ( - tfcoil_variables.c_tf_total / tfcoil_variables.tfareain - ) - - def coilshap(self): - """Calculates the TF coil shape - Calculates the shape of the INSIDE of the TF coil. The coil is - approximated by a straight inboard section and four elliptical arcs - This is a totally ad hoc model, with no physics or engineering basis. - - The referenced equations can be found in draft/unpublished document - attached in GitLab to issue #1328. - """ - FSTRAIGHT = 0.6 - if tfcoil_variables.i_tf_shape == 1 and physics_variables.itart == 0: - # PROCESS D-shape parametrisation - - # X position of the arcs, eq(21) - # The tfcoil_variables.xarc/tfcoil_variables.yarc are defined in the INSIDE part of the TF - tfcoil_variables.xarc[0] = build_variables.r_tf_inboard_out - tfcoil_variables.xarc[1] = ( - physics_variables.rmajor - 0.2e0 * physics_variables.rminor - ) - tfcoil_variables.xarc[2] = sctfcoil_module.r_tf_outboard_in - tfcoil_variables.xarc[3] = tfcoil_variables.xarc[1] - tfcoil_variables.xarc[4] = tfcoil_variables.xarc[0] - - # Height of straight section as a fraction of the coil inner height - if physics_variables.i_single_null == 0: - # Double null - tfcoil_variables.yarc[0] = FSTRAIGHT * build_variables.hmax - tfcoil_variables.yarc[1] = build_variables.hmax - tfcoil_variables.yarc[2] = 0 - tfcoil_variables.yarc[3] = -build_variables.hmax - tfcoil_variables.yarc[4] = -FSTRAIGHT * build_variables.hmax - else: - # Single null - tfcoil_variables.yarc[0] = FSTRAIGHT * ( - build_variables.hpfu - build_variables.dr_tf_inboard - ) - tfcoil_variables.yarc[1] = ( - build_variables.hpfu - build_variables.dr_tf_inboard - ) - tfcoil_variables.yarc[2] = 0 - tfcoil_variables.yarc[3] = -build_variables.hmax - tfcoil_variables.yarc[4] = -FSTRAIGHT * build_variables.hmax - - # Horizontal and vertical radii of inside edge of TF coil - # Arcs are numbered clockwise: - # 1=upper inboard, 2=upper outboard, 3=lower ouboard, 4=lower inboard - # 'len_tf_coil' is the length of the coil midline. - tfcoil_variables.len_tf_coil = ( - tfcoil_variables.yarc[0] - tfcoil_variables.yarc[4] - ) - for ii in range(4): - tfcoil_variables.tfa[ii] = abs( - tfcoil_variables.xarc[ii + 1] - tfcoil_variables.xarc[ii] - ) - tfcoil_variables.tfb[ii] = abs( - tfcoil_variables.yarc[ii + 1] - tfcoil_variables.yarc[ii] - ) - # Radii and length of midline of coil segments - aa = tfcoil_variables.tfa[ii] + 0.5e0 * build_variables.dr_tf_inboard - bb = tfcoil_variables.tfb[ii] + 0.5e0 * build_variables.dr_tf_inboard - tfcoil_variables.len_tf_coil = ( - tfcoil_variables.len_tf_coil + 0.25e0 * self.circumference(aa, bb) - ) - # note: final tfcoil_variables.len_tf_coil includes inboard leg length; eq(22) - - # Centrepost with D-shaped - # --- - elif tfcoil_variables.i_tf_shape == 1 and physics_variables.itart == 1: - # X position of the arcs, eq(23) and text before it - tfcoil_variables.yarc[0] = build_variables.r_cp_top - tfcoil_variables.yarc[1] = ( - physics_variables.rmajor - 0.2e0 * physics_variables.rminor - ) - tfcoil_variables.yarc[2] = sctfcoil_module.r_tf_outboard_in - tfcoil_variables.yarc[3] = tfcoil_variables.xarc[1] - tfcoil_variables.yarc[4] = tfcoil_variables.xarc[0] - - # Double null, eq(23) and text before it - tfcoil_variables.yarc[0] = ( - build_variables.hpfu - build_variables.dr_tf_inboard - ) - tfcoil_variables.yarc[1] = ( - build_variables.hpfu - build_variables.dr_tf_inboard - ) - tfcoil_variables.yarc[2] = 0 - tfcoil_variables.yarc[3] = -build_variables.hmax - tfcoil_variables.yarc[4] = -build_variables.hmax - - # TF middle circumference - tfcoil_variables.len_tf_coil = 2 * ( - tfcoil_variables.xarc[1] - tfcoil_variables.xarc[0] - ) - - for ii in range(1, 3): - tfcoil_variables.tfa[ii] = abs( - tfcoil_variables.xarc[ii + 1] - tfcoil_variables.xarc[ii] - ) - tfcoil_variables.tfb[ii] = abs( - tfcoil_variables.yarc[ii + 1] - tfcoil_variables.yarc[ii] - ) - - # Radii and length of midline of coil segments - aa = tfcoil_variables.tfa[ii] + 0.5e0 * build_variables.dr_tf_outboard - bb = tfcoil_variables.tfb[ii] + 0.5e0 * build_variables.dr_tf_outboard - tfcoil_variables.len_tf_coil = ( - tfcoil_variables.len_tf_coil + 0.25e0 * self.circumference(aa, bb) - ) - # IMPORTANT : THE CENTREPOST LENGTH IS NOT INCLUDED IN len_tf_coil FOR TART; eq(24) - # --- - - # Picture frame coil - # --- - elif tfcoil_variables.i_tf_shape == 2: - # X position of the arcs - if physics_variables.itart == 0: - tfcoil_variables.xarc[0] = build_variables.r_tf_inboard_out - if physics_variables.itart == 1: - tfcoil_variables.xarc[0] = build_variables.r_cp_top - tfcoil_variables.xarc[1] = sctfcoil_module.r_tf_outboard_in - tfcoil_variables.xarc[2] = tfcoil_variables.xarc[1] - tfcoil_variables.xarc[3] = tfcoil_variables.xarc[1] - tfcoil_variables.xarc[4] = tfcoil_variables.xarc[0] - - # Y position of the arcs - tfcoil_variables.yarc[0] = ( - build_variables.hpfu - build_variables.dr_tf_inboard - ) - tfcoil_variables.yarc[1] = ( - build_variables.hpfu - build_variables.dr_tf_inboard - ) - tfcoil_variables.yarc[2] = 0 - tfcoil_variables.yarc[3] = -build_variables.hmax - tfcoil_variables.yarc[4] = -build_variables.hmax - - # TF middle circumference - # IMPORTANT : THE CENTREPOST LENGTH IS NOT INCLUDED IN len_tf_coil FOR TART - if physics_variables.itart == 0: - tfcoil_variables.len_tf_coil = 2.0e0 * ( - 2.0e0 * build_variables.hmax - + build_variables.dr_tf_inboard - + build_variables.r_tf_outboard_mid - - build_variables.r_tf_inboard_mid - ) # eq(25) - elif physics_variables.itart == 1: - tfcoil_variables.len_tf_coil = ( - build_variables.hmax - + build_variables.hpfu - + 2.0e0 - * (build_variables.r_tf_outboard_mid - build_variables.r_cp_top) - ) # eq(26) - - # --- - - @staticmethod - def circumference(aaa, bbb): - """Calculate ellipse arc circumference using Ramanujan approximation (m) - See https://www.johndcook.com/blog/2013/05/05/ramanujan-circumference-ellipse/ - for a discussion of the precision of the formula - - An ellipse has the following formula: (x/a)² + (y/b)² = 1 - - :param aaa: the value of a in the formula of the ellipse. - :type aaa: float - - :param bbb: the value of b in the formula of the ellipse. - :type bbb: float - - :returns: an approximation of the circumference of the ellipse - :rtype: float - """ - hh = (aaa - bbb) ** 2 / (aaa + bbb) ** 2 - return ( - np.pi - * (aaa + bbb) - * (1.0e0 + (3.0e0 * hh) / (10.0e0 + np.sqrt(4.0e0 - 3.0e0 * hh))) - ) - - def tf_res_heating(self) -> None: - """ - Calculate resistive heating for resistive magnets. - - This method calculates the resistive heating for resistive magnets. - It considers the following scenarios: - - Clamped joints in superconductors might have resistive power losses on the joints. - - Sliding joints might have a region of high resistivity. - - Notes: - - The copper resisitivty is set to be that for GLIDCOP AL-15 at 20°C for copper (i_tf_sup = 0). - - The coefficient of resistivity is set to be that of pure copper - - References: - - https://www.spotweldingconsultants.com/GlidCop_AL_15.pdf - - - https://cirris.com/temperature-coefficient-of-copper/ - """ - - # Resistivity of the Glidcop copper centerpost - if tfcoil_variables.i_tf_sup == 0: - tfcoil_variables.rho_cp = ( - # 1.86 is the resitivity at `20°C` for GLIDCOP AL-15 - # 0.00393 is the coefficient of resistivity for copper - tfcoil_variables.frhocp - * (1.86e0 + 0.00393e0 * (tfcoil_variables.temp_cp_average - 293.15e0)) - * 1.0e-8 - ) - - # Resistivity of the aluminium centerpost - if tfcoil_variables.i_tf_sup == 2: - tfcoil_variables.rho_cp = tfcoil_variables.frhocp * ( - 2.00016e-14 * tfcoil_variables.temp_cp_average**3 - - 6.75384e-13 * tfcoil_variables.temp_cp_average**2 - + 8.89159e-12 * tfcoil_variables.temp_cp_average - ) - - # Calculations dedicated for configurations with CP - if physics_variables.itart == 1: - # Tricky trick to make the leg / CP tempearture the same - if ( - abs(tfcoil_variables.temp_tf_legs_outboard + 1.0e0) - < np.finfo(float(tfcoil_variables.temp_tf_legs_outboard)).eps - ): - sctfcoil_module.is_leg_cp_temp_same = 1 - tfcoil_variables.temp_tf_legs_outboard = ( - tfcoil_variables.temp_cp_average - ) - - # Leg resistivity (different leg temperature as separate cooling channels) - if tfcoil_variables.i_tf_sup == 0: - tfcoil_variables.rho_tf_leg = ( - tfcoil_variables.frholeg - * ( - 1.86e0 - + 0.00393e0 - * (tfcoil_variables.temp_tf_legs_outboard - 293.15e0) - ) - * 1.0e-8 - ) - elif tfcoil_variables.i_tf_sup == 2: - tfcoil_variables.rho_tf_leg = tfcoil_variables.frholeg * ( - 2.00016e-14 * tfcoil_variables.temp_tf_legs_outboard**3 - - 6.75384e-13 * tfcoil_variables.temp_tf_legs_outboard**2 - + 8.89159e-12 * tfcoil_variables.temp_tf_legs_outboard - ) - - # Tricky trick to make the leg / CP tempearture the same - if sctfcoil_module.is_leg_cp_temp_same == 1: - tfcoil_variables.temp_tf_legs_outboard = -1.0e0 - - # Centrepost resisitivity and conductor/insulation volume - - ( - tfcoil_variables.a_cp_cool, - tfcoil_variables.vol_cond_cp, - tfcoil_variables.p_cp_resistive, - sctfcoil_module.vol_ins_cp, - sctfcoil_module.vol_case_cp, - sctfcoil_module.vol_gr_ins_cp, - ) = self.cpost( - build_variables.r_tf_inboard_in, - build_variables.r_tf_inboard_out, - build_variables.r_cp_top, - sctfcoil_module.h_cp_top, - build_variables.hmax + build_variables.dr_tf_outboard, - tfcoil_variables.thkcas, - tfcoil_variables.casthi, - tfcoil_variables.tinstf, - tfcoil_variables.thicndut, - tfcoil_variables.n_tf_turn, - tfcoil_variables.c_tf_total, - tfcoil_variables.rho_cp, - tfcoil_variables.fcoolcp, - tfcoil_variables.n_tf_coils, - ) - - # Leg cross-section areas - # Rem : For physics_variables.itart = 1, these quantitire corresponds to the outer leg only - # --- - # Leg ground insulation area per coil [m2] - sctfcoil_module.a_leg_gr_ins = tfcoil_variables.a_tf_leg_outboard - ( - tfcoil_variables.tftort - 2.0e0 * tfcoil_variables.tinstf - ) * (build_variables.dr_tf_outboard - 2.0e0 * tfcoil_variables.tinstf) - - # Outboard leg turns insulation area per coil [m2] - sctfcoil_module.a_leg_ins = 2.0e0 * tfcoil_variables.thicndut * ( - tfcoil_variables.tftort - 2.0e0 * tfcoil_variables.tinstf - ) + 2.0e0 * tfcoil_variables.thicndut * tfcoil_variables.n_tf_turn * ( - build_variables.dr_tf_outboard - - 2.0e0 * (tfcoil_variables.thicndut + tfcoil_variables.tinstf) - ) # toroidal direction + radial direction - - # Exact TF outboard leg conductor area per coil [m2] - sctfcoil_module.a_leg_cond = (1.0e0 - tfcoil_variables.f_a_tf_cool_outboard) * ( - tfcoil_variables.a_tf_leg_outboard - - sctfcoil_module.a_leg_gr_ins - - sctfcoil_module.a_leg_ins - ) - # --- - - if physics_variables.itart == 1: - # Outer leg resistive power loss - # --- - # TF outboard leg's resistance calculation (per leg) [ohm] - tfcoil_variables.res_tf_leg = ( - tfcoil_variables.rho_tf_leg - * tfcoil_variables.len_tf_coil - / sctfcoil_module.a_leg_cond - ) - - # TF outer leg resistive power (TOTAL) [W] - tfcoil_variables.p_tf_leg_resistive = ( - tfcoil_variables.res_tf_leg - * (tfcoil_variables.c_tf_total / tfcoil_variables.n_tf_coils) ** 2 - ) * tfcoil_variables.n_tf_coils - # --- - - # Sliding joints resistive heating - # --- - if tfcoil_variables.i_cp_joints != 0: - # Number of contact area per joint (all legs) - n_contact_tot = ( - tfcoil_variables.n_tf_joints_contact - * np.round(tfcoil_variables.n_tf_turn) - * np.round(tfcoil_variables.n_tf_coils) - ) - - # Area of joint contact (all legs) - a_joints = ( - build_variables.dr_tf_outboard - * tfcoil_variables.th_joint_contact - * n_contact_tot - ) - - # Total joints resistive power losses - tfcoil_variables.pres_joints = ( - tfcoil_variables.n_tf_joints - * tfcoil_variables.rho_tf_joints - * tfcoil_variables.c_tf_total**2 - / a_joints - ) - else: - # Joints resistance to be evaluated for SC - tfcoil_variables.pres_joints = 0.0e0 - - # --- - - # Case of a resistive magnet without joints - # *** - else: - # TF resistive powers - tfcoil_variables.p_cp_resistive = ( - tfcoil_variables.rho_cp - * tfcoil_variables.c_tf_total**2 - * tfcoil_variables.len_tf_coil - / (sctfcoil_module.a_leg_cond * tfcoil_variables.n_tf_coils) - ) - - # tfcoil_variables.p_cp_resistive containts the the total resistive power losses - tfcoil_variables.p_tf_leg_resistive = 0.0e0 - - # No joints if physics_variables.itart = 0 - tfcoil_variables.pres_joints = 0.0e0 - - @staticmethod - @numba.njit(cache=True) - def cpost( - r_tf_inboard_in, - r_tf_inboard_out, - r_cp_top, - ztop, - hmaxi, - cas_in_th, - cas_out_th, - gr_ins_th, - ins_th, - n_tf_turn, - curr, - rho, - fcool, - n_tf_coils, - ): - """ - author: P J Knight, CCFE, Culham Science Centre - Calculates the volume and resistive power losses of a TART centrepost - This routine calculates the volume and resistive power losses - of a TART centrepost. It is assumed to be tapered - narrowest at - the midplane and reaching maximum thickness at the height of the - plasma. Above/below the plasma, the centrepost is cylindrical. - The shape of the taper is assumed to be an arc of a circle. - P J Knight, CCFE, Culham Science Centre - 21/10/96 PJK Initial version - 08/05/12 PJK Initial F90 version - 16/10/12 PJK Added constants; removed argument pi - 26/06/14 PJK Added error handling - 12/11/19 SK Using fixed cooling cross-section area along the CP - 26/11/19 SK added the coolant area, the conuctor/isulator/outer casing volume - 30/11/20 SK added the ground outer ground insulation volume - F/MI/PJK/LOGBOOK12, pp.33,34 - """ - yy_ins = np.zeros((101,)) # Exact conductor area (to be integrated) - yy_cond = np.zeros((101,)) # Turn insulation area (to be integrated) - yy_gr_ins = np.zeros((101,)) # Outter ground insulation area (to be integrated) - yy_casout = np.zeros((101,)) # Outter case area (to be integrated) - - rtop = r_cp_top - cas_out_th - gr_ins_th - - # Conductor outer radius at CP mid-plane [m] - rmid = r_tf_inboard_out - cas_out_th - gr_ins_th - - # Conductor inner radius [m] - r_tfin_inleg = r_tf_inboard_in + cas_in_th + gr_ins_th - # -# - - # Error traps - # ------------ - # if rtop <= 0.0e0: - # error_handling.fdiags[0] = rtop - # error_handling.report_error(115) - - # if ztop <= 0.0e0: - # error_handling.fdiags[0] = ztop - # error_handling.report_error(116) - - # if rmid <= 0.0e0: - # error_handling.fdiags[0] = rmid - # error_handling.report_error(117) - - # if build_variables.hmax <= 0.0e0: - # error_handling.fdiags[0] = build_variables.hmax - # error_handling.report_error(118) - - # if (fcool < 0.0e0) or (fcool > 1.0e0): - # error_handling.fdiags[0] = fcool - # error_handling.report_error(119) - - # if rtop < rmid: - # error_handling.fdiags[0] = rtop - # error_handling.fdiags[1] = rmid - # error_handling.report_error(120) - - # if build_variables.hmax < ztop: - # error_handling.fdiags[0] = build_variables.hmax - # error_handling.fdiags[1] = ztop - # error_handling.report_error(121) - - # ------------ - - # Mid-plane area calculations - # --------------------------- - # Total number of CP turns - n_turns_tot = n_tf_coils * n_tf_turn - - # Area of the innner TF central hole [m2] - a_tfin_hole = np.pi * r_tfin_inleg**2 - - # Mid-plane outer casing cross-section area [m2] - a_casout = np.pi * ( - (rmid + gr_ins_th + cas_out_th) ** 2 - (rmid + gr_ins_th) ** 2 - ) - - # Mid-plane outter ground insulation thickness [m2] - a_cp_gr_ins = ( - np.pi * ((rmid + gr_ins_th) ** 2 - rmid**2) - + 2.0e0 * gr_ins_th * (rmid - r_tfin_inleg) * n_tf_coils - ) - - # Mid-plane turn layer cross-section area [m2] - a_cp_ins = ( - np.pi - * ((r_tfin_inleg + ins_th) ** 2 - r_tfin_inleg**2) # Inner layer volume - + np.pi * (rmid**2 - (rmid - ins_th) ** 2) # Outter layer volume - + 2.0e0 * n_turns_tot * ins_th * (rmid - r_tfin_inleg - 2.0e0 * ins_th) - ) # inter turn separtion - - # Cooling pipes cross-section per coil [m2] - a_cp_cool = fcool * ( - (np.pi * rmid**2 - a_tfin_hole - a_cp_ins) / n_tf_coils - - 2.0e0 * gr_ins_th * (rmid - r_tfin_inleg) - ) # Wedge ground insulation - # --------------------------- - - # Trivial solutions - # ------------------ - if np.abs(fcool) < EPS: - vol_cond_cp = 0.0e0 - respow = 0.0e0 - vol_case_cp = 0.0e0 - vol_gr_ins_cp = 0.0e0 - vol_ins_cp = 0.0e0 - # error_handling.report_error(122) - return ( - a_cp_cool, - vol_cond_cp, - respow, - vol_ins_cp, - vol_case_cp, - vol_gr_ins_cp, - ) - - if np.abs(rmid - rtop) < EPS: - # Exact conductor cross-section - a_cond_midplane = ( - np.pi * rmid**2 - a_tfin_hole - n_tf_coils * a_cp_cool - a_cp_ins - ) - - # Volumes and resisitive losses calculations - vol_cond_cp = 2.0e0 * hmaxi * a_cond_midplane - vol_ins_cp = 2.0e0 * hmaxi * a_cp_ins - vol_gr_ins_cp = 2.0e0 * hmaxi * a_cp_gr_ins - respow = 2.0e0 * hmaxi * curr**2 * rho / a_cond_midplane - vol_case_cp = 2.0e0 * hmaxi * a_casout - - return ( - a_cp_cool, - vol_cond_cp, - respow, - vol_ins_cp, - vol_case_cp, - vol_gr_ins_cp, - ) - - # ------------------ - - # Find centre of circle (RC,0) defining the taper's arc - # (r1,z1) is midpoint of line joining (rmid,0) and (rtop,ztop) - # Rem : The taper arc is defined using the outer radius of the - # conductor including turn unsulation - # ------------------------------------------------------------- - r1 = 0.5e0 * (rmid + rtop) - z1 = 0.5e0 * ztop - - x = (r1 - rmid) ** 2 + z1**2 - y = ztop**2 / ((rtop - rmid) ** 2 + ztop**2) - - rc = rmid + np.sqrt(x / (1.0e0 - y)) - # ------------------------------------------------------------- - - # Find volume of tapered section of centrepost, and the resistive - # power losses, by integrating along the centrepost from the midplane - # -------------------------------------------------------------------- - # Calculate centrepost radius and cross-sectional areas at each Z - dz = 0.01e0 * ztop - - for ii in range(101): - z = ii * dz - z = np.fmin(np.array(z), ztop) - - r = rc - np.sqrt((rc - rmid) ** 2 - z * z) - - # if r <= 0.0e0: - # error_handling.fdiags[0] = r - # error_handling.fdiags[1] = rc - # error_handling.fdiags[2] = rmid - # error_handling.fdiags[3] = z - - # error_handling.report_error(123) - - # Insulation cross-sectional area at z - yy_ins[ii] = ( - np.pi * ((r_tfin_inleg + ins_th) ** 2 - r_tfin_inleg**2) - + np.pi * (r**2 - (r - ins_th) ** 2) # Inner layer volume - + 2.0e0 # Outter layer volume - * ins_th - * (r - r_tfin_inleg - 2.0e0 * ins_th) - * n_turns_tot - ) # inter turn layers - - # Conductor cross-sectional area at z - yy_cond[ii] = ( - np.pi * r**2 - - a_tfin_hole - - n_tf_coils * a_cp_cool - - yy_ins[ii] - - 2.0e0 * n_tf_coils * gr_ins_th * (r - r_tfin_inleg) - ) # Wedge ground insulation - - # Outer ground insulation area at z - yy_gr_ins[ii] = np.pi * ( - (r + gr_ins_th) ** 2 - r**2 - ) + 2.0e0 * n_tf_coils * gr_ins_th * (r - r_tfin_inleg) - - # Outer casing Cross-sectional area at z - yy_casout[ii] = np.pi * ( - (r + gr_ins_th + cas_out_th) ** 2 - (r + gr_ins_th) ** 2 - ) - - # Perform integrals using trapezium rule - sum1 = 0.0e0 - sum2 = 0.0e0 - sum3 = 0.0e0 - sum4 = 0.0e0 - sum5 = 0.0e0 - for ii in range(1, 100): - sum1 = sum1 + yy_cond[ii] - sum2 = sum2 + 1.0e0 / yy_cond[ii] - sum3 = sum3 + yy_ins[ii] - sum4 = sum4 + yy_casout[ii] - sum5 = sum5 + yy_gr_ins[ii] - - sum1 = 0.5e0 * dz * (yy_cond[0] + yy_cond[100] + 2.0e0 * sum1) - sum2 = 0.5e0 * dz * (1.0e0 / yy_cond[0] + 1.0e0 / yy_cond[100] + 2.0e0 * sum2) - sum3 = 0.5e0 * dz * (yy_ins[0] + yy_ins[100] + 2.0e0 * sum3) - sum4 = 0.5e0 * dz * (yy_casout[0] + yy_casout[100] + 2.0e0 * sum4) - sum5 = 0.5e0 * dz * (yy_gr_ins[0] + yy_gr_ins[100] + 2.0e0 * sum5) - - # Turn insulation layer cross section at CP top [m2] - a_cp_ins = ( - np.pi * ((r_tfin_inleg + ins_th) ** 2 - r_tfin_inleg**2) - + np.pi * (rtop**2 - (rtop - ins_th) ** 2) # Inner layer volume - + 2.0e0 # Outter layer volume - * ins_th - * (rtop - r_tfin_inleg - 2.0e0 * ins_th) - * n_turns_tot - ) # turn separtion layers - - # Ground insulation layer cross-section at CP top [m2] - a_cp_gr_ins = ( - np.pi * ((rtop + gr_ins_th) ** 2 - rtop**2) - + 2.0e0 * gr_ins_th * (rtop - r_tfin_inleg) * n_tf_coils - ) - - # Outer casing cross-section area at CP top [m2] - a_casout = np.pi * ( - (rmid + gr_ins_th + cas_out_th) ** 2 - (rmid + gr_ins_th) ** 2 - ) - - # Centrepost volume (ignoring coolant fraction) [m3] - vol_cond_cp = 2.0e0 * sum1 + 2.0e0 * ( # Tapered section - hmaxi - ztop - ) * ( # Straight section vertical height - np.pi * rtop**2 - - a_tfin_hole - - a_cp_ins - - n_tf_coils * a_cp_cool - - 2.0e0 * n_tf_coils * gr_ins_th * (rtop - r_tfin_inleg) - ) # subtracting ground insulation wedge separation - - # Resistive power losses in taped section (variable radius section) [W] - res_taped = rho * curr**2 * sum2 - - # Centrepost insulator volume [m3] - vol_ins_cp = 2.0e0 * (sum3 + (hmaxi - ztop) * a_cp_ins) - - # Ground insulation volume [m3] - vol_gr_ins_cp = 2.0e0 * ( - sum5 - + (hmaxi - ztop) * a_cp_gr_ins - + hmaxi * np.pi * (r_tfin_inleg**2 - (r_tfin_inleg - gr_ins_th) ** 2) - ) - - # CP casing volume [m3] - vol_case_cp = 2.0e0 * ( - sum4 - + (hmaxi - ztop) * a_casout - + hmaxi - * np.pi - * ( - (r_tfin_inleg - gr_ins_th) ** 2 - - (r_tfin_inleg - gr_ins_th - cas_in_th) ** 2 - ) - ) - - # Resistive power losses in cylindrical section (constant radius) [W] - res_cyl = ( - rho - * curr**2 - * ( - (hmaxi - ztop) - / ( - np.pi * rtop**2 - - a_tfin_hole - - a_cp_ins - - n_tf_coils * a_cp_cool - - 2.0e0 * n_tf_coils * gr_ins_th * (rtop - r_tfin_inleg) - ) - ) - ) # ground insulation separation - - # Total CP resistive power [W] - respow = 2.0e0 * (res_cyl + res_taped) - - return ( - a_cp_cool, - vol_cond_cp, - respow, - vol_ins_cp, - vol_case_cp, - vol_gr_ins_cp, - ) - - def vv_stress_on_quench(self): - """Calculate the Tresca stress [Pa] of the Vacuum Vessel (VV) - experienced when the TF coil quenches. - - Author: Timothy Nunn, UKAEA - - Assumes the current center line (CCL) of the TF coil is the - middle of the coil. - - We assume vertical symmetry which is only true for double null - machines. - """ - H_coil = build_variables.hmax + (build_variables.dr_tf_inboard / 2) - ri_coil = build_variables.r_tf_inboard_mid - ro_coil = build_variables.r_tf_outboard_mid - # NOTE: rm is measured from the outside edge of the coil because thats where - # the radius of the first ellipse is measured from - rm_coil = build_variables.r_tf_inboard_out + tfcoil_variables.tfa[0] - - H_vv = ( - build_variables.z_plasma_xpoint_upper - + build_variables.dz_xpoint_divertor - + divertor_variables.dz_divertor - + build_variables.dz_shld_upper - + (build_variables.dz_vv_upper / 2) - ) - # ri and ro for VV dont consider the shield widths - # because it is assumed the shield is on the plasma side - # of the VV - ri_vv = build_variables.r_vv_inboard_out - (build_variables.dr_vv_outboard / 2) - ro_vv = ( - build_variables.r_tf_outboard_mid - - (build_variables.dr_tf_outboard / 2) - - build_variables.dr_tf_shld_gap - - build_variables.dr_shld_thermal_outboard - - build_variables.dr_shld_vv_gap_outboard - - (build_variables.dr_vv_outboard / 2) - ) - - # Assume the radius of the first ellipse of the VV is in the same proportion to - # that of the plasma facing radii of the two structures - tf_vv_frac = build_variables.r_tf_inboard_out / build_variables.r_vv_inboard_out - rm_vv = build_variables.r_vv_inboard_out + ( - tfcoil_variables.tfa[0] * tf_vv_frac - ) - - sctfcoil_module.vv_stress_quench = vv_stress_on_quench( - # TF shape - H_coil=H_coil, - ri_coil=ri_coil, - ro_coil=ro_coil, - rm_coil=rm_coil, - ccl_length_coil=tfcoil_variables.len_tf_coil, - theta1_coil=tfcoil_variables.theta1_coil, - # VV shape - H_vv=H_vv, - ri_vv=ri_vv, - ro_vv=ro_vv, - rm_vv=rm_vv, - theta1_vv=tfcoil_variables.theta1_vv, - # TF properties - n_tf_coils=tfcoil_variables.n_tf_coils, - n_tf_turn=tfcoil_variables.n_tf_turn, - # Area of the radial plate taken to be the area of steel in the WP - # TODO: value clipped due to #1883 - s_rp=np.clip(sctfcoil_module.a_tf_steel, 0, None), - s_cc=sctfcoil_module.a_case_front - + sctfcoil_module.a_case_nose - + 2.0 * sctfcoil_module.t_lat_case_av, - taud=tfcoil_variables.tdmptf, - # TODO: is this the correct current? - i_op=sctfcoil_module.tfc_current / tfcoil_variables.n_tf_turn, - # VV properties - d_vv=build_variables.dr_vv_shells, - ) - - def tf_field_and_force(self): - """ - Calculate the TF coil field, force and VV quench consideration, and the resistive magnets resistance/volume - """ - if tfcoil_variables.i_tf_sup == 1: - self.vv_stress_on_quench() - - # Outer/inner WP radius removing the ground insulation layer and the insertion gap [m] - if tfcoil_variables.i_tf_sup == 1: - r_out_wp = ( - sctfcoil_module.r_wp_outer - - tfcoil_variables.tinstf - - tfcoil_variables.tfinsgap - ) - r_in_wp = ( - sctfcoil_module.r_wp_inner - + tfcoil_variables.tinstf - + tfcoil_variables.tfinsgap - ) - else: - r_out_wp = sctfcoil_module.r_wp_outer - tfcoil_variables.tinstf - r_in_wp = sctfcoil_module.r_wp_inner + tfcoil_variables.tinstf - - # Associated WP thickness - dr_wp = r_out_wp - r_in_wp - - # In plane forces - # --- - # Centering force = net inwards radial force per meters per TF coil [N/m] - tfcoil_variables.cforce = ( - 0.5e0 - * tfcoil_variables.bmaxtf - * tfcoil_variables.c_tf_total - / tfcoil_variables.n_tf_coils - ) - - # Vertical force per coil [N] - # *** - # Rem : this force does not depends on the TF shape or the presence of - # sliding joints, the in/outboard vertical tension repartition is - # -# - # Ouboard leg WP plasma side radius without ground insulation/insertion gat [m] - if tfcoil_variables.i_tf_sup == 1: - r_in_outwp = ( - sctfcoil_module.r_tf_outboard_in - + tfcoil_variables.casthi - + tfcoil_variables.tinstf - + tfcoil_variables.tfinsgap - ) - else: - r_in_outwp = sctfcoil_module.r_tf_outboard_in + tfcoil_variables.tinstf - - # If the TF coil has no dr_bore it would induce division by 0. - # In this situation, the dr_bore radius is set to a very small value : 1.0e-9 m - if abs(r_in_wp) < np.finfo(float(r_in_wp)).eps: - r_in_wp = 1.0e-9 - - # May the force be with you - vforce_tot = ( - 0.5e0 - * ( - physics_variables.bt - * physics_variables.rmajor - * tfcoil_variables.c_tf_total - ) - / (tfcoil_variables.n_tf_coils * dr_wp**2) - * ( - r_out_wp**2 * np.log(r_out_wp / r_in_wp) - + r_in_outwp**2 * np.log((r_in_outwp + dr_wp) / r_in_outwp) - + dr_wp**2 * np.log((r_in_outwp + dr_wp) / r_in_wp) - - dr_wp * (r_out_wp + r_in_outwp) - + 2.0e0 - * dr_wp - * ( - r_out_wp * np.log(r_in_wp / r_out_wp) - + r_in_outwp * np.log((r_in_outwp + dr_wp) / r_in_outwp) - ) - ) - ) - - # Case of a centrepost (physics_variables.itart == 1) with sliding joints (the CP vertical are separated from the leg ones) - # Rem SK : casing/insulation thickness not subtracted as part of the CP is genuinely connected to the legs.. - if physics_variables.itart == 1 and tfcoil_variables.i_cp_joints == 1: - # CP vertical tension [N] - tfcoil_variables.vforce = ( - 0.25e0 - * ( - physics_variables.bt - * physics_variables.rmajor - * tfcoil_variables.c_tf_total - ) - / (tfcoil_variables.n_tf_coils * dr_wp**2) - * ( - 2.0e0 * r_out_wp**2 * np.log(r_out_wp / r_in_wp) - + 2.0e0 * dr_wp**2 * np.log(build_variables.r_cp_top / r_in_wp) - + 3.0e0 * dr_wp**2 - - 2.0e0 * dr_wp * r_out_wp - + 4.0e0 * dr_wp * r_out_wp * np.log(r_in_wp / r_out_wp) - ) - ) - - # Vertical tension applied on the outer leg [N] - tfcoil_variables.vforce_outboard = vforce_tot - tfcoil_variables.vforce - - # Inboard vertical tension fraction - tfcoil_variables.f_vforce_inboard = tfcoil_variables.vforce / vforce_tot - - # Case of TF without joints or with clamped joints vertical tension - else: - # Inboard vertical tension [N] - tfcoil_variables.vforce = tfcoil_variables.f_vforce_inboard * vforce_tot - - # Ouboard vertical tension [N] - tfcoil_variables.vforce_outboard = tfcoil_variables.vforce * ( - (1.0e0 / tfcoil_variables.f_vforce_inboard) - 1.0e0 - ) - - # *** - - # Total vertical force - sctfcoil_module.vforce_inboard_tot = ( - tfcoil_variables.vforce * tfcoil_variables.n_tf_coils - ) - - @staticmethod - @numba.njit(cache=True) - def tfcind(tfthk, xarc, yarc): - """Calculates the self inductance of a TF coil - This routine calculates the self inductance of a TF coil - approximated by a straight inboard section and two elliptical arcs. - The inductance of the TFC (considered as a single axisymmetric turn) - is calculated by numerical integration over the cross-sectional area. - The contribution from the cross-sectional area of the - coil itself is calculated by taking the field as B(r)/2. - The field in the dr_bore is calculated for unit current. - Top/bottom symmetry is assumed. - - :param tfthk: TF coil thickness (m) - :type tfthk: float - """ - NINTERVALS = 100 - - # Integrate over the whole TF area, including the coil thickness. - x0 = xarc[1] - y0 = yarc[1] - - # Minor and major radii of the inside and outside perimeters of the the - # Inboard leg and arc. - # Average the upper and lower halves, which are different in the - # single null case - ai = xarc[1] - xarc[0] - bi = (yarc[1] - yarc[3]) / 2.0e0 - yarc[0] - ao = ai + tfthk - bo = bi + tfthk - # Interval used for integration - dr = ao / NINTERVALS - # Start both integrals from the centre-point where the arcs join. - # Initialise major radius - r = x0 - dr / 2.0e0 - - tfind = 0 - - for _ in range(NINTERVALS): - # Field in the dr_bore for unit current - b = RMU0 / (2.0e0 * np.pi * r) - # Find out if there is a dr_bore - if x0 - r < ai: - h_bore = y0 + bi * np.sqrt(1 - ((r - x0) / ai) ** 2) - h_thick = bo * np.sqrt(1 - ((r - x0) / ao) ** 2) - h_bore - else: - h_bore = 0.0e0 - # Include the contribution from the straight section - h_thick = bo * np.sqrt(1 - ((r - x0) / ao) ** 2) + yarc[0] - - # Assume B in TF coil = 1/2 B in dr_bore - # Multiply by 2 for upper and lower halves of coil - tfind += b * dr * (2.0e0 * h_bore + h_thick) - r = r - dr - - # Outboard arc - ai = xarc[2] - xarc[1] - bi = (yarc[1] - yarc[3]) / 2.0e0 - ao = ai + tfthk - bo = bi + tfthk - dr = ao / NINTERVALS - # Initialise major radius - r = x0 + dr / 2.0e0 - - for _ in range(NINTERVALS): - # Field in the dr_bore for unit current - b = RMU0 / (2.0e0 * np.pi * r) - # Find out if there is a dr_bore - if r - x0 < ai: - h_bore = y0 + bi * np.sqrt(1 - ((r - x0) / ai) ** 2) - h_thick = bo * np.sqrt(1 - ((r - x0) / ao) ** 2) - h_bore - else: - h_bore = 0.0e0 - h_thick = bo * np.sqrt(1 - ((r - x0) / ao) ** 2) - - # Assume B in TF coil = 1/2 B in dr_bore - # Multiply by 2 for upper and lower halves of coil - tfind += b * dr * (2.0e0 * h_bore + h_thick) - r = r + dr - - return tfind - - def tf_coil_area_and_masses(self): - """Subroutine to calculate the TF coil areas and masses""" - vol_case = 0.0e0 # Total TF case volume [m3] - vol_ins = 0.0e0 # Total leg turn insulation volume [m3] - vol_gr_ins = 0.0e0 # Total leg turn insulation volume [m3] - vol_cond = 0.0e0 # Total conductor insulator volume [m3] - vol_ins_leg = 0.0e0 # Outboard leg turn isulation volume [m3] - vol_gr_ins_leg = 0.0e0 # Outboard leg turn isulation volume [m3] - vol_cond_leg = 0.0e0 # Outboard leg conductor insulator volume [m3] - # --- - - # Surface areas (for cryo system) [m2] - wbtf = ( - build_variables.r_tf_inboard_out * np.sin(sctfcoil_module.theta_coil) - - build_variables.r_tf_inboard_in * sctfcoil_module.tan_theta_coil - ) - tfcoil_variables.tfocrn = ( - build_variables.r_tf_inboard_in * sctfcoil_module.tan_theta_coil - ) - tfcoil_variables.tficrn = tfcoil_variables.tfocrn + wbtf - - # Total surface area of two toroidal shells covering the TF coils [m2] - # (inside and outside surfaces) - # = 2 * centroid coil length * 2 pi R, where R is average of i/b and o/b centres - tfcoil_variables.tfcryoarea = ( - 2.0e0 - * tfcoil_variables.len_tf_coil - * constants.twopi - * 0.5e0 - * (build_variables.r_tf_inboard_mid + build_variables.r_tf_outboard_mid) - ) - - # Superconductor coil design specific calculation - # --- - if tfcoil_variables.i_tf_sup == 1: - # Mass of case [kg] - # *** - - # Mass of ground-wall insulation [kg] - # (assumed to be same density/material as turn insulation) - tfcoil_variables.whtgw = ( - tfcoil_variables.len_tf_coil - * (sctfcoil_module.awpc - sctfcoil_module.awptf) - * tfcoil_variables.dcondins - ) - - # The length of the vertical section is that of the first (inboard) segment - # = height of TF coil inner edge + (2 * coil thickness) - tfcoil_variables.cplen = (2.0e0 * build_variables.hmax) + ( - 2.0e0 * build_variables.dr_tf_inboard - ) - - # The 2.2 factor is used as a scaling factor to fit - # to the ITER-FDR value of 450 tonnes; see CCFE note T&M/PKNIGHT/PROCESS/026 - if physics_variables.itart == 1: - # tfcoil_variables.len_tf_coil does not include inboard leg ('centrepost') length in TART - tfcoil_variables.whtcas = ( - 2.2e0 - * tfcoil_variables.dcase - * ( - tfcoil_variables.cplen * tfcoil_variables.acasetf - + tfcoil_variables.len_tf_coil * tfcoil_variables.acasetfo - ) - ) - else: - tfcoil_variables.whtcas = ( - 2.2e0 - * tfcoil_variables.dcase - * ( - tfcoil_variables.cplen * tfcoil_variables.acasetf - + (tfcoil_variables.len_tf_coil - tfcoil_variables.cplen) - * tfcoil_variables.acasetfo - ) - ) - - # *** - - # Masses of conductor constituents - # --------------------------------- - # Superconductor mass [kg] - # Includes space allowance for central helium channel, area tfcoil_variables.awphec - tfcoil_variables.whtconsc = ( - tfcoil_variables.len_tf_coil - * tfcoil_variables.n_tf_turn - * tfcoil_variables.acstf - * (1.0e0 - tfcoil_variables.vftf) - * (1.0e0 - tfcoil_variables.fcutfsu) - - tfcoil_variables.len_tf_coil * tfcoil_variables.awphec - ) * tfcoil_variables.dcond[tfcoil_variables.i_tf_sc_mat - 1] - - # Copper mass [kg] - tfcoil_variables.whtconcu = ( - tfcoil_variables.len_tf_coil - * tfcoil_variables.n_tf_turn - * tfcoil_variables.acstf - * (1.0e0 - tfcoil_variables.vftf) - * tfcoil_variables.fcutfsu - - tfcoil_variables.len_tf_coil * tfcoil_variables.awphec - ) * constants.dcopper - if tfcoil_variables.whtconcu <= 0.0e0: - tfcoil_variables.whtconcu = 0.0e0 - - # Steel conduit (sheath) mass [kg] - tfcoil_variables.whtconsh = ( - tfcoil_variables.len_tf_coil - * tfcoil_variables.n_tf_turn - * tfcoil_variables.acndttf - * fwbs_variables.denstl - ) - - # Conduit insulation mass [kg] - # (tfcoil_variables.aiwp already contains tfcoil_variables.n_tf_turn) - tfcoil_variables.whtconin = ( - tfcoil_variables.len_tf_coil - * tfcoil_variables.aiwp - * tfcoil_variables.dcondins - ) - - # Total conductor mass [kg] - tfcoil_variables.whtcon = ( - tfcoil_variables.whtconsc - + tfcoil_variables.whtconcu - + tfcoil_variables.whtconsh - + tfcoil_variables.whtconin - ) - # --------------------------------- - - # Total TF coil mass [kg] (all coils) - tfcoil_variables.whttf = ( - tfcoil_variables.whtcas - + tfcoil_variables.whtcon - + tfcoil_variables.whtgw - ) * tfcoil_variables.n_tf_coils - - # If spherical tokamak, distribute between centrepost and outboard legs - # (in this case, total TF coil length = inboard `cplen` + outboard `len_tf_coil`) - if physics_variables.itart == 1: - tfleng_sph = tfcoil_variables.cplen + tfcoil_variables.len_tf_coil - tfcoil_variables.whtcp = tfcoil_variables.whttf * ( - tfcoil_variables.cplen / tfleng_sph - ) - tfcoil_variables.whttflgs = tfcoil_variables.whttf * ( - tfcoil_variables.len_tf_coil / tfleng_sph - ) - - # Resitivive magnets weights - # --- - # Rem SK : No casing for the outboard leg is considered for now # - else: - # Volumes - # ------- - # CP with joints - # --- - if physics_variables.itart == 1: - # Total volume of one outerleg [m3] - tfcoil_variables.voltfleg = ( - tfcoil_variables.len_tf_coil * tfcoil_variables.a_tf_leg_outboard - ) - - # Outboard leg TF conductor volume [m3] - vol_cond_leg = tfcoil_variables.len_tf_coil * sctfcoil_module.a_leg_cond - - # Total TF conductor volume [m3] - vol_cond = ( - tfcoil_variables.vol_cond_cp - + tfcoil_variables.n_tf_coils * vol_cond_leg - ) - - # Outboard leg TF turn insulation layer volume (per leg) [m3] - vol_ins_leg = tfcoil_variables.len_tf_coil * sctfcoil_module.a_leg_ins - - # Total turn insulation layer volume [m3] - vol_ins = ( - sctfcoil_module.vol_ins_cp - + tfcoil_variables.n_tf_coils * vol_ins_leg - ) - - # Ouboard leg TF ground insulation layer volume (per leg) [m3] - vol_gr_ins_leg = ( - tfcoil_variables.len_tf_coil * sctfcoil_module.a_leg_gr_ins - ) - - # Total ground insulation layer volume [m3] - vol_gr_ins = ( - sctfcoil_module.vol_gr_ins_cp - + tfcoil_variables.n_tf_coils * vol_gr_ins_leg - ) - - # Total volume of the CP casing [m3] - # Rem : no outer leg case - vol_case = sctfcoil_module.vol_case_cp - - # No joints - # --- - else: - # Total TF outer leg conductor volume [m3] - vol_cond = ( - tfcoil_variables.len_tf_coil - * sctfcoil_module.a_leg_cond - * tfcoil_variables.n_tf_coils - ) - - # Total turn insulation layer volume [m3] - vol_ins = ( - tfcoil_variables.len_tf_coil - * sctfcoil_module.a_leg_ins - * tfcoil_variables.n_tf_coils - ) - - # Total ground insulation volume [m3] - vol_gr_ins = ( - tfcoil_variables.len_tf_coil - * sctfcoil_module.a_leg_gr_ins - * tfcoil_variables.n_tf_coils - ) - - # Total case volume [m3] - vol_case = ( - tfcoil_variables.len_tf_coil - * tfcoil_variables.acasetf - * tfcoil_variables.n_tf_coils - ) - - # --- - # ------- - - # Copper magnets casing/conductor weights per coil [kg] - if tfcoil_variables.i_tf_sup == 0: - tfcoil_variables.whtcas = ( - fwbs_variables.denstl * vol_case / tfcoil_variables.n_tf_coils - ) # Per TF leg, no casing for outer leg - tfcoil_variables.whtconcu = ( - constants.dcopper * vol_cond / tfcoil_variables.n_tf_coils - ) - tfcoil_variables.whtconal = 0.0e0 - - # Outer legs/CP weights - if physics_variables.itart == 1: - # Weight of all the TF legs - tfcoil_variables.whttflgs = tfcoil_variables.n_tf_coils * ( - constants.dcopper * vol_cond_leg - + tfcoil_variables.dcondins * (vol_ins_leg + vol_gr_ins_leg) - ) - - # CP weight - tfcoil_variables.whtcp = ( - constants.dcopper * tfcoil_variables.vol_cond_cp - + tfcoil_variables.dcondins - * (sctfcoil_module.vol_ins_cp + sctfcoil_module.vol_gr_ins_cp) - + sctfcoil_module.vol_case_cp * fwbs_variables.denstl - ) - - # Cryo-aluminium conductor weights - # Casing made of re-inforced aluminium alloy - elif tfcoil_variables.i_tf_sup == 2: - # Casing weight (CP only if physics_variables.itart = 1)bper leg/coil - tfcoil_variables.whtcas = ( - constants.dalu * vol_case / tfcoil_variables.n_tf_coils - ) - tfcoil_variables.whtconcu = 0.0e0 - tfcoil_variables.whtconal = ( - constants.dalu * vol_cond / tfcoil_variables.n_tf_coils - ) - - # Outer legs/CP weights - if physics_variables.itart == 1: - # Weight of all the TF legs - tfcoil_variables.whttflgs = tfcoil_variables.n_tf_coils * ( - constants.dalu * vol_cond_leg - + tfcoil_variables.dcondins * (vol_ins_leg + vol_gr_ins_leg) - ) - - # CP weight - tfcoil_variables.whtcp = ( - constants.dalu * tfcoil_variables.vol_cond_cp - + tfcoil_variables.dcondins - * (sctfcoil_module.vol_ins_cp + sctfcoil_module.vol_gr_ins_cp) - + sctfcoil_module.vol_case_cp * fwbs_variables.denstl - ) - - # Turn insulation mass [kg] - tfcoil_variables.whtconin = ( - tfcoil_variables.dcondins * vol_ins / tfcoil_variables.n_tf_coils - ) - - # Ground wall insulation layer weight - tfcoil_variables.whtgw = ( - tfcoil_variables.dcondins * vol_gr_ins / tfcoil_variables.n_tf_coils - ) - - # Total weight - tfcoil_variables.whttf = ( - tfcoil_variables.whtcas - + tfcoil_variables.whtconcu - + tfcoil_variables.whtconal - + tfcoil_variables.whtconin - + tfcoil_variables.whtgw - ) * tfcoil_variables.n_tf_coils - - def peak_tf_with_ripple(self, n_tf_coils, wwp1, dr_tf_wp, tfin, bmaxtf): - """Peak toroidal field on the conductor - author: P J Knight, CCFE, Culham Science Centre - This subroutine calculates the peak toroidal field at the - outboard edge of the inboard TF coil winding pack, including - the effects of ripple. -

For 16, 18 or 20 coils, the calculation uses fitting formulae - derived by M. Kovari using MAGINT calculations on coil sets based - on a DEMO1 case. -

For other numbers of coils, the original estimate using a 9% - increase due to ripple from the axisymmetric calculation is used. - M. Kovari, Toroidal Field Coils - Maximum Field and Ripple - - Parametric Calculation, July 2014 - - :param n_tf_coils: number of TF coils - :type n_tf_coils: float - :param wwp1: width of plasma-facing face of winding pack (m) - :type wwp1: float - :param dr_tf_wp: radial thickness of winding pack (m) - :type dr_tf_wp: float - :param tfin: major radius of centre of winding pack (m) - :type tfin: float - :param bmaxtf: nominal (axisymmetric) peak toroidal field (T) - :type bmaxtf: float - - :returns: (bmaxtfrp, flag) - * bmaxtfrp: peak toroidal field including ripple (T) - * flag: flag warning of applicability problems - - :rtype: Tuple[float, int] - - """ - a = np.zeros((4,)) - flag = 0 - - # Set fitting coefficients for different numbers of TF coils - - int_n_tf = np.round(n_tf_coils) - - if int_n_tf == 16: - a[0] = 0.28101e0 - a[1] = 1.8481e0 - a[2] = -0.88159e0 - a[3] = 0.93834e0 - elif int_n_tf == 18: - a[0] = 0.29153e0 - a[1] = 1.81600e0 - a[2] = -0.84178e0 - a[3] = 0.90426e0 - elif int_n_tf == 20: - a[0] = 0.29853e0 - a[1] = 1.82130e0 - a[2] = -0.85031e0 - a[3] = 0.89808e0 - - else: - bmaxtfrp = 1.09e0 * bmaxtf - return bmaxtfrp, flag - - # Maximum winding pack width before adjacent packs touch - # (ignoring the external case and ground wall thicknesses) - - wmax = (2.0e0 * tfin + dr_tf_wp) * np.tan(np.pi / n_tf_coils) - - # Dimensionless winding pack width - - sctfcoil_module.tf_fit_t = wwp1 / wmax - if (sctfcoil_module.tf_fit_t < 0.3e0) or (sctfcoil_module.tf_fit_t > 1.1e0): - # write(*,*) 'PEAK_TF_WITH_RIPPLE: fitting problem; t = ',t - flag = 1 - - # Dimensionless winding pack radial thickness - - sctfcoil_module.tf_fit_z = dr_tf_wp / wmax - if (sctfcoil_module.tf_fit_z < 0.26e0) or (sctfcoil_module.tf_fit_z > 0.7e0): - # write(*,*) 'PEAK_TF_WITH_RIPPLE: fitting problem; z = ',z - flag = 2 - - # Ratio of peak field with ripple to nominal axisymmetric peak field - - sctfcoil_module.tf_fit_y = ( - a[0] - + a[1] * np.exp(-sctfcoil_module.tf_fit_t) - + a[2] * sctfcoil_module.tf_fit_z - + a[3] * sctfcoil_module.tf_fit_z * sctfcoil_module.tf_fit_t - ) - - bmaxtfrp = sctfcoil_module.tf_fit_y * bmaxtf - - return bmaxtfrp, flag - - def res_tf_internal_geom(self): - """ - Author : S. Kahn - Resisitve TF turn geometry, equivalent to winding_pack subroutines - - """ - sctfcoil_module.r_wp_inner = ( - build_variables.r_tf_inboard_in + tfcoil_variables.thkcas - ) - sctfcoil_module.r_wp_outer = ( - build_variables.r_tf_inboard_out - tfcoil_variables.casthi - ) - - # Conductor layer radial thickness at centercollumn top [m] - if physics_variables.itart == 1: - sctfcoil_module.dr_tf_wp_top = ( - build_variables.r_cp_top - - tfcoil_variables.casthi - - tfcoil_variables.thkcas - - build_variables.r_tf_inboard_in - ) - - # Number of turns - # Set by user (no turn structure by default, i.e. tfcoil_variables.n_tf_turn = 1 ) - if ( - abs(tfcoil_variables.n_tf_turn) - < np.finfo(float(tfcoil_variables.n_tf_turn)).eps - ): - tfcoil_variables.n_tf_turn = 1.0e0 - - # Total mid-plane cross-sectional area of winding pack, [m2] - # including the surrounding ground-wall insulation layer - sctfcoil_module.awpc = ( - np.pi - * (sctfcoil_module.r_wp_outer**2 - sctfcoil_module.r_wp_inner**2) - / tfcoil_variables.n_tf_coils - ) - - # Area of the front case, the plasma-facing case of the inner TF coil [m2] - sctfcoil_module.a_case_front = ( - np.pi - * ( - (sctfcoil_module.r_wp_outer + tfcoil_variables.casthi) ** 2 - - sctfcoil_module.r_wp_outer**2 - ) - / tfcoil_variables.n_tf_coils - ) - - # WP mid-plane cross-section excluding ground insulation per coil [m2] - sctfcoil_module.awptf = np.pi * ( - (sctfcoil_module.r_wp_outer - tfcoil_variables.tinstf) ** 2 - - (sctfcoil_module.r_wp_inner + tfcoil_variables.tinstf) ** 2 - ) / tfcoil_variables.n_tf_coils - 2.0e0 * tfcoil_variables.tinstf * ( - tfcoil_variables.dr_tf_wp - 2.0e0 * tfcoil_variables.tinstf - ) - - # Ground insulation cross-section area per coil [m2] - sctfcoil_module.a_ground_ins = sctfcoil_module.awpc - sctfcoil_module.awptf - - # Exact mid-plane cross-section area of the conductor per TF coil [m2] - a_tf_cond = np.pi * ( - ( - sctfcoil_module.r_wp_outer - - tfcoil_variables.tinstf - - tfcoil_variables.thicndut - ) - ** 2 - - ( - sctfcoil_module.r_wp_inner - + tfcoil_variables.tinstf - + tfcoil_variables.thicndut - ) - ** 2 - ) / tfcoil_variables.n_tf_coils - ( - tfcoil_variables.dr_tf_wp - - 2.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.thicndut) - ) * 2.0e0 * ( - tfcoil_variables.tinstf - + tfcoil_variables.thicndut * tfcoil_variables.n_tf_turn - ) - a_tf_cond = a_tf_cond * (1.0e0 - tfcoil_variables.fcoolcp) - - # Inter turn insulation area per coil [m2] - tfcoil_variables.aiwp = sctfcoil_module.awptf - a_tf_cond / ( - 1.0e0 - tfcoil_variables.fcoolcp - ) - - # Total insulation cross-section per coil [m2] - sctfcoil_module.a_tf_ins = tfcoil_variables.aiwp + sctfcoil_module.a_ground_ins - - # Insulation fraction [-] - sctfcoil_module.f_tf_ins = ( - tfcoil_variables.n_tf_coils - * sctfcoil_module.a_tf_ins - / tfcoil_variables.tfareain - ) - - # Total cross-sectional area of the bucking cylindre and the outer support - # support structure per coil [m2] - # physics_variables.itart = 1 : Only valid at mid-plane - tfcoil_variables.acasetf = ( - tfcoil_variables.tfareain / tfcoil_variables.n_tf_coils - ) - sctfcoil_module.awpc - - # Current per turn - tfcoil_variables.cpttf = tfcoil_variables.c_tf_total / ( - tfcoil_variables.n_tf_turn * tfcoil_variables.n_tf_coils - ) - - # Exact current density on TF oubard legs - tfcoil_variables.cdtfleg = tfcoil_variables.c_tf_total / ( - (1.0e0 - tfcoil_variables.fcoolcp) - * ( - tfcoil_variables.tftort - - 2.0e0 - * ( - tfcoil_variables.n_tf_turn * tfcoil_variables.thicndut - + tfcoil_variables.tinstf - ) - ) - * ( - build_variables.dr_tf_outboard - - 2.0e0 * (tfcoil_variables.thicndut + tfcoil_variables.tinstf) - ) - ) - - # Reporting negative WP areas issues - if sctfcoil_module.awpc < 0.0e0: - error_handling.fdiags[0] = sctfcoil_module.awpc - error_handling.fdiags[0] = tfcoil_variables.dr_tf_wp - error_handling.report_error(99) - - elif sctfcoil_module.awptf < 0.0e0: - error_handling.fdiags[0] = sctfcoil_module.awptf - error_handling.report_error(101) - - def sc_tf_internal_geom(self, i_tf_wp_geom, i_tf_case_geom, i_tf_turns_integer): - """ - Author : S. Kahn, CCFE - Seting the WP, case and turns geometry for SC magnets - """ - - # Calculating the WP / ground insulation areas - self.tf_wp_geom(i_tf_wp_geom) - - # Calculating the TF steel casing areas - self.tf_case_geom(i_tf_wp_geom, i_tf_case_geom) - - # WP/trun currents - self.tf_wp_currents() - - # sctfcoil_module.sc_tf_internal_geom( - # i_tf_wp_geom, i_tf_case_geom, i_tf_turns_integer - # ) - - # Setting the WP turn geometry / areas - if i_tf_turns_integer == 0: - # Non-ingeger number of turns - ( - tfcoil_variables.acstf, - tfcoil_variables.acndttf, - tfcoil_variables.insulation_area, - tfcoil_variables.n_tf_turn, - ) = self.tf_averaged_turn_geom( - tfcoil_variables.jwptf, - tfcoil_variables.thwcndut, - tfcoil_variables.thicndut, - tfcoil_variables.i_tf_sc_mat, - ) - - else: - # Integer number of turns - ( - tfcoil_variables.acstf, - tfcoil_variables.acndttf, - tfcoil_variables.insulation_area, - tfcoil_variables.cpttf, - tfcoil_variables.n_tf_turn, - ) = self.tf_integer_turn_geom( - tfcoil_variables.n_layer, - tfcoil_variables.n_pancake, - tfcoil_variables.thwcndut, - tfcoil_variables.thicndut, - ) - - # Areas and fractions - # ------------------- - # Central helium channel down the conductor core [m2] - tfcoil_variables.awphec = ( - 0.25e0 * tfcoil_variables.n_tf_turn * np.pi * tfcoil_variables.dhecoil**2 - ) - - # Total conductor cross-sectional area, taking account of void area - # and central helium channel [m2] - tfcoil_variables.acond = ( - tfcoil_variables.acstf - * tfcoil_variables.n_tf_turn - * (1.0e0 - tfcoil_variables.vftf) - - tfcoil_variables.awphec - ) - - # Void area in conductor for He, not including central channel [m2] - tfcoil_variables.avwp = ( - tfcoil_variables.acstf * tfcoil_variables.n_tf_turn * tfcoil_variables.vftf - ) - - # Area of inter-turn insulation: total [m2] - tfcoil_variables.aiwp = ( - tfcoil_variables.n_tf_turn * tfcoil_variables.insulation_area - ) - - # Area of steel structure in winding pack [m2] - tfcoil_variables.aswp = tfcoil_variables.n_tf_turn * tfcoil_variables.acndttf - - # Inboard coil steel area [m2] - sctfcoil_module.a_tf_steel = tfcoil_variables.acasetf + tfcoil_variables.aswp - - # Inboard coil steel fraction [-] - sctfcoil_module.f_tf_steel = ( - tfcoil_variables.n_tf_coils - * sctfcoil_module.a_tf_steel - / tfcoil_variables.tfareain - ) - - # Inboard coil insulation cross-section [m2] - sctfcoil_module.a_tf_ins = tfcoil_variables.aiwp + sctfcoil_module.a_ground_ins - - # Inboard coil insulation fraction [-] - sctfcoil_module.f_tf_ins = ( - tfcoil_variables.n_tf_coils - * sctfcoil_module.a_tf_ins - / tfcoil_variables.tfareain - ) - - # Negative areas or fractions error reporting - if ( - tfcoil_variables.acond <= 0.0e0 - or tfcoil_variables.avwp <= 0.0e0 - or tfcoil_variables.aiwp <= 0.0e0 - or tfcoil_variables.aswp <= 0.0e0 - or sctfcoil_module.a_tf_steel <= 0.0e0 - or sctfcoil_module.f_tf_steel <= 0.0e0 - or sctfcoil_module.a_tf_ins <= 0.0e0 - or sctfcoil_module.f_tf_ins <= 0.0e0 - ): - error_handling.fdiags[0] = tfcoil_variables.acond - error_handling.fdiags[1] = tfcoil_variables.avwp - error_handling.fdiags[2] = tfcoil_variables.aiwp - error_handling.fdiags[3] = tfcoil_variables.aswp - error_handling.fdiags[4] = sctfcoil_module.a_tf_steel - error_handling.fdiags[5] = sctfcoil_module.f_tf_steel - error_handling.fdiags[6] = sctfcoil_module.a_tf_ins - error_handling.fdiags[7] = sctfcoil_module.f_tf_ins - error_handling.report_error(276) - - def tf_wp_geom(self, i_tf_wp_geom): - """ - Author : S. Kahn, CCFE - Seting the WP geometry and area for SC magnets - """ - sctfcoil_module.r_wp_inner = ( - build_variables.r_tf_inboard_in + tfcoil_variables.thkcas - ) - - # Radial position of outer edge of winding pack [m] - sctfcoil_module.r_wp_outer = ( - sctfcoil_module.r_wp_inner + tfcoil_variables.dr_tf_wp - ) - - # Radius of geometrical centre of winding pack [m] - sctfcoil_module.r_wp_centre = 0.5e0 * ( - sctfcoil_module.r_wp_inner + sctfcoil_module.r_wp_outer - ) - - # TF toroidal thickness at the WP inner radius [m] - t_tf_at_wp = 2.0e0 * sctfcoil_module.r_wp_inner * sctfcoil_module.tan_theta_coil - - # Minimal toroidal thickness of winding pack [m] - sctfcoil_module.t_wp_toroidal = t_tf_at_wp - 2.0e0 * tfcoil_variables.casths - - # Rectangular WP - # -------------- - if i_tf_wp_geom == 0: - # Outer WP layer toroidal thickness [m] - tfcoil_variables.wwp1 = sctfcoil_module.t_wp_toroidal - - # Averaged toroidal thickness of of winding pack [m] - sctfcoil_module.t_wp_toroidal_av = sctfcoil_module.t_wp_toroidal - - # Total cross-sectional area of winding pack [m2] - sctfcoil_module.awpc = ( - tfcoil_variables.dr_tf_wp * sctfcoil_module.t_wp_toroidal - ) - - # WP cross-section without insertion gap and ground insulation [m2] - sctfcoil_module.awptf = ( - tfcoil_variables.dr_tf_wp - - 2.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.tfinsgap) - ) * ( - sctfcoil_module.t_wp_toroidal - - 2.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.tfinsgap) - ) - - # Cross-section area of the WP ground insulation [m2] - sctfcoil_module.a_ground_ins = ( - tfcoil_variables.dr_tf_wp - 2.0e0 * tfcoil_variables.tfinsgap - ) * ( - sctfcoil_module.t_wp_toroidal - 2.0e0 * tfcoil_variables.tfinsgap - ) - sctfcoil_module.awptf - - # Double rectangular WP - # --------------------- - elif i_tf_wp_geom == 1: - # Thickness of winding pack section at R > sctfcoil_module.r_wp_centre [m] - tfcoil_variables.wwp1 = 2.0e0 * ( - sctfcoil_module.r_wp_centre * sctfcoil_module.tan_theta_coil - - tfcoil_variables.casths - ) - - # Thickness of winding pack section at R < sctfcoil_module.r_wp_centre [m] - tfcoil_variables.wwp2 = 2.0e0 * ( - sctfcoil_module.r_wp_inner * sctfcoil_module.tan_theta_coil - - tfcoil_variables.casths - ) - - # Averaged toroidal thickness of of winding pack [m] - sctfcoil_module.t_wp_toroidal_av = 0.5e0 * ( - tfcoil_variables.wwp1 + tfcoil_variables.wwp2 - ) - - # Total cross-sectional area of winding pack [m2] - # Including ground insulation and insertion gap - sctfcoil_module.awpc = ( - tfcoil_variables.dr_tf_wp * sctfcoil_module.t_wp_toroidal_av - ) - - # WP cross-section without insertion gap and ground insulation [m2] - sctfcoil_module.awptf = ( - 0.5e0 - * ( - tfcoil_variables.dr_tf_wp - - 2.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.tfinsgap) - ) - * ( - tfcoil_variables.wwp1 - + tfcoil_variables.wwp2 - - 4.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.tfinsgap) - ) - ) - - # Cross-section area of the WP ground insulation [m2] - sctfcoil_module.a_ground_ins = ( - 0.5e0 - * (tfcoil_variables.dr_tf_wp - 2.0e0 * tfcoil_variables.tfinsgap) - * ( - tfcoil_variables.wwp1 - + tfcoil_variables.wwp2 - - 4.0e0 * tfcoil_variables.tfinsgap - ) - - sctfcoil_module.awptf - ) - - # Trapezoidal WP - # -------------- - else: - # Thickness of winding pack section at sctfcoil_module.r_wp_outer [m] - tfcoil_variables.wwp1 = 2.0e0 * ( - sctfcoil_module.r_wp_outer * sctfcoil_module.tan_theta_coil - - tfcoil_variables.casths - ) - - # Thickness of winding pack section at sctfcoil_module.r_wp_inner [m] - tfcoil_variables.wwp2 = 2.0e0 * ( - sctfcoil_module.r_wp_inner * sctfcoil_module.tan_theta_coil - - tfcoil_variables.casths - ) - - # Averaged toroidal thickness of of winding pack [m] - sctfcoil_module.t_wp_toroidal_av = 0.5e0 * ( - tfcoil_variables.wwp1 + tfcoil_variables.wwp2 - ) - - # Total cross-sectional area of winding pack [m2] - # Including ground insulation and insertion gap - sctfcoil_module.awpc = tfcoil_variables.dr_tf_wp * ( - tfcoil_variables.wwp2 - + 0.5e0 * (tfcoil_variables.wwp1 - tfcoil_variables.wwp2) - ) - - # WP cross-section without insertion gap and ground insulation [m2] - sctfcoil_module.awptf = ( - tfcoil_variables.dr_tf_wp - - 2.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.tfinsgap) - ) * ( - tfcoil_variables.wwp2 - - 2.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.tfinsgap) - + 0.5e0 * (tfcoil_variables.wwp1 - tfcoil_variables.wwp2) - ) - - # Cross-section area of the WP ground insulation [m2] - sctfcoil_module.a_ground_ins = ( - tfcoil_variables.dr_tf_wp - 2.0e0 * tfcoil_variables.tfinsgap - ) * ( - tfcoil_variables.wwp2 - - 2.0e0 * tfcoil_variables.tfinsgap - + 0.5e0 * (tfcoil_variables.wwp1 - tfcoil_variables.wwp2) - ) - sctfcoil_module.awptf - - # -------------- - - # Negative WP area error reporting - if sctfcoil_module.awptf <= 0.0e0 or sctfcoil_module.awpc <= 0.0e0: - error_handling.fdiags[0] = sctfcoil_module.awptf - error_handling.fdiags[1] = sctfcoil_module.awpc - error_handling.report_error(99) - - def tf_case_geom(self, i_tf_wp_geom, i_tf_case_geom): - """ - Author : S. Kahn, CCFE - Setting the case geometry and area for SC magnets - """ - tfcoil_variables.acasetf = ( - tfcoil_variables.tfareain / tfcoil_variables.n_tf_coils - ) - sctfcoil_module.awpc - - # Outboard leg cross-sectional area of surrounding case [m2] - tfcoil_variables.acasetfo = ( - tfcoil_variables.a_tf_leg_outboard - sctfcoil_module.awpc - ) - - # Front casing area [m2] - if i_tf_case_geom == 0: - # Circular front case - sctfcoil_module.a_case_front = ( - sctfcoil_module.theta_coil * build_variables.r_tf_inboard_out**2 - - sctfcoil_module.tan_theta_coil * sctfcoil_module.r_wp_outer**2 - ) - else: - # Straight front case - sctfcoil_module.a_case_front = ( - (sctfcoil_module.r_wp_outer + tfcoil_variables.casthi) ** 2 - - sctfcoil_module.r_wp_outer**2 - ) * sctfcoil_module.tan_theta_coil - - # Nose casing area [m2] - sctfcoil_module.a_case_nose = ( - sctfcoil_module.tan_theta_coil * sctfcoil_module.r_wp_inner**2 - - sctfcoil_module.theta_coil * build_variables.r_tf_inboard_in**2 - ) - - # Report error if the casing area is negative - if tfcoil_variables.acasetf <= 0.0e0 or tfcoil_variables.acasetfo <= 0.0e0: - error_handling.fdiags[0] = tfcoil_variables.acasetf - error_handling.fdiags[1] = tfcoil_variables.acasetfo - error_handling.report_error(99) - - # Average lateral casing thickness - # -------------- - # Rectangular casing - if i_tf_wp_geom == 0: - sctfcoil_module.t_lat_case_av = ( - tfcoil_variables.casths - + 0.5e0 * sctfcoil_module.tan_theta_coil * tfcoil_variables.dr_tf_wp - ) - - # Double rectangular WP - elif i_tf_wp_geom == 1: - sctfcoil_module.t_lat_case_av = ( - tfcoil_variables.casths - + 0.25e0 * sctfcoil_module.tan_theta_coil * tfcoil_variables.dr_tf_wp - ) - - # Trapezoidal WP - else: - sctfcoil_module.t_lat_case_av = tfcoil_variables.casths - - # -------------- - - def tf_integer_turn_geom(self, n_layer, n_pancake, thwcndut, thicndut): - """ - Authors: J Morris & S Khan - Setting the TF WP turn geometry for SC magnets from the number - of turns rows in the radial direction. The turns can have any - rectangular shapes. - This calculation has two purposes, first to check if a turn can exist - (positive cable space) and the second to provide its dimenesions, - areas and the its associated current - - """ - sctfcoil_module.rbcndut = thwcndut * 0.75e0 - - # Radial turn dimension [m] - sctfcoil_module.t_turn_radial = ( - tfcoil_variables.dr_tf_wp - - 2.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.tfinsgap) - ) / n_layer - - if sctfcoil_module.t_turn_radial <= (2.0e0 * thicndut + 2.0e0 * thwcndut): - error_handling.fdiags[0] = sctfcoil_module.t_turn_radial - error_handling.fdiags[1] = thicndut - error_handling.fdiags[2] = thwcndut - error_handling.report_error(100) - - # Toroidal turn dimension [m] - sctfcoil_module.t_turn_toroidal = ( - sctfcoil_module.t_wp_toroidal - - 2.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.tfinsgap) - ) / n_pancake - - if sctfcoil_module.t_turn_toroidal <= (2.0e0 * thicndut + 2.0e0 * thwcndut): - error_handling.fdiags[0] = sctfcoil_module.t_turn_toroidal - error_handling.fdiags[1] = thicndut - error_handling.fdiags[2] = thwcndut - error_handling.report_error(100) - - tfcoil_variables.t_turn_tf = np.sqrt( - sctfcoil_module.t_turn_radial * sctfcoil_module.t_turn_toroidal - ) - - # Number of TF turns - n_tf_turn = np.double(n_layer * n_pancake) - - # Current per turn [A/turn] - cpttf = sctfcoil_module.tfc_current / n_tf_turn - - # Radial and toroidal dimension of conductor [m] - sctfcoil_module.t_conductor_radial = ( - sctfcoil_module.t_turn_radial - 2.0e0 * thicndut - ) - sctfcoil_module.t_conductor_toroidal = ( - sctfcoil_module.t_turn_toroidal - 2.0e0 * thicndut - ) - tfcoil_variables.t_conductor = np.sqrt( - sctfcoil_module.t_conductor_radial * sctfcoil_module.t_conductor_toroidal - ) - - # Dimension of square cable space inside conduit [m] - sctfcoil_module.t_cable_radial = ( - sctfcoil_module.t_conductor_radial - 2.0e0 * thwcndut - ) - sctfcoil_module.t_cable_toroidal = ( - sctfcoil_module.t_conductor_toroidal - 2.0e0 * thwcndut - ) - sctfcoil_module.t_cable = np.sqrt( - sctfcoil_module.t_cable_radial * sctfcoil_module.t_cable_toroidal - ) - - # Cross-sectional area of cable space per turn - # taking account of rounded inside corners [m2] - acstf = (sctfcoil_module.t_cable_radial * sctfcoil_module.t_cable_toroidal) - ( - 4.0e0 - np.pi - ) * sctfcoil_module.rbcndut**2 - - if acstf <= 0.0e0: - if (sctfcoil_module.t_cable_radial < 0.0e0) or ( - sctfcoil_module.t_cable_toroidal < 0.0e0 - ): - error_handling.fdiags[0] = acstf - error_handling.fdiags[1] = sctfcoil_module.t_cable_radial - error_handling.fdiags[2] = sctfcoil_module.t_cable_toroidal - error_handling.report_error(101) - else: - error_handling.fdiags[0] = acstf - error_handling.fdiags[1] = sctfcoil_module.t_cable_radial - error_handling.fdiags[1] = sctfcoil_module.t_cable_toroidal - error_handling.report_error(102) - sctfcoil_module.rbcndut = 0.0e0 - acstf = ( - sctfcoil_module.t_cable_radial * sctfcoil_module.t_cable_toroidal - ) - - # Cross-sectional area of conduit jacket per turn [m2] - acndttf = ( - sctfcoil_module.t_conductor_radial * sctfcoil_module.t_conductor_toroidal - - acstf - ) - - # Area of inter-turn insulation: single turn [m2] - insulation_area = ( - sctfcoil_module.t_turn_radial * sctfcoil_module.t_turn_toroidal - - acndttf - - acstf - ) - return acstf, acndttf, insulation_area, cpttf, n_tf_turn - - # ------------- - - def tf_wp_currents(self): - """ - Author : S. Kahn, CCFE - Turn engineering turn currents/densities - """ - tfcoil_variables.jwptf = max( - 1.0e0, - tfcoil_variables.c_tf_total - / (tfcoil_variables.n_tf_coils * sctfcoil_module.awptf), - ) - - @staticmethod - @numba.njit(cache=True) - def stresscl( - n_tf_layer, - n_radial_array, - n_tf_wp_layers, - i_tf_bucking, - r_tf_inboard_in, - dr_bore, - hmax, - f_z_cs_tf_internal, - dr_cs, - i_tf_inside_cs, - dr_tf_inboard, - dr_cs_tf_gap, - i_pf_conductor, - j_cs_flat_top_end, - j_cs_pulse_start, - c_pf_coil_turn_peak_input, - n_pf_coils_in_group, - ld_ratio_cst, - r_out_cst, - f_a_cs_steel, - eyoung_steel, - poisson_steel, - eyoung_cond_axial, - poisson_cond_axial, - eyoung_cond_trans, - poisson_cond_trans, - eyoung_ins, - poisson_ins, - thicndut, - eyoung_copper, - poisson_copper, - i_tf_sup, - eyoung_res_tf_buck, - r_wp_inner, - tan_theta_coil, - theta_coil, - r_wp_outer, - a_tf_steel, - a_case_front, - a_case_nose, - tfinsgap, - tinstf, - n_tf_turn, - i_tf_turns_integer, - t_cable, - t_cable_radial, - dhecoil, - fcutfsu, - thwcndut, - t_lat_case_av, - t_wp_toroidal_av, - a_tf_ins, - aswp, - acond, - awpc, - eyoung_al, - poisson_al, - fcoolcp, - n_tf_graded_layers, - c_tf_total, - casthi, - i_tf_stress_model, - vforce_inboard_tot, - i_tf_tresca, - acasetf, - vforce, - acndttf, - ): - """TF coil stress routine - - - author: P J Knight, CCFE, Culham Science Centre - author: J Morris, CCFE, Culham Science Centre - author: S Kahn, CCFE, Culham Science Centre - author: J Galambos, FEDC/ORNL - - This subroutine sets up the stress calculations for the - TF coil set. - PROCESS Superconducting TF Coil Model, J. Morris, CCFE, 1st May 2014 - """ - jeff = np.zeros((n_tf_layer,)) - # Effective current density [A/m2] - - radtf = np.zeros((n_tf_layer + 1,)) - # Radii used to define the layers used in the stress models [m] - # Layers are labelled from inboard to outbard - - eyoung_trans = np.zeros((n_tf_layer,)) - # Young's moduli (one per layer) of the TF coil in the - # transverse (radial/toroidal) direction. Used in the stress - # models [Pa] - - poisson_trans = np.zeros( - (n_tf_layer,), - ) - # Poisson's ratios (one per layer) of the TF coil between the - # two transverse directions (radial and toroidal). Used in the - # stress models. - - eyoung_member_array = np.zeros((n_tf_wp_layers,)) - # Array to store the Young's moduli of the members to composite into smeared - # properties [Pa] - - poisson_member_array = np.zeros((n_tf_wp_layers,)) - # Array to store the Poisson's ratios of the members to composite into smeared - # properti - - l_member_array = np.zeros((n_tf_wp_layers,)) - # Array to store the linear dimension (thickness) of the members to composite into smeared - # properties [m] - - eyoung_axial = np.zeros((n_tf_layer,)) - # Young's moduli (one per layer) of the TF coil in the vertical - # direction used in the stress models [Pa] - - poisson_axial = np.zeros((n_tf_layer,)) - # Poisson's ratios (one per layer) of the TF coil between the - # vertical and transverse directions (in that order). Used in the - # stress models. d(transverse strain)/d(vertical strain) with - # only vertical stress. - - sig_tf_wp_av_z = np.zeros(((n_tf_layer - i_tf_bucking) * n_radial_array,)) - # TF Inboard leg WP smeared vertical stress r distribution at mid-plane [Pa] - - sig_tf_r_max = np.zeros((n_tf_layer,)) - # Radial stress of the point of maximum shear stress of each layer [Pa] - - sig_tf_t_max = np.zeros((n_tf_layer,)) - # Toroidal stress of the point of maximum shear stress of each layer [Pa] - - sig_tf_z_max = np.zeros((n_tf_layer,)) - # Vertical stress of the point of maximum shear stress of each layer [Pa] - # Rem : Currently constant but will be r dependent in the future - - sig_tf_vmises_max = np.zeros((n_tf_layer,)) - # Von-Mises stress of the point of maximum shear stress of each layer [Pa] - - s_shear_tf_peak = np.zeros((n_tf_layer,)) - # Maximum shear stress, for the Tresca yield criterion of each layer [Pa] - # If the CEA correction is addopted, the CEA corrected value is used - - sig_tf_z = np.zeros((n_tf_layer * n_radial_array,)) - # TF Inboard leg vertical tensile stress at mid-plane [Pa] - - sig_tf_smeared_r = np.zeros((n_tf_layer * n_radial_array,)) - # TF Inboard leg radial smeared stress r distribution at mid-plane [Pa] - - sig_tf_smeared_t = np.zeros((n_tf_layer * n_radial_array,)) - # TF Inboard leg tangential smeared stress r distribution at mid-plane [Pa] - - sig_tf_smeared_z = np.zeros((n_tf_layer * n_radial_array,)) - # TF Inboard leg vertical smeared stress r distribution at mid-plane [Pa] - - fac_sig_z_wp_av = 0.0 - # WP averaged vertical stress unsmearing factor - - str_tf_r = np.zeros((n_radial_array * n_tf_layer,)) - str_tf_t = np.zeros((n_radial_array * n_tf_layer,)) - str_tf_z = np.zeros((n_radial_array * n_tf_layer,)) - - sig_tf_case = None - sig_tf_cs_bucked = None - str_wp = None - casestr = None - insstrain = None - - # New extended plane strain model can handle it - if ( - abs(r_tf_inboard_in) < np.finfo(float(r_tf_inboard_in)).eps - and i_tf_stress_model != 2 - ): - raise ValueError("r_tf_inboard_in is ~= 0", 245) - - # TODO: following is no longer used/needed? - # if tfcoil_variables.acstf >= 0.0e0: - # tcbs = numpy.sqrt(tfcoil_variables.acstf) - # else: - # tcbs = 0.0e0 - - # LAYER ELASTIC PROPERTIES - # ------------------------ - # Number of bucking layers - n_tf_bucking = i_tf_bucking - - # CS properties (bucked and wedged) - # --- - if i_tf_bucking >= 2: - # Calculation performed at CS flux swing (no current on CS) - jeff[0] = 0.0e0 - - # Inner radius of the CS - if i_tf_inside_cs == 1: - # CS not used as wedge support i_tf_inside_cs = 1 - radtf[0] = 0.001 - else: - radtf[0] = dr_bore - - # Superconducting CS - if i_pf_conductor == 0: - # Getting the turn dimention from scratch - # as the TF is called before CS in caller.f90 - # -# - - # CS vertical cross-section area [m2] - if i_tf_inside_cs == 1: - a_oh = 2.0e0 * hmax * f_z_cs_tf_internal * (dr_bore - dr_tf_inboard) - else: - a_oh = 2.0e0 * hmax * f_z_cs_tf_internal * dr_cs - - # Maximum current in Central Solenoid, at either BOP or EOF [MA-turns] - # Absolute value - curr_oh_max = ( - 1.0e-6 * np.maximum(j_cs_flat_top_end, j_cs_pulse_start) * a_oh - ) - - # Number of turns - n_oh_turns = ( - 1.0e6 - * curr_oh_max - / c_pf_coil_turn_peak_input[sum(n_pf_coils_in_group)] - ) - - # CS Turn vertical cross-sectionnal area - a_cs_turn = a_oh / n_oh_turns - - # CS coil turn geometry calculation - stadium shape - # Literature: https://doi.org/10.1016/j.fusengdes.2017.04.052 - d_cond_cst = ( - a_cs_turn / ld_ratio_cst - ) ** 0.5 # width of cs turn conduit - l_cond_cst = ld_ratio_cst * d_cond_cst # length of cs turn conduit - # Radius of turn space = r_in_cst - # Radius of curved outer corrner r_out_cst = 3mm from literature - # ld_ratio_cst = 70 / 22 from literature - p1 = ((l_cond_cst - d_cond_cst) / np.pi) ** 2 - p2 = ( - (l_cond_cst * d_cond_cst) - - (4 - np.pi) * (r_out_cst**2) - - (a_cs_turn * f_a_cs_steel) - ) / np.pi - r_in_cst = -((l_cond_cst - d_cond_cst) / np.pi) + np.sqrt(p1 + p2) - t_cond_oh = ( - d_cond_cst / 2 - ) - r_in_cst # thickness of steel conduit in cs turn - - # OH/CS conduit thickness calculated assuming square conduit [m] - # The CS insulation layer is assumed to the same as the TF one - - # CS turn cable space thickness - t_cable_oh = r_in_cst * 2 - # -# - - # Smeared elastic properties of the CS - # These smearing functions were written assuming transverse- - # isotropic materials; that is not true of the CS, where the - # stiffest dimension is toroidal and the radial and vertical - # dimension are less stiff. Nevertheless this attempts to - # hit the mark. - # [EDIT: eyoung_cond is for the TF coil, not the CS coil] - - # Get transverse properties - (eyoung_trans[0], a_working, poisson_trans[0]) = eyoung_parallel( - eyoung_steel, - f_a_cs_steel, - poisson_steel, - eyoung_cond_axial, - 1e0 - f_a_cs_steel, - poisson_cond_axial, - ) - - # Get vertical properties - # Split up into "members", concentric squares in cross section - # (described in Figure 10 of the TF coil documentation) - # Conductor - eyoung_member_array[0] = eyoung_cond_trans - poisson_member_array[0] = poisson_cond_trans - l_member_array[0] = t_cable_oh - # Steel conduit - eyoung_member_array[1] = eyoung_steel - poisson_member_array[1] = poisson_steel - l_member_array[1] = 2 * t_cond_oh - # Insulation - eyoung_member_array[2] = eyoung_ins - poisson_member_array[2] = poisson_ins - l_member_array[2] = 2 * thicndut - # [EDIT: Add central cooling channel? Would be new member #1] - - # Compute the composited (smeared) properties - ( - eyoung_axial[0], - a_working, - poisson_axial[0], - eyoung_cs_stiffest_leg, - ) = eyoung_t_nested_squares( - 3, eyoung_member_array, l_member_array, poisson_member_array - ) - - # resistive CS (copper) - else: - # Here is a rough approximation - eyoung_trans[0] = eyoung_copper - eyoung_axial[0] = eyoung_copper - poisson_trans[0] = poisson_copper - poisson_axial[0] = poisson_copper - - # --- - - # CS-TF interlayer properties - # --- - if i_tf_bucking == 3: - # No current in this layer - jeff[1] = 0.0e0 - - # Outer radius of the CS - if i_tf_inside_cs == 1: - radtf[1] = dr_bore - dr_tf_inboard - dr_cs_tf_gap - else: - radtf[1] = dr_bore + dr_cs - - # Assumed to be Kapton for the moment - # Ref : https://www.dupont.com/content/dam/dupont/products-and-services/membranes-and-films/polyimde-films/documents/DEC-Kapton-summary-of-properties.pdf - eyoung_trans[1] = 2.5e9 - eyoung_axial[1] = 2.5e9 - poisson_trans[1] = 0.34e0 # Default value for young modulus - poisson_axial[1] = 0.34e0 # Default value for young modulus - - # --- - - # bucking cylinder/casing properties - # --- - if i_tf_bucking >= 1: - # No current in bucking cylinder/casing - jeff[n_tf_bucking - 1] = 0.0e0 - - if i_tf_sup == 1: - eyoung_trans[n_tf_bucking - 1] = eyoung_steel - eyoung_axial[n_tf_bucking - 1] = eyoung_steel - poisson_trans[n_tf_bucking - 1] = poisson_steel - poisson_axial[n_tf_bucking - 1] = poisson_steel - - # Bucking cylinder properties - else: - eyoung_trans[n_tf_bucking - 1] = eyoung_res_tf_buck - eyoung_axial[n_tf_bucking - 1] = eyoung_res_tf_buck - poisson_trans[n_tf_bucking - 1] = poisson_steel # Seek better value # - poisson_axial[n_tf_bucking - 1] = poisson_steel # Seek better value # - - # Innernost TF casing radius - radtf[n_tf_bucking - 1] = r_tf_inboard_in - - # --- - - # (Super)conductor layer properties - # --- - # SC coil - if i_tf_sup == 1: - # Inner/outer radii of the layer representing the WP in stress calculations [m] - # These radii are chosen to preserve the true WP area; see Issue #1048 - r_wp_inner_eff = r_wp_inner * np.sqrt(tan_theta_coil / theta_coil) - r_wp_outer_eff = r_wp_outer * np.sqrt(tan_theta_coil / theta_coil) - - # Area of the cylinder representing the WP in stress calculations [m2] - a_wp_eff = (r_wp_outer_eff**2 - r_wp_inner_eff**2) * theta_coil - - # Steel cross-section under the area representing the WP in stress calculations [m2] - a_wp_steel_eff = a_tf_steel - a_case_front - a_case_nose - - # WP effective insulation thickness (SC only) [m] - # include groundwall insulation + insertion gap in tfcoil_variables.thicndut - # inertion gap is tfcoil_variables.tfinsgap on 4 sides - t_ins_eff = thicndut + (tfinsgap + tinstf) / n_tf_turn - - # Effective WP young modulus in the toroidal direction [Pa] - # The toroidal property drives the stress calculation (J. Last report no 4) - # Hence, the radial direction is relevant for the property smearing - # Rem : This assumption might be re-defined for bucked and wedged design - - # Non-integer or interger number of turns - t_cable_eyng = t_cable if i_tf_turns_integer == 0 else t_cable_radial - - # Average WP Young's modulus in the transverse - # (radial and toroidal) direction - # Split up into "members", concentric squares in cross section - # (described in Figure 10 of the TF coil documentation) - # Helium - eyoung_member_array[0] = 0e0 - poisson_member_array[0] = poisson_steel - l_member_array[0] = dhecoil - # Conductor and co-wound copper - ( - eyoung_member_array[1], - l_member_array[1], - poisson_member_array[1], - ) = eyoung_series( - np.double(eyoung_cond_trans), - (t_cable_eyng - dhecoil) * (1.0e0 - fcutfsu), - np.double(poisson_cond_trans), - np.double(eyoung_copper), - (t_cable_eyng - dhecoil) * fcutfsu, - np.double(poisson_copper), - ) - # Steel conduit - eyoung_member_array[2] = eyoung_steel - poisson_member_array[2] = poisson_steel - l_member_array[2] = 2 * thwcndut - # Insulation - eyoung_member_array[3] = eyoung_ins - poisson_member_array[3] = poisson_ins - l_member_array[3] = 2 * t_ins_eff - - # Compute the composited (smeared) properties - ( - eyoung_wp_trans, - a_working, - poisson_wp_trans, - eyoung_wp_stiffest_leg, - ) = eyoung_t_nested_squares( - 4, - eyoung_member_array, - l_member_array, - poisson_member_array, - ) - - # Lateral casing correction (series-composition) - (eyoung_wp_trans_eff, a_working, poisson_wp_trans_eff) = eyoung_series( - eyoung_wp_trans, - np.double(t_wp_toroidal_av), - poisson_wp_trans, - np.double(eyoung_steel), - 2.0e0 * t_lat_case_av, - np.double(poisson_steel), - ) - - poisson_wp_trans_eff = np.double(poisson_wp_trans_eff) - - # Average WP Young's modulus in the vertical direction - # Split up into "members", concentric squares in cross section - # (described in Figure 10 of the TF coil documentation) - # Steel conduit - eyoung_member_array[0] = eyoung_steel - poisson_member_array[0] = poisson_steel - l_member_array[0] = aswp - # Insulation - eyoung_member_array[1] = eyoung_ins - poisson_member_array[1] = poisson_ins - l_member_array[1] = a_tf_ins - # Copper - eyoung_member_array[2] = eyoung_copper - poisson_member_array[2] = poisson_copper - l_member_array[2] = acond * fcutfsu - # Conductor - eyoung_member_array[3] = eyoung_cond_axial - poisson_member_array[3] = poisson_cond_axial - l_member_array[3] = acond * (1.0e0 - fcutfsu) - # Helium and void - eyoung_member_array[4] = 0e0 - poisson_member_array[4] = poisson_steel - l_member_array[4] = awpc - acond - a_tf_ins - aswp - # Compute the composite / smeared properties: - (eyoung_wp_axial, a_working, poisson_wp_axial) = eyoung_parallel_array( - 5, - eyoung_member_array, - l_member_array, - poisson_member_array, - ) - - # Average WP Young's modulus in the vertical direction, now including the lateral case - # Parallel-composite the steel and insulation, now including the lateral case (sidewalls) - (eyoung_wp_axial_eff, a_working, poisson_wp_axial_eff) = eyoung_parallel( - eyoung_steel, - a_wp_steel_eff - aswp, - poisson_steel, - eyoung_wp_axial, - a_working, - poisson_wp_axial, - ) - - # Resistive coil - else: - # Picking the conductor material Young's modulus - if i_tf_sup == 0: - eyoung_cond = eyoung_copper - poisson_cond = poisson_copper - elif i_tf_sup == 2: - eyoung_cond = eyoung_al - poisson_cond = poisson_al - - # Effective WP young modulus in the toroidal direction [Pa] - # Rem : effect of cooling pipes and insulation not taken into account - # for now as it needs a radially dependent Young modulus - eyoung_wp_trans_eff = eyoung_cond - eyoung_wp_trans = np.double(eyoung_cond) - poisson_wp_trans_eff = np.double(poisson_cond) - poisson_wp_trans = np.double(poisson_cond) - - # WP area using the stress model circular geometry (per coil) [m2] - a_wp_eff = (r_wp_outer**2 - r_wp_inner**2) * theta_coil - - # Effective conductor region young modulus in the vertical direction [Pa] - # Parallel-composite conductor and insulator - (eyoung_wp_axial, a_working, poisson_wp_axial) = eyoung_parallel( - eyoung_cond, - (a_wp_eff - a_tf_ins) * (1.0e0 - fcoolcp), - poisson_cond, - eyoung_ins, - a_tf_ins, - poisson_ins, - ) - # Parallel-composite cooling pipes into that - (eyoung_wp_axial, a_working, poisson_wp_axial) = eyoung_parallel( - 0e0, - (a_wp_eff - a_tf_ins) * fcoolcp, - poisson_cond, - eyoung_wp_axial, - a_working, - poisson_wp_axial, - ) - - # Effective young modulus used in stress calculations - eyoung_wp_axial_eff = eyoung_wp_axial - poisson_wp_axial_eff = poisson_wp_axial - - # Effect conductor layer inner/outer radius - r_wp_inner_eff = np.double(r_wp_inner) - r_wp_outer_eff = np.double(r_wp_outer) - - # Thickness of the layer representing the WP in stress calcualtions [m] - dr_tf_wp_eff = r_wp_outer_eff - r_wp_outer_eff - - # Thickness of WP with homogeneous stress property [m] - dr_wp_layer = dr_tf_wp_eff / n_tf_graded_layers - - for ii in range(np.intc(n_tf_graded_layers)): - # Homogeneous current in (super)conductor - jeff[n_tf_bucking + ii] = c_tf_total / ( - np.pi * (r_wp_outer_eff**2 - r_wp_inner_eff**2) - ) - - # Same thickness for all WP layers in stress calculation - radtf[n_tf_bucking + ii] = r_wp_inner_eff + ii * dr_wp_layer - - # Young modulus - eyoung_trans[n_tf_bucking + ii] = eyoung_wp_trans_eff - eyoung_axial[n_tf_bucking + ii] = eyoung_wp_axial_eff - - # Poisson's ratio - poisson_trans[n_tf_bucking + ii] = poisson_wp_trans_eff - poisson_axial[n_tf_bucking + ii] = poisson_wp_axial_eff - - # Steel case on the plasma side of the inboard TF coil - # As per Issue #1509 - jeff[n_tf_layer - 1] = 0.0e0 - radtf[n_tf_layer - 1] = r_wp_outer_eff - eyoung_trans[n_tf_layer - 1] = eyoung_steel - eyoung_axial[n_tf_layer - 1] = eyoung_steel - poisson_trans[n_tf_layer - 1] = poisson_steel - poisson_axial[n_tf_layer - 1] = poisson_steel - - # last layer radius - radtf[n_tf_layer] = r_wp_outer_eff + casthi - - # The ratio between the true cross sectional area of the - # front case, and that considered by the plane strain solver - f_tf_stress_front_case = ( - a_case_front - / theta_coil - / (radtf[n_tf_layer] ** 2 - radtf[n_tf_layer - 1] ** 2) - ) - - # Correct for the missing axial stiffness from the missing - # outer case steel as per the updated description of - # Issue #1509 - eyoung_axial[n_tf_layer - 1] = ( - eyoung_axial[n_tf_layer - 1] * f_tf_stress_front_case - ) - - # --- - # ------------------------ - - # RADIAL STRESS SUBROUTINES CALL - # ------------------------------ - # Stress model not valid the TF does not contain any hole - # (Except if i_tf_plane_stress == 2; see Issue 1414) - # Current action : trigger and error and add a little hole - # to allow stress calculations - # Rem SK : Can be easily ameneded playing around the boundary conditions - # New extended plane strain model can handle it - if abs(radtf[0]) < np.finfo(float(radtf[0])).eps and i_tf_stress_model != 2: - radtf[0] = 1.0e-9 - # --- - - # Old generalized plane stress model - # --- - if i_tf_stress_model == 1: - # Plane stress calculation (SC) [Pa] - - (sig_tf_r, sig_tf_t, deflect, radial_array) = plane_stress( - nu=poisson_trans, - rad=radtf, - ey=eyoung_trans, - j=jeff, - nlayers=int(n_tf_layer), - n_radial_array=int(n_radial_array), - ) - - # Vertical stress [Pa] - sig_tf_z[:] = vforce / ( - acasetf + acndttf * n_tf_turn - ) # Array equation [EDIT: Are you sure? It doesn't look like one to me] - - # Strain in vertical direction on WP - str_wp = sig_tf_z[n_tf_bucking] / eyoung_wp_axial_eff - - # Case strain - casestr = sig_tf_z[n_tf_bucking - 1] / eyoung_steel - - # Radial strain in insulator - insstrain = ( - sig_tf_r[n_radial_array - 1] - * eyoung_wp_stiffest_leg - / eyoung_wp_trans_eff - / eyoung_ins - ) - # --- - - # New generalized plane strain formulation - # --- - # See #1670 for why i_tf_stress_model=0 was removed - elif i_tf_stress_model in (0, 2): - # Extended plane strain calculation [Pa] - # Issues #1414 and #998 - # Permits build_variables.dr_bore >= 0, O(n) in layers - # If build_variables.dr_bore > 0, same result as generalized plane strain calculation - - ( - radial_array, - sig_tf_r, - sig_tf_t, - sig_tf_z, - str_tf_r, - str_tf_t, - str_tf_z, - deflect, - ) = extended_plane_strain( - poisson_trans, - poisson_axial, - eyoung_trans, - eyoung_axial, - radtf, - jeff, - vforce_inboard_tot, - int(n_tf_layer), - int(n_radial_array), - int(n_tf_bucking), - ) - - # Strain in TF conductor material - str_wp = str_tf_z[n_tf_bucking * n_radial_array] - - # --- - - # Storing the smeared properties for output - - sig_tf_smeared_r[:] = sig_tf_r # Array equation - sig_tf_smeared_t[:] = sig_tf_t # Array equation - sig_tf_smeared_z[:] = sig_tf_z # Array equation - - # ------------------------------ - - # STRESS DISTRIBUTIONS CORRECTIONS - # -------------------------------- - # SC central solenoid coil stress unsmearing (bucked and wedged only) - # --- - if i_tf_bucking >= 2 and i_pf_conductor == 0: - # Central Solenoid (OH) steel conduit stress unsmearing factors - for ii in range(n_radial_array): - sig_tf_r[ii] = sig_tf_r[ii] * eyoung_cs_stiffest_leg / eyoung_axial[0] - sig_tf_t[ii] = sig_tf_t[ii] * eyoung_steel / eyoung_trans[0] - sig_tf_z[ii] = sig_tf_z[ii] * eyoung_cs_stiffest_leg / eyoung_axial[0] - - # --- - - # No TF vertical forces on CS and CS-TF layer (bucked and wedged only) - # --- - # This correction is only applied if the plane stress model is used - # as the generalized plane strain calculates the vertical stress properly - if i_tf_bucking >= 2 and i_tf_stress_model == 1: - for ii in range((n_tf_bucking - 1) * n_radial_array): - sig_tf_z[ii] = 0.0e0 - - # --- - - # Toroidal coil unsmearing - # --- - # Copper magnets - if i_tf_sup == 0: - # Vertical force unsmearing factor - fac_sig_z = eyoung_copper / eyoung_wp_axial_eff - - # Toroidal WP steel stress unsmearing factor - fac_sig_t = 1.0e0 - fac_sig_r = 1.0e0 - - elif i_tf_sup == 1: - # Vertical WP steel stress unsmearing factor - if i_tf_stress_model != 1: - fac_sig_z = eyoung_steel / eyoung_wp_axial_eff - fac_sig_z_wp_av = eyoung_wp_axial / eyoung_wp_axial_eff - else: - fac_sig_z = 1.0e0 - - # Toroidal WP steel conduit stress unsmearing factor - fac_sig_t = eyoung_wp_stiffest_leg / eyoung_wp_trans_eff - - # Radial WP steel conduit stress unsmearing factor - fac_sig_r = eyoung_wp_stiffest_leg / eyoung_wp_trans_eff - - elif i_tf_sup == 2: - # Vertical WP steel stress unsmearing factor - fac_sig_z = eyoung_al / eyoung_wp_axial_eff - - # Toroidal WP steel stress unsmearing factor - # NO CALCULTED FOR THE MOMENT (to be done later) - fac_sig_t = 1.0e0 - fac_sig_r = 1.0e0 - - # Application of the unsmearing to the WP layers - # For each point within the winding pack / conductor, unsmear the - # stress. This is n_radial_array test points within tfcoil_variables.n_tf_graded_layers - # layers starting at n_tf_bucking + 1 - # GRADED MODIF : add another do loop to allow the graded properties - # to be taken into account - for ii in range( - n_tf_bucking * n_radial_array, - ((n_tf_bucking + n_tf_graded_layers) * n_radial_array), - ): - sig_tf_wp_av_z[ii - n_tf_bucking * n_radial_array] = ( - sig_tf_z[ii] * fac_sig_z_wp_av - ) - sig_tf_r[ii] = sig_tf_r[ii] * fac_sig_r - sig_tf_t[ii] = sig_tf_t[ii] * fac_sig_t - sig_tf_z[ii] = sig_tf_z[ii] * fac_sig_z - - # For each point within the front case, - # remove the correction for the missing axial - # stiffness as per the updated description of - # Issue #1509 - for ii in range( - (n_tf_bucking + n_tf_graded_layers) * n_radial_array, - (n_tf_layer * n_radial_array), - ): - sig_tf_z[ii] = sig_tf_z[ii] / f_tf_stress_front_case - # --- - - # Tresca / Von Mises yield criteria calculations - # ----------------------------- - # Array equation - s_shear_tf = np.maximum( - np.absolute(sig_tf_r - sig_tf_z), np.absolute(sig_tf_z - sig_tf_t) - ) - - # Array equation - - sig_tf_vmises = np.sqrt( - 0.5e0 - * ( - (sig_tf_r - sig_tf_t) ** 2 - + (sig_tf_r - sig_tf_z) ** 2 - + (sig_tf_z - sig_tf_t) ** 2 - ) - ) - - # Array equation - s_shear_cea_tf_cond = s_shear_tf.copy() - - # SC conducting layer stress distribution corrections - # --- - if i_tf_sup == 1: - # GRADED MODIF : add another do loop to allow the graded properties - # to be taken into account - for ii in range( - (n_tf_bucking * n_radial_array), n_tf_layer * n_radial_array - ): - # Addaped Von-mises stress calculation to WP strucure [Pa] - - svmxz = sigvm(0.0e0, sig_tf_t[ii], sig_tf_z[ii], 0.0e0, 0.0e0, 0.0e0) - - svmyz = sigvm(sig_tf_r[ii], 0.0e0, sig_tf_z[ii], 0.0e0, 0.0e0, 0.0e0) - sig_tf_vmises[ii] = max(svmxz, svmyz) - - # Maximum shear stress for the Tresca yield criterion using CEA calculation [Pa] - s_shear_cea_tf_cond[ii] = ( - 1.02e0 * abs(sig_tf_r[ii]) + 1.6e0 * sig_tf_z[ii] - ) - - # --- - # ----------------------------- - - # Output formating : Maximum shear stress of each layer for the Tresca yield criterion - # ---------------- - for ii in range(n_tf_layer): - sig_max = 0.0e0 - ii_max = 0 - - for jj in range(ii * n_radial_array, (ii + 1) * n_radial_array): - # CEA out of plane approximation - if i_tf_tresca == 1 and i_tf_sup == 1 and ii >= i_tf_bucking + 1: - if sig_max < s_shear_cea_tf_cond[jj]: - sig_max = s_shear_cea_tf_cond[jj] - ii_max = jj - - # Conventional Tresca - else: - if sig_max < s_shear_tf[jj]: - sig_max = s_shear_tf[jj] - ii_max = jj - - # OUT.DAT output - - sig_tf_r_max[ii] = sig_tf_r[ii_max] - sig_tf_t_max[ii] = sig_tf_t[ii_max] - sig_tf_z_max[ii] = sig_tf_z[ii_max] - sig_tf_vmises_max[ii] = sig_tf_vmises[ii_max] - - # Maximum shear stress for the Tresca yield criterion (or CEA OOP correction) - - if i_tf_tresca == 1 and i_tf_sup == 1 and ii >= i_tf_bucking + 1: - s_shear_tf_peak[ii] = s_shear_cea_tf_cond[ii_max] - else: - s_shear_tf_peak[ii] = s_shear_tf[ii_max] - - # Constraint equation for the Tresca yield criterion - - sig_tf_wp = s_shear_tf_peak[n_tf_bucking] - # Maximum assumed in the first graded layer - - if i_tf_bucking >= 1: - sig_tf_case = s_shear_tf_peak[n_tf_bucking - 1] - if i_tf_bucking >= 2: - sig_tf_cs_bucked = s_shear_tf_peak[0] - # ---------------- - - return ( - sig_tf_r_max, - sig_tf_t_max, - sig_tf_z_max, - sig_tf_vmises_max, - s_shear_tf_peak, - deflect, - eyoung_axial, - eyoung_trans, - eyoung_wp_axial, - eyoung_wp_trans, - poisson_wp_trans, - radial_array, - s_shear_cea_tf_cond, - poisson_wp_axial, - sig_tf_r, - sig_tf_smeared_r, - sig_tf_smeared_t, - sig_tf_smeared_z, - sig_tf_t, - s_shear_tf, - sig_tf_vmises, - sig_tf_z, - str_tf_r, - str_tf_t, - str_tf_z, - n_radial_array, - n_tf_bucking, - sig_tf_wp, - sig_tf_case, - sig_tf_cs_bucked, - str_wp, - casestr, - insstrain, - sig_tf_wp_av_z, - ) - - def outtf(self, peaktfflag): - """Writes superconducting TF coil output to file - author: P J Knight, CCFE, Culham Science Centre - outfile : input integer : output file unit - peaktfflag : input integer : warning flag from peak TF calculation - This routine writes the superconducting TF coil results - to the output file. - PROCESS Superconducting TF Coil Model, J. Morris, CCFE, 1st May 2014 - """ - - # General coil parameters - po.osubhd(self.outfile, "TF design") - po.ovarin( - self.outfile, - "Conductor technology", - "(i_tf_sup)", - tfcoil_variables.i_tf_sup, - ) - - if tfcoil_variables.i_tf_sup == 0: - po.ocmmnt( - self.outfile, " -> Resitive coil : Water cooled copper (GLIDCOP AL-15)" - ) - elif tfcoil_variables.i_tf_sup == 1: - po.ocmmnt(self.outfile, " -> Superconducting coil (SC)") - elif tfcoil_variables.i_tf_sup == 2: - po.ocmmnt(self.outfile, " -> Reisitive coil : Helium cooled aluminium") - - # SC material scaling - if tfcoil_variables.i_tf_sup == 1: - po.ovarin( - self.outfile, - "Superconductor material", - "(i_tf_sc_mat)", - tfcoil_variables.i_tf_sc_mat, - ) - - if tfcoil_variables.i_tf_sc_mat == 1: - po.ocmmnt(self.outfile, " -> ITER Nb3Sn critical surface model") - elif tfcoil_variables.i_tf_sc_mat == 2: - po.ocmmnt(self.outfile, " -> Bi-2212 high temperature superconductor") - elif tfcoil_variables.i_tf_sc_mat == 3: - po.ocmmnt(self.outfile, " -> NbTi") - elif tfcoil_variables.i_tf_sc_mat == 4: - po.ocmmnt( - self.outfile, - " -> ITER Nb3Sn critical surface model, user-defined parameters", - ) - elif tfcoil_variables.i_tf_sc_mat == 5: - po.ocmmnt(self.outfile, " -> WST Nb3Sn") - elif tfcoil_variables.i_tf_sc_mat == 6: - po.ocmmnt( - self.outfile, - " -> High temperature superconductor: REBCO HTS tape in CroCo strand", - ) - elif tfcoil_variables.i_tf_sc_mat == 7: - po.ocmmnt( - self.outfile, - " -> Durham Ginzburg-Landau critical surface model for Nb-Ti", - ) - elif tfcoil_variables.i_tf_sc_mat == 8: - po.ocmmnt( - self.outfile, - " -> Durham Ginzburg-Landau critical surface model for REBCO", - ) - elif tfcoil_variables.i_tf_sc_mat == 9: - po.ocmmnt( - self.outfile, - " -> Hazelton experimental data + Zhai conceptual model for REBCO", - ) - - # Joints strategy - po.ovarin( - self.outfile, - "Presence of TF demountable joints", - "(itart)", - physics_variables.itart, - ) - if physics_variables.itart == 1: - po.ocmmnt( - self.outfile, " -> TF coil made of a Centerpost (CP) and outer legs" - ) - po.ocmmnt(self.outfile, " interfaced with demountable joints") - else: - po.ocmmnt(self.outfile, " -> Coils without demountable joints") - - # Centring forces support strategy - po.ovarin( - self.outfile, - "TF inboard leg support strategy", - "(i_tf_bucking)", - tfcoil_variables.i_tf_bucking, - ) - - if tfcoil_variables.i_tf_bucking == 0: - po.ocmmnt(self.outfile, " -> No support structure") - elif tfcoil_variables.i_tf_bucking == 1: - if tfcoil_variables.i_tf_sup == 1: - po.ocmmnt(self.outfile, " -> Steel casing") - elif ( - abs(tfcoil_variables.eyoung_res_tf_buck - 205.0e9) - < np.finfo(float(tfcoil_variables.eyoung_res_tf_buck)).eps - ): - po.ocmmnt(self.outfile, " -> Steel bucking cylinder") - else: - po.ocmmnt(self.outfile, " -> Bucking cylinder") - - elif ( - tfcoil_variables.i_tf_bucking in (2, 3) - and build_variables.i_tf_inside_cs == 1 - ): - po.ocmmnt( - self.outfile, - " -> TF in contact with dr_bore filler support (bucked and weged design)", - ) - - elif ( - tfcoil_variables.i_tf_bucking in (2, 3) - and build_variables.i_tf_inside_cs == 0 - ): - po.ocmmnt( - self.outfile, " -> TF in contact with CS (bucked and weged design)" - ) - - # TF coil geometry - po.osubhd(self.outfile, "TF coil Geometry :") - po.ovarin( - self.outfile, - "Number of TF coils", - "(n_tf_coils)", - int(tfcoil_variables.n_tf_coils), - ) - po.ovarre( - self.outfile, - "Inboard leg centre radius (m)", - "(r_tf_inboard_mid)", - build_variables.r_tf_inboard_mid, - "OP ", - ) - po.ovarre( - constants.mfile, - "Inboard leg inner radius (m)", - "(r_tf_inboard_in)", - build_variables.r_tf_inboard_in, - "OP ", - ) - po.ovarre( - constants.mfile, - "Inboard leg outer radius (m)", - "(r_tf_inboard_out)", - build_variables.r_tf_inboard_out, - "OP ", - ) - po.ovarin( - self.outfile, - "WP shape selection switch", - "(i_tf_wp_geom)", - tfcoil_variables.i_tf_wp_geom, - ) - po.ovarre( - constants.mfile, - "Radial position of inner edge and centre of winding pack (m)", - "(r_wp_inner)", - sctfcoil_module.r_wp_inner, - "OP ", - ) - - po.ovarre( - self.outfile, - "Outboard leg centre radius (m)", - "(r_tf_outboard_mid)", - build_variables.r_tf_outboard_mid, - "OP ", - ) - po.ovarin( - self.outfile, - "Outboard leg nose case type", - "(i_tf_case_geom)", - tfcoil_variables.i_tf_case_geom, - ) - po.ovarre( - self.outfile, - "Total inboard leg radial thickness (m)", - "(dr_tf_inboard)", - build_variables.dr_tf_inboard, - ) - po.ovarre( - self.outfile, - "Total outboard leg radial thickness (m)", - "(dr_tf_outboard)", - build_variables.dr_tf_outboard, - ) - po.ovarre( - self.outfile, - "Outboard leg toroidal thickness (m)", - "(tftort)", - tfcoil_variables.tftort, - "OP ", - ) - po.ovarre( - self.outfile, - "Maximum inboard edge height (m)", - "(hmax)", - build_variables.hmax, - "OP ", - ) - if physics_variables.itart == 1: - po.ovarre( - self.outfile, - "Mean coil circumference (inboard leg not included) (m)", - "(len_tf_coil)", - tfcoil_variables.len_tf_coil, - "OP ", - ) - po.ovarre( - self.outfile, - "Length of the inboard segment (m)", - "(cplen)", - tfcoil_variables.cplen, - "OP ", - ) - else: - po.ovarre( - self.outfile, - "Mean coil circumference (including inboard leg length) (m)", - "(len_tf_coil)", - tfcoil_variables.len_tf_coil, - "OP ", - ) - - # Vertical shape - po.ovarin( - self.outfile, - "Vertical TF shape", - "(i_tf_shape)", - tfcoil_variables.i_tf_shape, - ) - if tfcoil_variables.i_tf_shape == 1: - po.oblnkl(self.outfile) - po.ocmmnt(self.outfile, "D-shape coil, inner surface shape approximated by") - po.ocmmnt( - self.outfile, - "by a straight segment and elliptical arcs between the following points:", - ) - po.oblnkl(self.outfile) - elif tfcoil_variables.i_tf_shape == 2: - po.oblnkl(self.outfile) - po.ocmmnt(self.outfile, "Picture frame coil, inner surface approximated by") - po.ocmmnt( - self.outfile, "by a straight segment between the following points:" - ) - po.oblnkl(self.outfile) - - po.write(self.outfile, " point x(m) y(m)") - for ii in range(5): - po.write( - self.outfile, - f" {ii} {tfcoil_variables.xarc[ii]} {tfcoil_variables.yarc[ii]}", - ) - po.ovarre( - constants.mfile, - f"TF coil arc point {ii} R (m)", - f"(xarc({ii + 1}))", - tfcoil_variables.xarc[ii], - ) - po.ovarre( - constants.mfile, - f"TF coil arc point {ii} Z (m)", - f"(yarc({ii + 1}))", - tfcoil_variables.yarc[ii], - ) - - # CP tapering geometry - if physics_variables.itart == 1 and tfcoil_variables.i_tf_sup != 1: - po.osubhd(self.outfile, "Tapered Centrepost TF coil Dimensions:") - po.ovarre( - self.outfile, - "TF coil centrepost outer radius at midplane (m)", - "(r_tf_inboard_out)", - build_variables.r_tf_inboard_out, - ) - po.ovarre( - self.outfile, - "TF coil centrepost outer radius at its top (m)", - "(r_cp_top)", - build_variables.r_cp_top, - ) - po.ovarre( - self.outfile, - "Top/miplane TF CP radius ratio (-)", - "(f_r_cp)", - build_variables.f_r_cp, - ) - po.ovarre( - self.outfile, - "Distance from the midplane to the top of the tapered section (m)", - "(h_cp_top)", - sctfcoil_module.h_cp_top, - ) - po.ovarre( - self.outfile, - "Distance from the midplane to the top of the centrepost (m)", - "(hmax + dr_tf_outboard)", - build_variables.hmax + build_variables.dr_tf_outboard, - ) - - # Turn/WP gemoetry - if tfcoil_variables.i_tf_sup == 1: - # Total material fraction - po.osubhd(self.outfile, "Global material area/fractions:") - po.ovarre( - self.outfile, - "TF cross-section (total) (m2)", - "(tfareain)", - tfcoil_variables.tfareain, - ) - po.ovarre( - self.outfile, - "Total steel cross-section (m2)", - "(a_tf_steel*n_tf_coils)", - sctfcoil_module.a_tf_steel * tfcoil_variables.n_tf_coils, - ) - po.ovarre( - self.outfile, - "Total steel TF fraction", - "(f_tf_steel)", - sctfcoil_module.f_tf_steel, - ) - po.ovarre( - self.outfile, - "Total Insulation cross-section (total) (m2)", - "(a_tf_ins*n_tf_coils)", - sctfcoil_module.a_tf_ins * tfcoil_variables.n_tf_coils, - ) - po.ovarre( - self.outfile, - "Total Insulation fraction", - "(f_tf_ins)", - sctfcoil_module.f_tf_ins, - ) - - # External casing - po.osubhd(self.outfile, "External steel Case Information :") - po.ovarre( - self.outfile, - "Casing cross section area (per leg) (m2)", - "(acasetf)", - tfcoil_variables.acasetf, - ) - po.ovarre( - self.outfile, - "Inboard leg case plasma side wall thickness (m)", - "(casthi)", - tfcoil_variables.casthi, - ) - po.ovarre( - self.outfile, - 'Inboard leg case inboard "nose" thickness (m)', - "(thkcas)", - tfcoil_variables.thkcas, - ) - po.ovarre( - self.outfile, - "Inboard leg case sidewall thickness at its narrowest point (m)", - "(casths)", - tfcoil_variables.casths, - ) - po.ovarre( - self.outfile, - "External case mass per coil (kg)", - "(whtcas)", - tfcoil_variables.whtcas, - "OP ", - ) - - # Winding pack structure - po.osubhd(self.outfile, "TF winding pack (WP) geometry:") - po.ovarre( - self.outfile, - "WP cross section area with insulation and insertion (per coil) (m2)", - "(awpc)", - sctfcoil_module.awpc, - ) - po.ovarre( - self.outfile, - "WP cross section area (per coil) (m2)", - "(aswp)", - sctfcoil_module.awptf, - ) - po.ovarre( - self.outfile, - "Winding pack radial thickness (m)", - "(dr_tf_wp)", - tfcoil_variables.dr_tf_wp, - "OP ", - ) - if tfcoil_variables.i_tf_turns_integer == 1: - po.ovarre( - self.outfile, - "Winding pack toroidal width (m)", - "(wwp1)", - tfcoil_variables.wwp1, - "OP ", - ) - else: - po.ovarre( - self.outfile, - "Winding pack toroidal width 1 (m)", - "(wwp1)", - tfcoil_variables.wwp1, - "OP ", - ) - po.ovarre( - self.outfile, - "Winding pack toroidal width 2 (m)", - "(wwp2)", - tfcoil_variables.wwp2, - "OP ", - ) - - po.ovarre( - self.outfile, - "Ground wall insulation thickness (m)", - "(tinstf)", - tfcoil_variables.tinstf, - ) - po.ovarre( - self.outfile, - "Winding pack insertion gap (m)", - "(tfinsgap)", - tfcoil_variables.tfinsgap, - ) - - # WP material fraction - po.osubhd(self.outfile, "TF winding pack (WP) material area/fractions:") - po.ovarre( - self.outfile, - "Steel WP cross-section (total) (m2)", - "(aswp*n_tf_coils)", - tfcoil_variables.aswp * tfcoil_variables.n_tf_coils, - ) - po.ovarre( - self.outfile, - "Steel WP fraction", - "(aswp/awpc)", - tfcoil_variables.aswp / sctfcoil_module.awpc, - ) - po.ovarre( - self.outfile, - "Insulation WP fraction", - "(aiwp/awpc)", - tfcoil_variables.aiwp / sctfcoil_module.awpc, - ) - po.ovarre( - self.outfile, - "Cable WP fraction", - "((awpc-aswp-aiwp)/awpc)", - (sctfcoil_module.awpc - tfcoil_variables.aswp - tfcoil_variables.aiwp) - / sctfcoil_module.awpc, - ) - - # Number of turns - po.osubhd(self.outfile, "WP turn information:") - po.ovarin( - self.outfile, - "Turn parametrisation", - "(i_tf_turns_integer)", - tfcoil_variables.i_tf_turns_integer, - ) - if tfcoil_variables.i_tf_turns_integer == 0: - po.ocmmnt(self.outfile, " Non-integer number of turns") - else: - po.ocmmnt(self.outfile, " Integer number of turns") - - po.ovarre( - self.outfile, - "Number of turns per TF coil", - "(n_tf_turn)", - tfcoil_variables.n_tf_turn, - "OP ", - ) - if tfcoil_variables.i_tf_turns_integer == 1: - po.ovarin( - self.outfile, - "Number of TF pancakes", - "(n_pancake)", - tfcoil_variables.n_pancake, - ) - po.ovarin( - self.outfile, - "Number of TF layers", - "(n_layer)", - tfcoil_variables.n_layer, - ) - - po.oblnkl(self.outfile) - - if tfcoil_variables.i_tf_turns_integer == 1: - po.ovarre( - self.outfile, - "Radial width of turn (m)", - "(t_turn_radial)", - sctfcoil_module.t_turn_radial, - ) - po.ovarre( - self.outfile, - "Toroidal width of turn (m)", - "(t_turn_toroidal)", - sctfcoil_module.t_turn_toroidal, - ) - po.ovarre( - self.outfile, - "Radial width of conductor (m)", - "(t_conductor_radial)", - sctfcoil_module.t_conductor_radial, - "OP ", - ) - po.ovarre( - self.outfile, - "Toroidal width of conductor (m)", - "(t_conductor_toroidal)", - sctfcoil_module.t_conductor_toroidal, - "OP ", - ) - po.ovarre( - self.outfile, - "Radial width of cable space", - "(t_cable_radial)", - sctfcoil_module.t_cable_radial, - ) - po.ovarre( - self.outfile, - "Toroidal width of cable space", - "(t_cable_toroidal)", - sctfcoil_module.t_cable_toroidal, - ) - else: - po.ovarre( - self.outfile, - "Width of turn including inter-turn insulation (m)", - "(t_turn_tf)", - tfcoil_variables.t_turn_tf, - "OP ", - ) - po.ovarre( - self.outfile, - "Width of conductor (square) (m)", - "(t_conductor)", - tfcoil_variables.t_conductor, - "OP ", - ) - po.ovarre( - self.outfile, - "Width of space inside conductor (m)", - "(t_cable)", - sctfcoil_module.t_cable, - "OP ", - ) - - po.ovarre( - self.outfile, - "Steel conduit thickness (m)", - "(thwcndut)", - tfcoil_variables.thwcndut, - ) - po.ovarre( - self.outfile, - "Inter-turn insulation thickness (m)", - "(thicndut)", - tfcoil_variables.thicndut, - ) - - if tfcoil_variables.i_tf_sc_mat in (1, 2, 3, 4, 5, 7, 8, 9): - po.osubhd(self.outfile, "Conductor information:") - po.ovarre( - self.outfile, - "Diameter of central helium channel in cable", - "(dhecoil)", - tfcoil_variables.dhecoil, - ) - po.ocmmnt(self.outfile, "Fractions by area") - po.ovarre( - self.outfile, - "internal area of the cable space", - "(acstf)", - tfcoil_variables.acstf, - ) - po.ovarre( - self.outfile, - "Coolant fraction in conductor excluding central channel", - "(vftf)", - tfcoil_variables.vftf, - ) - po.ovarre( - self.outfile, - "Copper fraction of conductor", - "(fcutfsu)", - tfcoil_variables.fcutfsu, - ) - po.ovarre( - self.outfile, - "Superconductor fraction of conductor", - "(1-fcutfsu)", - 1 - tfcoil_variables.fcutfsu, - ) - # TODO - # po.ovarre(self.outfile,'Conductor fraction of winding pack','(tfcoil_variables.acond/ap)',acond/ap, 'OP ') - # po.ovarre(self.outfile,'Conduit fraction of winding pack','(tfcoil_variables.n_tf_turn*tfcoil_variables.acndttf/ap)',n_tf_turn*tfcoil_variables.acndttf/ap, 'OP ') - # po.ovarre(self.outfile,'Insulator fraction of winding pack','(tfcoil_variables.aiwp/ap)',aiwp/ap, 'OP ') - # po.ovarre(self.outfile,'Helium area fraction of winding pack excluding central channel','(tfcoil_variables.avwp/ap)',avwp/ap, 'OP ') - # po.ovarre(self.outfile,'Central helium channel area as fraction of winding pack','(tfcoil_variables.awphec/ap)',awphec/ap, 'OP ') - ap = ( - tfcoil_variables.acond - + tfcoil_variables.n_tf_turn * tfcoil_variables.acndttf - + tfcoil_variables.aiwp - + tfcoil_variables.avwp - + tfcoil_variables.awphec - ) - po.ovarrf( - self.outfile, - "Check total area fractions in winding pack = 1", - "", - ( - tfcoil_variables.acond - + tfcoil_variables.n_tf_turn * tfcoil_variables.acndttf - + tfcoil_variables.aiwp - + tfcoil_variables.avwp - + tfcoil_variables.awphec - ) - / ap, - ) - po.ovarrf( - self.outfile, - "minimum TF conductor temperature margin (K)", - "(tmargmin_tf)", - tfcoil_variables.tmargmin_tf, - ) - po.ovarrf( - self.outfile, - "TF conductor temperature margin (K)", - "(tmargtf)", - tfcoil_variables.tmargtf, - ) - - po.ovarin( - self.outfile, - "Elastic properties behavior", - "(i_tf_cond_eyoung_axial)", - tfcoil_variables.i_tf_cond_eyoung_axial, - ) - if tfcoil_variables.i_tf_cond_eyoung_axial == 0: - po.ocmmnt(self.outfile, " Conductor stiffness neglected") - elif tfcoil_variables.i_tf_cond_eyoung_axial == 1: - po.ocmmnt(self.outfile, " Conductor stiffness is user-input") - elif tfcoil_variables.i_tf_cond_eyoung_axial == 2: - po.ocmmnt( - self.outfile, - " Conductor stiffness is set by material-specific default", - ) - - po.ovarre( - self.outfile, - "Conductor axial Youngs modulus", - "(eyoung_cond_axial)", - tfcoil_variables.eyoung_cond_axial, - ) - po.ovarre( - self.outfile, - "Conductor transverse Youngs modulus", - "(eyoung_cond_trans)", - tfcoil_variables.eyoung_cond_trans, - ) - else: - # External casing - po.osubhd(self.outfile, "Bucking cylinder information:") - po.ovarre( - self.outfile, - "Casing cross section area (per leg) (m2)", - "(acasetf)", - tfcoil_variables.acasetf, - ) - po.ovarre( - self.outfile, - "Inboard leg case plasma side wall thickness (m)", - "(casthi)", - tfcoil_variables.casthi, - ) - po.ovarre( - self.outfile, - "Inboard leg bucking cylinder thickness (m)", - "(thkcas)", - tfcoil_variables.thkcas, - ) - - # Conductor layer geometry - po.osubhd(self.outfile, "Inboard TFC conductor sector geometry:") - po.ovarre( - self.outfile, - "Inboard TFC conductor sector area with gr insulation (per leg) (m2)", - "(awpc)", - sctfcoil_module.awpc, - ) - po.ovarre( - self.outfile, - "Inboard TFC conductor sector area, NO ground & gap (per leg) (m2)", - "(awptf)", - sctfcoil_module.awptf, - ) - po.ovarre( - self.outfile, - "Inboard conductor sector radial thickness (m)", - "(dr_tf_wp)", - tfcoil_variables.dr_tf_wp, - ) - if physics_variables.itart == 1: - po.ovarre( - self.outfile, - "Central collumn top conductor sector radial thickness (m)", - "(dr_tf_wp_top)", - sctfcoil_module.dr_tf_wp_top, - ) - - po.ovarre( - self.outfile, - "Ground wall insulation thickness (m)", - "(tinstf)", - tfcoil_variables.tinstf, - ) - # Turn info - po.osubhd(self.outfile, "Coil turn information:") - po.ovarre( - self.outfile, - "Number of turns per TF leg", - "(n_tf_turn)", - tfcoil_variables.n_tf_turn, - ) - po.ovarre( - self.outfile, - "Turn insulation thickness", - "(thicndut)", - tfcoil_variables.thicndut, - ) - po.ovarre( - self.outfile, - "Mid-plane CP cooling fraction", - "(fcoolcp)", - tfcoil_variables.fcoolcp, - ) - po.ovarre( - self.outfile, - "Outboard leg current per turn (A)", - "(cpttf)", - tfcoil_variables.cpttf, - ) - po.ovarre( - self.outfile, - "Inboard leg conductor volume (m3)", - "(vol_cond_cp)", - tfcoil_variables.vol_cond_cp, - ) - po.ovarre( - self.outfile, - "Outboard leg volume per coil (m3)", - "(voltfleg)", - tfcoil_variables.voltfleg, - ) - - # Coil masses - po.osubhd(self.outfile, "TF coil mass:") - po.ovarre( - self.outfile, - "Superconductor mass per coil (kg)", - "(whtconsc)", - tfcoil_variables.whtconsc, - "OP ", - ) - po.ovarre( - self.outfile, - "Copper mass per coil (kg)", - "(whtconcu)", - tfcoil_variables.whtconcu, - "OP ", - ) - po.ovarre( - self.outfile, - "Steel conduit mass per coil (kg)", - "(whtconsh)", - tfcoil_variables.whtconsh, - "OP ", - ) - po.ovarre( - self.outfile, - "Conduit insulation mass per coil (kg)", - "(whtconin)", - tfcoil_variables.whtconin, - "OP ", - ) - if tfcoil_variables.i_tf_sup == 1: - po.ovarre( - self.outfile, - "Total conduit mass per coil (kg)", - "(whtcon)", - tfcoil_variables.whtcon, - "OP ", - ) - - if physics_variables.itart == 1: - po.ovarre( - self.outfile, - "Mass of inboard legs (kg)", - "(whtcp)", - tfcoil_variables.whtcp, - "OP ", - ) - po.ovarre( - self.outfile, - "Mass of outboard legs (kg)", - "(whttflgs)", - tfcoil_variables.whttflgs, - "OP ", - ) - - po.ovarre( - self.outfile, - "Mass of each TF coil (kg)", - "(whttf/n_tf_coils)", - tfcoil_variables.whttf / tfcoil_variables.n_tf_coils, - "OP ", - ) - po.ovarre( - self.outfile, - "Total TF coil mass (kg)", - "(whttf)", - tfcoil_variables.whttf, - "OP ", - ) - - # TF current and field - po.osubhd(self.outfile, "Maximum B field and currents:") - po.ovarre( - self.outfile, - "Nominal peak field assuming toroidal symmetry (T)", - "(bmaxtf)", - tfcoil_variables.bmaxtf, - "OP ", - ) - po.ovarre( - self.outfile, - "Total current in all TF coils (MA)", - "(c_tf_total/1.D6)", - 1.0e-6 * tfcoil_variables.c_tf_total, - "OP ", - ) - po.ovarre( - self.outfile, - "TF coil current (summed over all coils) (A)", - "(c_tf_total)", - tfcoil_variables.c_tf_total, - ) - if tfcoil_variables.i_tf_sup == 1: - po.ovarre( - self.outfile, - "Actual peak field at discrete conductor (T)", - "(bmaxtfrp)", - tfcoil_variables.bmaxtfrp, - "OP ", - ) - po.ovarre( - self.outfile, - "Winding pack current density (A/m2)", - "(jwptf)", - tfcoil_variables.jwptf, - "OP ", - ) - - po.ovarre( - self.outfile, - "Inboard leg mid-plane conductor current density (A/m2)", - "(oacdcp)", - tfcoil_variables.oacdcp, - ) - if physics_variables.itart == 1: - po.ovarre( - self.outfile, - "Outboard leg conductor current density (A/m2)", - "(cdtfleg)", - tfcoil_variables.cdtfleg, - ) - - po.ovarre( - self.outfile, - "Total stored energy in TF coils (GJ)", - "(estotftgj)", - tfcoil_variables.estotftgj, - "OP ", - ) - - # TF forces - po.osubhd(self.outfile, "TF Forces:") - po.ovarre( - self.outfile, - "Inboard vertical tension per coil (N)", - "(vforce)", - tfcoil_variables.vforce, - "OP ", - ) - po.ovarre( - self.outfile, - "Outboard vertical tension per coil (N)", - "(vforce_outboard)", - tfcoil_variables.vforce_outboard, - "OP ", - ) - po.ovarre( - self.outfile, - "inboard vertical tension fraction (-)", - "(f_vforce_inboard)", - tfcoil_variables.f_vforce_inboard, - "OP ", - ) - po.ovarre( - self.outfile, - "Centring force per coil (N/m)", - "(cforce)", - tfcoil_variables.cforce, - "OP ", - ) - - # Resistive coil parameters - if tfcoil_variables.i_tf_sup != 1: - po.osubhd(self.outfile, "Resitive loss parameters:") - if tfcoil_variables.i_tf_sup == 0: - po.ocmmnt( - self.outfile, - "Resistive Material : GLIDCOP AL-15 - Dispersion Strengthened Copper", - ) - elif tfcoil_variables.i_tf_sup == 2: - po.ocmmnt( - self.outfile, "Resistive Material : Pure Aluminium (99.999+ %)" - ) - - if physics_variables.itart == 1: - po.ovarre( - self.outfile, - "CP resistivity (ohm.m)", - "(rho_cp)", - tfcoil_variables.rho_cp, - ) - po.ovarre( - self.outfile, - "Leg resistivity (ohm.m)", - "(rho_tf_leg)", - tfcoil_variables.rho_tf_leg, - ) - po.ovarre( - self.outfile, - "CP resistive power loss (W)", - "(p_cp_resistive)", - tfcoil_variables.p_cp_resistive, - ) - po.ovarre( - self.outfile, - "Total legs resitive power loss, (W)", - "(p_tf_leg_resistive)", - tfcoil_variables.p_tf_leg_resistive, - ) - po.ovarre( - self.outfile, - "joints resistive power loss (W)", - "(pres_joints)", - tfcoil_variables.pres_joints, - ) - po.ovarre( - self.outfile, - "Outboard leg resistance per coil (ohm)", - "(res_tf_leg)", - tfcoil_variables.res_tf_leg, - ) - po.ovarre( - self.outfile, - "Average CP temperature (K)", - "(temp_cp_average)", - tfcoil_variables.temp_cp_average, - ) - po.ovarre( - self.outfile, - "Average leg temperature (K)", - "(temp_tf_legs_outboard)", - tfcoil_variables.temp_tf_legs_outboard, - ) - - else: - po.ovarre( - self.outfile, - "TF resistivity (ohm.m)", - "(p_cp_resistive)", - tfcoil_variables.rho_cp, - ) - po.ovarre( - self.outfile, - "TF coil resistive power less (total) (ohm.m)", - "(p_cp_resistive)", - tfcoil_variables.p_cp_resistive, - ) - po.ovarre( - self.outfile, - "Average coil temperature (K)", - "(temp_cp_average)", - tfcoil_variables.temp_cp_average, - ) - - # Ripple calculations - po.osubhd(self.outfile, "Ripple information:") - if tfcoil_variables.i_tf_shape == 1: - if peaktfflag == 1: - error_handling.report_error(144) - elif peaktfflag == 2: - error_handling.report_error(145) - - po.ovarre( - self.outfile, - "Max allowed tfcoil_variables.ripple amplitude at plasma outboard midplane (%)", - "(ripmax)", - tfcoil_variables.ripmax, - ) - po.ovarre( - self.outfile, - "Ripple amplitude at plasma outboard midplane (%)", - "(ripple)", - tfcoil_variables.ripple, - "OP ", - ) - else: - po.ovarre( - self.outfile, - "Max allowed tfcoil_variables.ripple amplitude at plasma (%)", - "(ripmax)", - tfcoil_variables.ripmax, - ) - po.ovarre( - self.outfile, - "Ripple at plasma edge (%)", - "(ripple)", - tfcoil_variables.ripple, - ) - po.ocmmnt( - self.outfile, - " Ripple calculation to be re-defined for picure frame coils", - ) - - # Quench information - if tfcoil_variables.i_tf_sup == 1: - po.osubhd(self.outfile, "Quench information :") - po.ovarre( - self.outfile, - "Actual quench time (or time constant) (s)", - "(tdmptf)", - tfcoil_variables.tdmptf, - ) - po.ovarre( - self.outfile, - "Vacuum Vessel stress on quench (Pa)", - "(vv_stress_quench)", - sctfcoil_module.vv_stress_quench, - "OP ", - ) - po.ovarre( - self.outfile, - "Maximum allowed voltage during quench due to insulation (kV)", - "(vdalw)", - tfcoil_variables.vdalw, - ) - po.ovarre( - self.outfile, - "Actual quench voltage (kV)", - "(vtfskv)", - tfcoil_variables.vtfskv, - "OP ", - ) - - if tfcoil_variables.i_tf_sc_mat in (1, 2, 3, 4, 5): - po.ovarre( - self.outfile, - "Maximum allowed temp rise during a quench (K)", - "(tmaxpro)", - tfcoil_variables.tmaxpro, - ) - elif tfcoil_variables == 6: - po.ocmmnt(self.outfile, "CroCo cable with jacket: ") - - if 75 in numerics.icc: - po.ovarre( - self.outfile, - "Maximum permitted TF coil current / copper area (A/m2)", - "(copperA_m2_max)", - rebco_variables.copperA_m2_max, - ) - - po.ovarre( - self.outfile, - "Actual TF coil current / copper area (A/m2)", - "(copperA_m2)", - rebco_variables.copperA_m2, - ) - - # TF coil radial build - po.osubhd(self.outfile, "Radial build of TF coil centre-line :") - # po.write(self.outfile,5) - # 5 format(t43,'Thickness (m)',t60,'Outer radius (m)') - - radius = build_variables.r_tf_inboard_in - po.obuild(self.outfile, "Innermost edge of TF coil", radius, radius) - - # Radial build for SC TF coils - if tfcoil_variables.i_tf_sup == 1: - radius = radius + tfcoil_variables.thkcas - po.obuild( - self.outfile, - 'Coil case ("nose")', - tfcoil_variables.thkcas, - radius, - "(thkcas)", - ) - - radius = radius + tfcoil_variables.tfinsgap - po.obuild( - self.outfile, - "Insertion gap for winding pack", - tfcoil_variables.tfinsgap, - radius, - "(tfinsgap)", - ) - - radius = radius + tfcoil_variables.tinstf - po.obuild( - self.outfile, - "Winding pack ground insulation", - tfcoil_variables.tinstf, - radius, - "(tinstf)", - ) - - radius = ( - radius - + 0.5e0 * tfcoil_variables.dr_tf_wp - - tfcoil_variables.tinstf - - tfcoil_variables.tfinsgap - ) - po.obuild( - self.outfile, - "Winding - first half", - tfcoil_variables.dr_tf_wp / 2e0 - - tfcoil_variables.tinstf - - tfcoil_variables.tfinsgap, - radius, - "(dr_tf_wp/2-tinstf-tfinsgap)", - ) - - radius = ( - radius - + 0.5e0 * tfcoil_variables.dr_tf_wp - - tfcoil_variables.tinstf - - tfcoil_variables.tfinsgap - ) - po.obuild( - self.outfile, - "Winding - second half", - tfcoil_variables.dr_tf_wp / 2e0 - - tfcoil_variables.tinstf - - tfcoil_variables.tfinsgap, - radius, - "(dr_tf_wp/2-tinstf-tfinsgap)", - ) - - radius = radius + tfcoil_variables.tinstf - po.obuild( - self.outfile, - "Winding pack insulation", - tfcoil_variables.tinstf, - radius, - "(tinstf)", - ) - - radius = radius + tfcoil_variables.tfinsgap - po.obuild( - self.outfile, - "Insertion gap for winding pack", - tfcoil_variables.tfinsgap, - radius, - "(tfinsgap)", - ) - - radius = radius + tfcoil_variables.casthi - po.obuild( - self.outfile, - "Plasma side case min radius", - tfcoil_variables.casthi, - radius, - "(casthi)", - ) - - radius = radius / np.cos(np.pi / tfcoil_variables.n_tf_coils) - po.obuild( - self.outfile, - "Plasma side case max radius", - build_variables.r_tf_inboard_out, - radius, - "(r_tf_inboard_out)", - ) - - # Radial build for restive coil - else: - radius = radius + tfcoil_variables.thkcas - po.obuild( - self.outfile, - "Coil bucking cylindre", - tfcoil_variables.thkcas, - radius, - "(thkcas)", - ) - - radius = radius + tfcoil_variables.tinstf - po.obuild( - self.outfile, - "Conductor ground insulation", - tfcoil_variables.tinstf, - radius, - "(tinstf)", - ) - - radius = ( - radius + 0.5e0 * tfcoil_variables.dr_tf_wp - tfcoil_variables.tinstf - ) - po.obuild( - self.outfile, - "Conductor - first half", - tfcoil_variables.dr_tf_wp / 2e0 - tfcoil_variables.tinstf, - radius, - "(tfcoil_variables.dr_tf_wp/2-tfcoil_variables.tinstf)", - ) - - radius = ( - radius + 0.5e0 * tfcoil_variables.dr_tf_wp - tfcoil_variables.tinstf - ) - po.obuild( - self.outfile, - "Conductor - second half", - tfcoil_variables.dr_tf_wp / 2e0 - tfcoil_variables.tinstf, - radius, - "(tfcoil_variables.dr_tf_wp/2-tfcoil_variables.tinstf)", - ) - - radius = radius + tfcoil_variables.tinstf - po.obuild( - self.outfile, - "Conductor ground insulation", - tfcoil_variables.tinstf, - radius, - "(tinstf)", - ) - - radius = radius + tfcoil_variables.casthi - po.obuild( - self.outfile, - "Plasma side TF coil support", - tfcoil_variables.casthi, - radius, - "(casthi)", - ) - - # Radial build consistency check - if ( - abs( - radius - build_variables.r_tf_inboard_in - build_variables.dr_tf_inboard - ) - < 10.0e0 * np.finfo(float(radius)).eps - ): - po.ocmmnt(self.outfile, "TF coil dimensions are consistent") - else: - po.ocmmnt(self.outfile, "ERROR: TF coil dimensions are NOT consistent:") - po.ovarre( - self.outfile, - "Radius of plasma-facing side of inner leg SHOULD BE [m]", - "", - build_variables.r_tf_inboard_in + build_variables.dr_tf_inboard, - ) - po.ovarre( - self.outfile, - "Inboard TF coil radial thickness [m]", - "(dr_tf_inboard)", - build_variables.dr_tf_inboard, - ) - po.oblnkl(self.outfile) - - tf_total_height = ( - build_variables.dh_tf_inner_bore + 2 * build_variables.dr_tf_inboard - ) - tf_total_width = ( - build_variables.dr_tf_inner_bore - + build_variables.dr_tf_inboard - + build_variables.dr_tf_outboard - ) - po.oblnkl(self.outfile) - po.obuild( - self.outfile, - "Total height and width of TFC [m]", - tf_total_height, - tf_total_width, - ) - - # Top section TF coil radial build (physics_variables.itart = 1 only) - if physics_variables.itart == 1 and tfcoil_variables.i_tf_sup != 1: - po.osubhd(self.outfile, "Radial build of TF coil at central collumn top :") - # write(self.outfile,5) - - # Restart the radial build at bucking cylindre inner radius - radius = build_variables.r_tf_inboard_in - po.obuild(self.outfile, "Innermost edge of TF coil", radius, radius) - - radius = radius + tfcoil_variables.thkcas - po.obuild( - self.outfile, - "Coil bucking cylindre", - tfcoil_variables.thkcas, - radius, - "(thkcas)", - ) - - radius = radius + tfcoil_variables.tinstf - po.obuild( - self.outfile, - "Conductor ground insulation", - tfcoil_variables.tinstf, - radius, - "(tinstf)", - ) - - radius = ( - radius + 0.5e0 * sctfcoil_module.dr_tf_wp_top - tfcoil_variables.tinstf - ) - po.obuild( - self.outfile, - "Conductor - first half", - 0.5e0 * sctfcoil_module.dr_tf_wp_top - tfcoil_variables.tinstf, - radius, - "(dr_tf_wp_top/2-tinstf)", - ) - - radius = ( - radius + 0.5e0 * sctfcoil_module.dr_tf_wp_top - tfcoil_variables.tinstf - ) - po.obuild( - self.outfile, - "Conductor - second half", - 0.5e0 * sctfcoil_module.dr_tf_wp_top - tfcoil_variables.tinstf, - radius, - "(dr_tf_wp_top/2-tinstf)", - ) - - radius = radius + tfcoil_variables.tinstf - po.obuild( - self.outfile, - "Conductor ground insulation", - tfcoil_variables.tinstf, - radius, - "(tinstf)", - ) - - radius = radius + tfcoil_variables.casthi - po.obuild( - self.outfile, - "Plasma side TF coil support", - tfcoil_variables.casthi, - radius, - "(casthi)", - ) - - # Consistency check - if abs(radius - build_variables.r_cp_top) < np.finfo(float(radius)).eps: - po.ocmmnt(self.outfile, "Top TF coil dimensions are consistent") - else: - po.ocmmnt(self.outfile, "ERROR: TF coil dimensions are NOT consistent:") - po.ovarre( - self.outfile, - "Radius of plasma-facing side of inner leg SHOULD BE [m]", - "", - build_variables.r_cp_top, - ) - po.oblnkl(self.outfile) - - def out_stress( - self, - sig_tf_r_max, - sig_tf_t_max, - sig_tf_z_max, - sig_tf_vmises_max, - s_shear_tf_peak, - deflect, - eyoung_axial, - eyoung_trans, - eyoung_wp_axial, - eyoung_wp_trans, - poisson_wp_trans, - radial_array, - s_shear_cea_tf_cond, - poisson_wp_axial, - sig_tf_r, - sig_tf_smeared_r, - sig_tf_smeared_t, - sig_tf_smeared_z, - sig_tf_t, - s_shear_tf, - sig_tf_vmises, - sig_tf_z, - str_tf_r, - str_tf_t, - str_tf_z, - n_radial_array, - n_tf_bucking, - sig_tf_wp_av_z, - ): - """Subroutine showing the writing the TF midplane stress analysis - in the output file and the stress distribution in the SIG_TF.json - file used to plot stress distributions - Author : S. Kahn - """ - - def table_format_arrays(a, mult=1, delim="\t\t"): - return delim.join([str(i * mult) for i in a]) - - # Stress output section - po.oheadr(self.outfile, "TF coils ") - po.osubhd(self.outfile, "TF Coil Stresses (CCFE model) :") - - if tfcoil_variables.i_tf_stress_model == 1: - po.ocmmnt(self.outfile, "Plane stress model with smeared properties") - else: - po.ocmmnt(self.outfile, "Generalized plane strain model") - - po.ovarre( - self.outfile, - "Allowable maximum shear stress in TF coil case (Tresca criterion) (Pa)", - "(sig_tf_case_max)", - tfcoil_variables.sig_tf_case_max, - ) - - po.ovarre( - self.outfile, - "Allowable maximum shear stress in TF coil conduit (Tresca criterion) (Pa)", - "(sig_tf_wp_max)", - tfcoil_variables.sig_tf_wp_max, - ) - if tfcoil_variables.i_tf_tresca == 1 and tfcoil_variables.i_tf_sup == 1: - po.ocmmnt( - self.outfile, - "WP conduit Tresca criterion corrected using CEA formula (i_tf_tresca = 1)", - ) - - if tfcoil_variables.i_tf_bucking >= 3: - po.ocmmnt( - self.outfile, "No stress limit imposed on the TF-CS interface layer" - ) - po.ocmmnt( - self.outfile, " -> Too much unknow on it material choice/properties" - ) - - # OUT.DAT data on maximum shear stress values for the Tresca criterion - po.ocmmnt( - self.outfile, - "Materal stress of the point of maximum shear stress (Tresca criterion) for each layer", - ) - po.ocmmnt( - self.outfile, - "Please use utilities/plot_stress_tf.py for radial plots plots summary", - ) - - if tfcoil_variables.i_tf_bucking == 0: - if tfcoil_variables.i_tf_sup == 1: - po.write(self.outfile, " Layers \t\t\t\t WP \t\t Outer case") - else: - po.write(self.outfile, " Layers \t\t\t\t conductor \t\t Outer case") - - elif tfcoil_variables.i_tf_bucking == 1: - if tfcoil_variables.i_tf_sup == 1: - po.write( - self.outfile, " Layers \t\t\t\t Steel case \t\t WP \t\t Outer case" - ) - else: - po.write( - self.outfile, - " Layers \t\t\t\t bucking \t\t conductor \t\t Outer case", - ) - - elif tfcoil_variables.i_tf_bucking == 2: - if tfcoil_variables.i_tf_sup == 1: - po.write( - self.outfile, - " Layers \t\t\t\t CS \t\t Steel case \t\t WP \t\t Outer case", - ) - else: - po.write( - self.outfile, - " Layers \t\t\t\t CS \t\t bucking \t\t conductor \t\t Outer case", - ) - - elif tfcoil_variables.i_tf_bucking == 3: - if tfcoil_variables.i_tf_sup == 1: - po.write( - self.outfile, - " Layers \t\t\t\t CS \t\t interface \t\t Steel case \t\t WP \t\t Outer case", - ) - else: - po.write( - self.outfile, - " Layers \t\t\t\t CS \t\t interface \t\t bucking \t\t conductor \t\t Outer case", - ) - - po.write( - self.outfile, - f" Radial stress \t\t\t (MPa) \t\t {table_format_arrays(sig_tf_r_max, 1e-6)}", - ) - po.write( - self.outfile, - f" Toroidal stress \t\t\t (MPa) \t\t {table_format_arrays(sig_tf_t_max, 1e-6)}", - ) - po.write( - self.outfile, - f" Vertical stress \t\t\t (MPa) \t\t {table_format_arrays(sig_tf_z_max, 1e-6)}", - ) - po.write( - self.outfile, - f" Von-Mises stress \t\t\t (MPa) \t\t {table_format_arrays(sig_tf_vmises_max, 1e-6)}", - ) - - if tfcoil_variables.i_tf_tresca == 1 and tfcoil_variables.i_tf_sup == 1: - po.write( - self.outfile, - f" Shear (CEA Tresca) \t\t\t (MPa) \t\t {table_format_arrays(s_shear_tf_peak, 1e-6)}", - ) - else: - po.write( - self.outfile, - f" Shear (Tresca) \t\t\t (MPa) \t\t {table_format_arrays(s_shear_tf_peak, 1e-6)}", - ) - - po.write(self.outfile, "") - po.write( - self.outfile, - f" Toroidal modulus \t\t\t (GPa) \t\t {table_format_arrays(eyoung_trans, 1e-9)}", - ) - po.write( - self.outfile, - f" Vertical modulus \t\t\t (GPa) \t\t {table_format_arrays(eyoung_axial, 1e-9)}", - ) - po.write(self.outfile, "") - po.ovarre( - self.outfile, - "WP transverse modulus (GPa)", - "(eyoung_wp_trans*1.0d-9)", - eyoung_wp_trans * 1.0e-9, - "OP ", - ) - po.ovarre( - self.outfile, - "WP vertical modulus (GPa)", - "(eyoung_wp_axial*1.0d-9)", - eyoung_wp_axial * 1.0e-9, - "OP ", - ) - po.ovarre( - self.outfile, - "WP transverse Poissons ratio", - "(poisson_wp_trans)", - poisson_wp_trans, - "OP ", - ) - po.ovarre( - self.outfile, - "WP vertical-transverse Pois. rat.", - "(poisson_wp_axial)", - poisson_wp_axial, - "OP ", - ) - - # MFILE.DAT data - for ii in range(n_tf_bucking + 2): - po.ovarre( - constants.mfile, - f"Radial stress at maximum shear of layer {ii + 1} (Pa)", - f"(sig_tf_r_max({ii + 1}))", - sig_tf_r_max[ii], - ) - po.ovarre( - constants.mfile, - f"toroidal stress at maximum shear of layer {ii + 1} (Pa)", - f"(sig_tf_t_max({ii + 1}))", - sig_tf_t_max[ii], - ) - po.ovarre( - constants.mfile, - f"Vertical stress at maximum shear of layer {ii + 1} (Pa)", - f"(sig_tf_z_max({ii + 1}))", - sig_tf_z_max[ii], - ) - po.ovarre( - constants.mfile, - f"Von-Mises stress at maximum shear of layer {ii + 1} (Pa)", - f"(sig_tf_vmises_max({ii + 1}))", - sig_tf_vmises_max[ii], - ) - if tfcoil_variables.i_tf_tresca == 1 and tfcoil_variables.i_tf_sup == 1: - po.ovarre( - constants.mfile, - f"Maximum shear stress for CEA Tresca yield criterion {ii + 1} (Pa)", - f"(s_shear_tf_peak({ii + 1}))", - s_shear_tf_peak[ii], - ) - else: - po.ovarre( - constants.mfile, - f"Maximum shear stress for the Tresca yield criterion {ii + 1} (Pa)", - f"(s_shear_tf_peak({ii + 1}))", - s_shear_tf_peak[ii], - ) - - # SIG_TF.json storage - sig_file_data = { - "Points per layers": n_radial_array, - "Radius (m)": radial_array, - "Radial stress (MPa)": sig_tf_r * 1e-6, - "Toroidal stress (MPa)": sig_tf_t * 1e-6, - "Vertical stress (MPa)": sig_tf_z * 1e-6, - "Radial smear stress (MPa)": sig_tf_smeared_r * 1e-6, - "Toroidal smear stress (MPa)": sig_tf_smeared_t * 1e-6, - "Vertical smear stress (MPa)": sig_tf_smeared_z * 1e-6, - "Von-Mises stress (MPa)": sig_tf_vmises * 1e-6, - "CEA Tresca stress (MPa)": ( - s_shear_cea_tf_cond * 1e-6 - if tfcoil_variables.i_tf_sup == 1 - else s_shear_tf * 1e-6 - ), - "rad. displacement (mm)": deflect * 1e3, - } - if tfcoil_variables.i_tf_stress_model != 1: - sig_file_data = { - **sig_file_data, - "Radial strain": str_tf_r, - "Toroidal strain": str_tf_t, - "Vertical strain": str_tf_z, - } - - if tfcoil_variables.i_tf_sup == 1: - sig_file_data = { - **sig_file_data, - "WP smeared stress (MPa)": sig_tf_wp_av_z * 1.0e-6, - } - - sig_file_data = { - k: list(v) if isinstance(v, np.ndarray) else v - for k, v in sig_file_data.items() - } - - sig_filename = ( - f2py_compatible_to_string(global_variables.output_prefix) + "SIG_TF.json" - ) - with open(sig_filename, "w") as f: - json.dump(sig_file_data, f) - - # TODO sig_tf_wp_av_z is always undefined here. This needs correcting or removing - # if ( tfcoil_variables.i_tf_sup == 1 ) : - # write(constants.sig_file,'(t2, "WP" ," smeared stress", t20, "(MPa)",t26, *(F11.3,3x))') sig_tf_wp_av_z*1.0e-6 - # - - # Quantities from the plane stress stress formulation (no resitive coil output) - if tfcoil_variables.i_tf_stress_model == 1 and tfcoil_variables.i_tf_sup == 1: - # Other quantities (displacement strain, etc..) - po.ovarre( - self.outfile, - "Maximum radial deflection at midplane (m)", - "(deflect)", - deflect[n_radial_array - 1], - "OP ", - ) - po.ovarre( - self.outfile, - "Vertical strain on casing", - "(casestr)", - tfcoil_variables.casestr, - "OP ", - ) - po.ovarre( - self.outfile, - "Radial strain on insulator", - "(insstrain)", - tfcoil_variables.insstrain, - "OP ", - ) - - def tf_averaged_turn_geom(self, jwptf, thwcndut, thicndut, i_tf_sc_mat): - """ - subroutine straight from Python, see comments in tf_averaged_turn_geom_wrapper - Authors : J. Morris, CCFE - Authors : S. Kahn, CCFE - Setting the TF WP turn geometry for SC magnets from the number - the current per turn. - This calculation has two purposes, first to check if a turn can exist - (positive cable space) and the second to provide its dimensions, - areas and the (float) number of turns - """ - - # Turn dimension is a an input - if tfcoil_variables.t_turn_tf_is_input: - # Turn area [m2] - a_turn = tfcoil_variables.t_turn_tf**2 - - # Current per turn [A] - tfcoil_variables.cpttf = a_turn * jwptf - - # Turn cable dimension is an input - elif tfcoil_variables.t_cable_tf_is_input: - # Turn squared dimension [m] - tfcoil_variables.t_turn_tf = tfcoil_variables.t_cable_tf + 2.0e0 * ( - thicndut + thwcndut - ) - - # Turn area [m2] - a_turn = tfcoil_variables.t_turn_tf**2 - - # Current per turn [A] - tfcoil_variables.cpttf = a_turn * jwptf - - # Current per turn is an input - else: - # Turn area [m2] - # Allow for additional inter-layer insulation MDK 13/11/18 - # Area of turn including conduit and inter-layer insulation - a_turn = tfcoil_variables.cpttf / jwptf - - # Dimension of square cross-section of each turn including inter-turn insulation [m] - tfcoil_variables.t_turn_tf = np.sqrt(a_turn) - - # Square turn assumption - sctfcoil_module.t_turn_radial = tfcoil_variables.t_turn_tf - sctfcoil_module.t_turn_toroidal = tfcoil_variables.t_turn_tf - - # See derivation in the following document - # k:\power plant physics and technology\process\hts\hts coil module for process.docx - tfcoil_variables.t_conductor = ( - -tfcoil_variables.layer_ins - + np.sqrt(tfcoil_variables.layer_ins**2 + 4.0e00 * a_turn) - ) / 2 - 2.0e0 * thicndut - - # Total number of turns per TF coil (not required to be an integer) - n_tf_turn = sctfcoil_module.awptf / a_turn - - # Area of inter-turn insulation: single turn [m2] - insulation_area = a_turn - tfcoil_variables.t_conductor**2 - - # NOTE: Fortran has acstf as an intent(out) variable that was outputting - # into tfcoil_variables.acstf. The local variable, however, appears to - # initially hold the value of tfcoil_variables.acstf despite not being - # intent(in). I have replicated this behaviour in Python for now. - acstf = copy.copy(tfcoil_variables.acstf) - - # ITER like turn structure - if i_tf_sc_mat != 6: - # Radius of rounded corners of cable space inside conduit [m] - rbcndut = thwcndut * 0.75e0 - - # Dimension of square cable space inside conduit [m] - sctfcoil_module.t_cable = tfcoil_variables.t_conductor - 2.0e0 * thwcndut - - # Cross-sectional area of cable space per turn - # taking account of rounded inside corners [m2] - acstf = sctfcoil_module.t_cable**2 - (4.0e0 - np.pi) * rbcndut**2 - - if acstf <= 0.0e0: - if tfcoil_variables.t_conductor < 0.0e0: - error_handling.fdiags[0] = acstf - error_handling.fdiags[1] = sctfcoil_module.t_cable - error_handling.report_error(101) - else: - error_handling.fdiags[0] = acstf - error_handling.fdiags[1] = sctfcoil_module.t_cable - error_handling.report_error(102) - rbcndut = 0.0e0 - acstf = sctfcoil_module.t_cable**2 - - # Cross-sectional area of conduit jacket per turn [m2] - acndttf = tfcoil_variables.t_conductor**2 - acstf - - # REBCO turn structure - elif i_tf_sc_mat == 6: - # Diameter of circular cable space inside conduit [m] - sctfcoil_module.t_cable = tfcoil_variables.t_conductor - 2.0e0 * thwcndut - - # Cross-sectional area of conduit jacket per turn [m2] - acndttf = tfcoil_variables.t_conductor**2 - acstf - - return acstf, acndttf, insulation_area, n_tf_turn - - -@numba.njit(cache=True) -def eyoung_t_nested_squares(n, eyoung_j_in, l_in, poisson_j_perp_in): - """ - Author : C. Swanson, PPPL - January 2022 - This subroutine gives the smeared transverse elastic - properties of n members whose cross sectional areas are - nested squares. It uses the subroutines eyoung_series and - eyoung_parallel, above, so please be aware of the assumptions - inherent in those subroutines. - - It assumes that each "leg" of the square cross section - (vertical slice, as described in Figure 10 of the TF coil - documentation) is composed of several layers under stress in - series, and each leg is under stress in parallel with every - other leg. - """ - eyoung_j_working = np.zeros((n,)) - l_working = np.zeros((n,)) - poisson_j_perp_working = np.zeros((n,)) - - # First member - eyoung_j_working[0] = eyoung_j_in[0] - l_working[0] = l_in[0] - poisson_j_perp_working[0] = poisson_j_perp_in[0] - - for ii in range(1, n): - # Initialize the leg of which this is the new member - eyoung_j_working[ii] = eyoung_j_in[ii] - l_working[ii] = l_working[ii - 1] + l_in[ii] - poisson_j_perp_working[ii] = poisson_j_perp_in[ii] - - # Serial-composite the new layer of this member into the previous legs - # changed from range(ii-1) because range(0) == [] - for jj in range(ii): - ( - eyoung_j_working[jj], - l_working[jj], - poisson_j_perp_working[jj], - ) = eyoung_series( - eyoung_j_working[ii], - l_in[ii], - poisson_j_perp_working[ii], - eyoung_j_working[jj], - l_working[jj], - poisson_j_perp_working[jj], - ) - - # Find stiffest leg - eyoung_stiffest = max(eyoung_j_working) - - eyoung_j_out = 0 - l_out = 0 - poisson_j_perp_out = 0 - - # Parallel-composite them all together - for ii in range(n): - eyoung_j_out, l_out, poisson_j_perp_out = eyoung_parallel( - eyoung_j_working[ii], - l_in[ii], - poisson_j_perp_working[ii], - eyoung_j_out, - l_out, - poisson_j_perp_out, - ) - - return eyoung_j_out, l_out, poisson_j_perp_out, eyoung_stiffest - - -@numba.njit(cache=True) -def eyoung_series(eyoung_j_1, l_1, poisson_j_perp_1, eyoung_j_2, l_2, poisson_j_perp_2): - """ - Author : C. Swanson, PPPL - January 2022 - See Issue #1205 for derivation PDF - This subroutine gives the smeared elastic properties of two - members that are carrying a force in series with each other. - The force goes in direction j. - The assumption is that the stresses in j are equal. - The smeared Young's modulus is the inverse of the average of - the inverse of the Young's moduli, weighted by the length - of the members in j. - Members 1 and 2 are the individual members to be smeared. - Member 3 is the effective smeared member (output). - The smeared Poisson's ratio is the averaged of the Poisson's - ratios, weighted by the quantity (Young's modulus / length of - the members in j). - - If you're dealing with anisotropy, please pay attention to the - fact that the specific Young's Modulus used here is that in - the j direction, and the specific Poisson's ratio used here is - that between the j and transverse directions in that order. - (transverse strain / j strain, under j stress) - The smeared Poisson's ratio is computed assuming the transverse - dynamics are isotropic, and that the two members are free to - shrink/expand under Poisson effects without interference from - each other. This may not be true of your case. - - To build up a composite smeared member of any number of - individual members, you can pass the same properties for - members 2 and 3, and call it successively, using the properties - of each member as the first triplet of arguments. This way, the - last triplet acts as a "working sum": - call eyoung_series(triplet1, triplet2, tripletOUT) - call eyoung_series(triplet3, tripletOUT, tripletOUT) - call eyoung_series(triplet4, tripletOUT, tripletOUT) - ... etc. - So that tripletOUT would eventually have the smeared properties - of the total composite member. - """ - - if eyoung_j_1 * eyoung_j_2 == 0: - # poisson_j_perp_3 = 0 - poisson_j_perp_3 = poisson_j_perp_1 if eyoung_j_1 == 0 else poisson_j_perp_2 - - eyoung_j_3 = 0.0 - l_3 = l_1 + l_2 - else: - poisson_j_perp_3 = ( - poisson_j_perp_1 * l_1 / eyoung_j_1 + poisson_j_perp_2 * l_2 / eyoung_j_2 - ) / (l_1 / eyoung_j_1 + l_2 / eyoung_j_2) - eyoung_j_3 = (l_1 + l_2) / (l_1 / eyoung_j_1 + l_2 / eyoung_j_2) - l_3 = l_1 + l_2 - - eyoung_j_3 = np.array(eyoung_j_3) - poisson_j_perp_3 = np.array(poisson_j_perp_3) - - return eyoung_j_3, l_3, poisson_j_perp_3 - - -@numba.njit(cache=True) -def eyoung_parallel( - eyoung_j_1, a_1, poisson_j_perp_1, eyoung_j_2, a_2, poisson_j_perp_2 -): - """ - Author : C. Swanson, PPPL - January 2022 - See Issue #1205 for derivation PDF - This subroutine gives the smeared elastic properties of two - members that are carrying a force in parallel with each other. - The force goes in direction j. - Members 1 and 2 are the individual members to be smeared. - Member 3 is the effective smeared member (output). - This is pretty easy because the smeared properties are simply - the average weighted by the cross-sectional areas perpendicular - to j. - The assumption is that the strains in j are equal. - If you're dealing with anisotropy, please pay attention to the - fact that the specific Young's Modulus used here is that in - the j direction, and the specific Poisson's ratio used here is - that between the j and transverse directions in that order. - (transverse strain / j strain, under j stress) - The smeared Poisson's ratio is computed assuming the transverse - dynamics are isotropic, and that the two members are free to - shrink/expand under Poisson effects without interference from - each other. This may not be true of your case. - - To build up a composite smeared member of any number of - individual members, you can pass the same properties for - members 2 and 3, and call it successively, using the properties - of each member as the first triplet of arguments. This way, the - last triplet acts as a "working sum": - call eyoung_parallel(triplet1, triplet2, tripletOUT) - call eyoung_parallel(triplet3, tripletOUT, tripletOUT) - call eyoung_parallel(triplet4, tripletOUT, tripletOUT) - ... etc. - So that tripletOUT would eventually have the smeared properties - of the total composite member. - """ - poisson_j_perp_3 = (poisson_j_perp_1 * a_1 + poisson_j_perp_2 * a_2) / (a_1 + a_2) - eyoung_j_3 = (eyoung_j_1 * a_1 + eyoung_j_2 * a_2) / (a_1 + a_2) - a_3 = a_1 + a_2 - - return eyoung_j_3, a_3, poisson_j_perp_3 - - -@numba.njit(cache=True) -def sigvm(sx: float, sy: float, sz: float, txy: float, txz: float, tyz: float) -> float: - """Calculates Von Mises stress in a TF coil - author: P J Knight, CCFE, Culham Science Centre - author: B Reimer, FEDC - This routine calculates the Von Mises combination of - stresses (Pa) in a TF coil. - - :param sx: In-plane stress in X direction [Pa] - :param sy: In-plane stress in Y direction [Pa] - :param sz: In-plane stress in Z direction [Pa] - :param txy: Out-of-plane stress in X-Y plane [Pa] - :param txz: Out-of-plane stress in X-Z plane [Pa] - :param tyz: Out-of-plane stress in Y-Z plane [Pa] - - :returns: Von Mises combination of stresses (Pa) in a TF coil. - """ - - return np.sqrt( - 0.5 - * ( - (sx - sy) ** 2 - + (sx - sz) ** 2 - + (sz - sy) ** 2 - + 6 * (txy**2 + txz**2 + tyz**2) - ) - ) - - -@numba.njit(cache=True, error_model="numpy") -def extended_plane_strain( - nu_t, - nu_zt, - ey_t, - ey_z, - rad, - d_curr, - v_force, - nlayers, - n_radial_array, - i_tf_bucking, -): - """Author : C. Swanson, PPPL and S. Kahn, CCFE - September 2021 - There is a writeup of the derivation of this model on the gitlab server. - https://git.ccfe.ac.uk/process/process/-/issues/1414 - This surboutine estimates the radial displacement, stresses, and strains of - the inboard midplane of the TF. It assumes that structures are axisymmetric - and long in the axial (z) direction, the "axisymmetric extended plane strain" - problem. The TF is assumed to be constructed from some number of layers, - within which materials properties and current densities are uniform. - The 1D radially-resolved solution is reduced to a 0D matrix inversion problem - using analytic solutions to Lame's thick cylinder problem. Materials may be - transverse-isotropic in Young's modulus and Poisson's ratio. The consraints - are: Either zero radial stress or zero radial displacement at the inner - surface (depending on whether the inner radius is zero), zero radial stress - at the outer surface, total axial force (tension) is equal to the input, - and optionally the axial force of an inner subset of cylinders is zero (slip - conditions between the inner and outer subset). The matrix inversion / linear - solve is always 4x4, no matter how many layers there are. - The problem is formulated around a solution vector (A,B,eps_z,1.0,eps_z_slip) - where A and B are the parameters in Lame's solution where u = A*r + B/r, u - is the radial displacement. eps_z is the axial strain on the outer, force- - carrying layers. eps_z_slip is the axial strain on the inner, non-force- - carrying layers (optionally). The solution vector is A,B at the outermost - radius, and is transformed via matrix multiplication into those A,B - values at other radii. The constraints are inner products with this vector, - and so when stacked form a matrix to invert. - """ - # outputs - sigr = np.zeros((n_radial_array * nlayers,)) - # Stress distribution in the radial direction (r) [Pa] - - sigt = np.zeros((n_radial_array * nlayers,)) - # Stress distribution in the toroidal direction (t) [Pa] - - sigz = np.zeros((n_radial_array * nlayers,)) - # Stress distribution in the vertical direction (z) - - str_r = np.zeros((n_radial_array * nlayers,)) - # Strain distribution in the radial direction (r) - - str_t = np.zeros((n_radial_array * nlayers,)) - # Strain distribution in the toroidal direction (t) - - str_z = np.zeros((n_radial_array * nlayers,)) - # Uniform strain in the vertical direction (z) - - r_deflect = np.zeros((n_radial_array * nlayers,)) - # Radial displacement radial distribution [m] - - rradius = np.zeros((n_radial_array * nlayers,)) - # Radius array [m] - - # local arrays - # Stiffness form of compliance tensor - nu_tz = np.zeros((nlayers,)) - # Transverse-axial Poisson's ratio - # (ratio of axial strain to transverse strain upon transverse stress) - ey_bar_z = np.zeros((nlayers,)) - # Axial effective Young's modulus (zero cross-strains, not stresses) [Pa] - ey_bar_t = np.zeros((nlayers,)) - # Transverse effective Young's modulus [Pa] - nu_bar_t = np.zeros((nlayers,)) - # Transverse effective Poisson's ratio - nu_bar_tz = np.zeros((nlayers,)) - # Transverse-axial effective Poisson's ratio - nu_bar_zt = np.zeros((nlayers,)) - # Axial-transverse effective Poisson's ratio - - # Lorentz force parameters - currents = np.zeros((nlayers,)) - # Currents in each layer [A] - currents_enclosed = np.zeros((nlayers,)) - # Currents enclosed by inner radius of each layer [A] - f_lin_fac = np.zeros((nlayers,)) - # Factor that multiplies r linearly in the force density - f_rec_fac = np.zeros((nlayers,)) - # Factor that multiplies r reciprocally in the force density - f_int_a = np.zeros((nlayers,)) - # Force density integral that adds to Lame parameter A - f_int_b = np.zeros((nlayers,)) - # Force density integral that adds to Lame parameter B - - # Layer transfer matrices - m_int = np.zeros( - ( - 5, - 5, - nlayers, - ), - ) - # Matrix that transforms the Lame parmeter vector from the - # outer radius to the inner radius of each layer - m_ext = np.zeros( - ( - 5, - 5, - nlayers, - ), - ) - # Matrix that transforms the Lame parmeter vector from the - # inner radius of one layer to the outer radius of the - # next inner. - m_tot = np.zeros( - ( - 5, - 5, - nlayers, - ), - ) - # Matrix that transforms the Lame parmeter vector from the - # outer radius of the outer layer to the inner radius of - # each. - - # Axial force inner product - v_force_row = np.zeros( - ( - 1, - 5, - ), - ) - # Row vector (matrix multiplication is inner product) to - # obtain the axial force from the force-carrying layers - v_force_row_slip = np.zeros( - ( - 1, - 5, - ), - ) - # Row vector (matrix multiplication is inner product) to - # obtain the axial force inner slip layers (no net force) - rad_row_helper = np.zeros( - ( - 1, - 5, - ), - ) - # A helper variable to store [radius, 1, 0, 0, 0] in row - - # Boundary condition matrix - m_bc = np.zeros( - ( - 4, - 5, - ), - ) - # Boundary condition matrix. Multiply this with the - # outermost solution vector, (A,B,eps_z,1.0,eps_z_slip), - # to obtain a zero vector. - m_toinv = np.zeros( - ( - 4, - 4, - ), - ) - # Matrix to invert to get the solution - RHS_vec = np.zeros((4,)) - # Right-hand-side vector to divide m_toinv - a_vec_solution = np.zeros((5,)) - # Solution vector, Lame parameters at outer radius, strain - # of force-carrying layers, and strain of slip layers - # (A,B,eps_z,1,eps_z_slip) - - # Constructing the solution everywhere - a_vec_layer = np.zeros((5,)) - # Lame parameters and strains vector at outer radius - # of each layer - - # The stress calcualtion differential equations is analytically sloved - # The final solution is given by the layer boundary conditions on - # radial stress and displacement between layers solved - # The problem is set as aa.cc = bb, cc being the constant we search - - # Inner slip layers parameters - # Section 15 in the writeup - # Innermost layer that takes axial force. Layers inner of this - # have zero net axial force, to include CS decoupling. - # This configuration JUST HAPPENS to work out because of - # the specific i_tf_bucking options; if those are changed, - # will need a switch here. - nonslip_layer = i_tf_bucking - - if nonslip_layer < 1: - nonslip_layer = 1 - - # Stiffness tensor factors - # Section 3 in the writeup - # With Section 12 anisotropic materials properties - # *** - # Dependent Poisson's ratio: nu-transverse-axial - # from nu-axial-transverse and the Young's moduli - nu_tz[:] = nu_zt * ey_t / ey_z - - # Effective Young's Moduli and Poisson's ratios - # holding strain, not stress, cross-terms constant - ey_bar_z[:] = ey_z * (1 - nu_t) / (1 - nu_t - 2 * nu_tz * nu_zt) - ey_bar_t[:] = ( - ey_t * (1 - nu_tz * nu_zt) / (1 - nu_t - 2 * nu_tz * nu_zt) / (1 + nu_t) - ) - - nu_bar_t[:] = (nu_t + nu_tz * nu_zt) / (1 - nu_tz * nu_zt) - nu_bar_tz[:] = nu_tz / (1 - nu_t) - nu_bar_zt[:] = nu_zt * (1 + nu_t) / (1 - nu_tz * nu_zt) - - # Lorentz force parameters - # Section 13 in the writeup - # *** - # Currents in each layer [A] - currents[:] = np.pi * d_curr * (rad[1 : nlayers + 1] ** 2 - rad[:nlayers] ** 2) - # Currents enclosed by inner radius of each layer [A] - currents_enclosed[0] = 0.0e0 - - for ii in range(1, nlayers): - currents_enclosed[ii] = currents_enclosed[ii - 1] + currents[ii - 1] - # Factor that multiplies r linearly in the force density - f_lin_fac[:] = RMU0 / 2.0e0 * d_curr**2 - # Factor that multiplies r reciprocally in the force density - f_rec_fac[:] = ( - RMU0 - / 2.0e0 - * (d_curr * currents_enclosed / np.pi - d_curr**2 * rad[:nlayers] ** 2) - ) - # Force density integral that adds to Lame parameter A - f_int_a[:] = 0.5e0 * f_lin_fac * ( - rad[1 : nlayers + 1] ** 2 - rad[:nlayers] ** 2 - ) + f_rec_fac * np.log(rad[1 : nlayers + 1] / rad[:nlayers]) - if f_rec_fac[0] == 0e0: - f_int_a[0] = 0.5e0 * f_lin_fac[0] * (rad[1] ** 2 - rad[0] ** 2) - - # Force density integral that adds to Lame parameter B - f_int_b[:] = 0.25e0 * f_lin_fac * ( - rad[1 : nlayers + 1] ** 4 - rad[:nlayers] ** 4 - ) + 0.5e0 * f_rec_fac * (rad[1 : nlayers + 1] ** 2 - rad[:nlayers] ** 2) - - # Transformation matrix from outer to inner Lame parameters - # Section 5 in the writeup - # With Section 12 anisotropic materials properties - # *** - # m_int[kk] multiplies Lame parameter vector of layer kk (A,B,eps_z,1.0,eps_z_slip) - # and transforms the values at the outer radius to the values at the inner radius - for kk in range(nlayers): - m_int[0, 0, kk] = 1.0e0 - m_int[1, 1, kk] = 1.0e0 - m_int[2, 2, kk] = 1.0e0 - m_int[3, 3, kk] = 1.0e0 - m_int[4, 4, kk] = 1.0e0 - - m_int[0, 3, kk] = -0.5e0 / ey_bar_t[kk] * f_int_a[kk] - m_int[1, 3, kk] = 0.5e0 / ey_bar_t[kk] * f_int_b[kk] - - # Transformation matrix between layers - # Section 6 in the writeup - # With Section 12 anisotropic materials properties - # With Section 15 inner slip-decoupled layers - # *** - # m_ext[kk] multiplies Lame parameter vector of layer kk (A,B,eps_z,1.0,eps_z_slip) - # and transforms the values at the inner radius to the values at the outer radius - # of layer kk-1 - for kk in range(1, nonslip_layer - 1): - ey_fac = ey_bar_t[kk] / ey_bar_t[kk - 1] - m_ext[0, 2, kk] = 0.0e0 - m_ext[0, 4, kk] = 0.5e0 * (ey_fac * nu_bar_zt[kk] - nu_bar_zt[kk - 1]) - - if nonslip_layer > 1: - ey_fac = ey_bar_t[nonslip_layer - 1] / ey_bar_t[nonslip_layer - 2] - m_ext[0, 2, nonslip_layer - 1] = 0.5e0 * ey_fac * nu_bar_zt[nonslip_layer - 1] - m_ext[0, 4, nonslip_layer - 1] = 0.5e0 * (-nu_bar_zt[nonslip_layer - 2]) - - for kk in range(nonslip_layer, nlayers): - ey_fac = ey_bar_t[kk] / ey_bar_t[kk - 1] - m_ext[0, 2, kk] = 0.5e0 * (ey_fac * nu_bar_zt[kk] - nu_bar_zt[kk - 1]) - m_ext[0, 4, kk] = 0.0e0 - - for kk in range(1, nlayers): - ey_fac = ey_bar_t[kk] / ey_bar_t[kk - 1] - m_ext[0, 0, kk] = 0.5e0 * (ey_fac * (1 + nu_bar_t[kk]) + 1 - nu_bar_t[kk - 1]) - if rad[kk] > 0e0: - m_ext[0, 1, kk] = ( - 0.5e0 - / rad[kk] ** 2 - * (1 - nu_bar_t[kk - 1] - ey_fac * (1 - nu_bar_t[kk])) - ) - - m_ext[1, 0, kk] = rad[kk] ** 2 * (1 - m_ext[0, 0, kk]) - m_ext[1, 1, kk] = 1 - rad[kk] ** 2 * m_ext[0, 1, kk] - m_ext[1, 2, kk] = -(rad[kk] ** 2) * m_ext[0, 2, kk] - m_ext[1, 4, kk] = -(rad[kk] ** 2) * m_ext[0, 4, kk] - m_ext[2, 2, kk] = 1.0e0 - m_ext[3, 3, kk] = 1.0e0 - m_ext[4, 4, kk] = 1.0e0 - - # Total transformation matrix, from Lame parmeters at outside to - # Lame parameters at inside of each layer - # Section 7 in the writeup - # *** - m_tot[:, :, nlayers - 1] = m_int[:, :, nlayers - 1] - - for kk in range(nlayers - 2, -1, -1): - m_tot[:, :, kk] = np.ascontiguousarray(m_int[:, :, kk]) @ ( - np.ascontiguousarray(m_ext[:, :, kk + 1]) - @ np.ascontiguousarray(m_tot[:, :, kk + 1]) - ) - - # Axial force inner product. Dot-product this with the - # outermost solution vector, (A,B,eps_z,1.0,eps_z_slip), - # to obtain the axial force. - # Section 8 in the writeup - # *** - # Axial stiffness products - ey_bar_z_area = np.pi * sum( - ey_bar_z[nonslip_layer - 1 : nlayers] - * ( - rad[nonslip_layer : nlayers + 1] ** 2 - - rad[nonslip_layer - 1 : nlayers] ** 2 - ) - ) - ey_bar_z_area_slip = np.pi * sum( - ey_bar_z[: nonslip_layer - 1] - * (rad[1:nonslip_layer] ** 2 - rad[: nonslip_layer - 1] ** 2) - ) - - # Axial stiffness inner product, for layers which carry axial force - rad_row_helper[0, :] = [rad[nlayers] ** 2, 1e0, 0e0, 0e0, 0e0] - v_force_row[:, :] = ( - 2e0 * np.pi * ey_bar_z[nlayers - 1] * nu_bar_tz[nlayers - 1] * rad_row_helper - ) - rad_row_helper[0, :] = [rad[nonslip_layer - 1] ** 2, 1e0, 0e0, 0e0, 0e0] - v_force_row[:, :] = v_force_row - 2e0 * np.pi * ey_bar_z[ - nonslip_layer - 1 - ] * nu_bar_tz[nonslip_layer - 1] * ( - rad_row_helper @ np.ascontiguousarray(m_tot[:, :, nonslip_layer - 1]) - ) - for kk in range(nonslip_layer, nlayers): - rad_row_helper[0, :] = [rad[kk] ** 2, 1e0, 0e0, 0e0, 0e0] - v_force_row[:, :] = v_force_row + 2e0 * np.pi * ( - ey_bar_z[kk - 1] * nu_bar_tz[kk - 1] - ey_bar_z[kk] * nu_bar_tz[kk] - ) * (rad_row_helper @ np.ascontiguousarray(m_tot[:, :, kk])) - - # Include the effect of axial stiffness - v_force_row[0, 2] += ey_bar_z_area - - # Axial stiffness inner product, for layers which DON'T carry force - if nonslip_layer > 1: - rad_row_helper[0, :] = [rad[nonslip_layer - 1] ** 2, 1e0, 0e0, 0e0, 0e0] - v_force_row_slip[:, :] = ( - 2e0 - * np.pi - * ey_bar_z[nonslip_layer - 2] - * nu_bar_tz[nonslip_layer - 2] - * (rad_row_helper @ np.ascontiguousarray(m_tot[:, :, nonslip_layer - 1])) - ) - rad_row_helper[0, :] = [rad[0] ** 2, 1e0, 0e0, 0e0, 0e0] - v_force_row_slip[:, :] -= ( - 2e0 - * np.pi - * ey_bar_z[0] - * nu_bar_tz[0] - * (rad_row_helper @ np.ascontiguousarray(m_tot[:, :, 0])) - ) - for kk in range(1, nonslip_layer - 1): - rad_row_helper[0, :] = [rad[kk] ** 2, 1e0, 0e0, 0e0, 0e0] - v_force_row_slip[:, :] += ( - 2e0 - * np.pi - * (ey_bar_z[kk - 1] * nu_bar_tz[kk - 1] - ey_bar_z[kk] * nu_bar_tz[kk]) - * (rad_row_helper @ np.ascontiguousarray(m_tot[:, :, kk])) - ) - # Include the effect of axial stiffness - v_force_row_slip[0, 4] += ey_bar_z_area_slip - else: - # If there's no inner slip layer, still need a finite 5th - # element to ensure no singular matrix - v_force_row_slip[0, :] = [0e0, 0e0, 0e0, 0e0, 1e0] - - # Boundary condition matrix. Multiply this with the - # outermost solution vector, (A,B,eps_z,1.0,eps_z_slip), - # to obtain a zero vector. - # Solved to get the Lame parameters. - # Section 9 in the writeup - # *** - # Outer boundary condition row, zero radial stress - m_bc[0, :] = [ - (1e0 + nu_bar_t[nlayers - 1]) * rad[nlayers] ** 2, - -1e0 + nu_bar_t[nlayers - 1], - nu_bar_zt[nlayers - 1] * rad[nlayers] ** 2, - 0e0, - 0e0, - ] - # Inner boundary condition row, zero radial stress - # or zero displacement if rad(1)=0 - if nonslip_layer > 1: - m_bc[1, :] = [ - (1e0 + nu_bar_t[0]) * rad[0] ** 2, - -1e0 + nu_bar_t[0], - 0e0, - 0e0, - nu_bar_zt[0] * rad[0] ** 2, - ] - else: - m_bc[1, :] = [ - (1e0 + nu_bar_t[0]) * rad[0] ** 2, - -1e0 + nu_bar_t[0], - nu_bar_zt[0] * rad[0] ** 2, - 0e0, - 0e0, - ] - - m_bc[1, :] = np.ascontiguousarray(m_bc[1, :]) @ np.ascontiguousarray(m_tot[:, :, 0]) - # Axial force boundary condition - m_bc[2, :] = v_force_row[0, :] - m_bc[2, 3] = m_bc[2, 3] - v_force - # Axial force boundary condition of slip layers - m_bc[3, :] = v_force_row_slip[0, :] - - # The solution, the outermost Lame parameters A,B - # and the axial strains of the force-carrying and - # slip layers eps_z and eps_z_slip. - # Section 10 in the writeup - # *** - m_toinv[:, :3] = m_bc[ - :, - :3, - ] - m_toinv[:, 3] = m_bc[:, 4] - RHS_vec[:] = -m_bc[:, 3] - - a_vec_solution[:4] = np.linalg.solve(m_toinv, RHS_vec) - - a_vec_solution[4] = a_vec_solution[3] - a_vec_solution[3] = 1 - - # Radial/toroidal/vertical stress radial distribution - # ------ - # Radial displacement, stress and strain distributions - - a_vec_layer[:] = a_vec_solution[:] - for ii in range(nlayers - 1, -1, -1): - a_layer = a_vec_layer[0] - b_layer = a_vec_layer[1] - - dradius = (rad[ii + 1] - rad[ii]) / (n_radial_array - 1) - - for jj in range(ii * n_radial_array, (ii + 1) * n_radial_array): - rradius[jj] = rad[ii] + dradius * (jj - (n_radial_array * ii)) - - f_int_a_plot = 0.5e0 * f_lin_fac[ii] * ( - rad[ii + 1] ** 2 - rradius[jj] ** 2 - ) + f_rec_fac[ii] * np.log(rad[ii + 1] / (rradius[jj])) - f_int_b_plot = 0.25e0 * f_lin_fac[ii] * ( - rad[ii + 1] ** 4 - rradius[jj] ** 4 - ) + 0.5e0 * f_rec_fac[ii] * (rad[ii + 1] ** 2 - rradius[jj] ** 2) - a_plot = a_layer - 0.5e0 / ey_bar_t[ii] * f_int_a_plot - b_plot = b_layer + 0.5e0 / ey_bar_t[ii] * f_int_b_plot - - # Radial displacement - r_deflect[jj] = a_plot * rradius[jj] + b_plot / rradius[jj] - - # Radial strain - str_r[jj] = a_plot - b_plot / rradius[jj] ** 2 - # Azimuthal strain - str_t[jj] = a_plot + b_plot / rradius[jj] ** 2 - # Axial strain - if ii < nonslip_layer - 1: - str_z[jj] = a_vec_solution[4] - else: - str_z[jj] = a_vec_solution[2] - - # Radial stress - sigr[jj] = ey_bar_t[ii] * ( - str_r[jj] + (nu_bar_t[ii] * str_t[jj]) + (nu_bar_zt[ii] * str_z[jj]) - ) - # Aximuthal stress - sigt[jj] = ey_bar_t[ii] * ( - str_t[jj] + (nu_bar_t[ii] * str_r[jj]) + (nu_bar_zt[ii] * str_z[jj]) - ) - # Axial stress - sigz[jj] = ey_bar_z[ii] * ( - str_z[jj] + (nu_bar_tz[ii] * (str_r[jj] + str_t[jj])) - ) - - a_vec_layer = np.ascontiguousarray(m_tot[:, :, ii]) @ a_vec_solution - a_vec_layer = np.ascontiguousarray(m_ext[:, :, ii]) @ a_vec_layer - # ------ - - return rradius, sigr, sigt, sigz, str_r, str_t, str_z, r_deflect - - -@numba.njit(cache=True) -def plane_stress(nu, rad, ey, j, nlayers, n_radial_array): - """Calculates the stresses in a superconductor TF coil - inboard leg at the midplane using the plain stress approximation - author: P J Knight, CCFE, Culham Science Centre - author: J Morris, CCFE, Culham Science Centre - author: S Kahn, CCFE, Culham Science Centre - This routine calculates the stresses in a superconductor TF coil - inboard leg at midplane. -

A 2 layer plane stress model developed by CCFE is used. The first layer - is the steel case inboard of the winding pack, and the second - layer is the winding pack itself. - PROCESS Superconducting TF Coil Model, J. Morris, CCFE, 1st May 2014 - """ - alpha = np.zeros((nlayers,)) - beta = np.zeros((nlayers,)) - # Lorentz body force parametres - - area = np.zeros((nlayers,)) - # Layer area - - aa = np.zeros(( - 2 * nlayers, - 2 * nlayers, - )) - # Matrix encoding the integration constant cc coeficients - - bb = np.zeros((2 * nlayers,)) - # Vector encoding the alpha/beta (lorentz forces) contribution - - cc = np.zeros((2 * nlayers,)) - c1 = np.zeros((nlayers,)) - c2 = np.zeros((nlayers,)) - # Integration constants vector (solution) - - rradius = np.zeros((nlayers * n_radial_array,)) - # Radius array [m] - - sigr = np.zeros((nlayers * n_radial_array,)) - # Radial stress radial distribution [Pa] - - sigt = np.zeros((nlayers * n_radial_array,)) - # Toroidal stress radial distribution [Pa] - - r_deflect = np.zeros((nlayers * n_radial_array,)) - # Radial deflection (displacement) radial distribution [m] - - kk = ey / (1 - nu**2) - - # Lorentz forces parametrisation coeficients (array equation) - alpha = 0.5e0 * RMU0 * j**2 / kk - - inner_layer_curr = 0.0e0 - for ii in range(nlayers): - beta[ii] = ( - 0.5e0 - * RMU0 - * j[ii] - * (inner_layer_curr - np.pi * j[ii] * rad[ii] ** 2) - / (np.pi * kk[ii]) - ) - - # Layer area - area[ii] = np.pi * (rad[ii + 1] ** 2 - rad[ii] ** 2) - - # Total current carried by the inners layers - inner_layer_curr = inner_layer_curr + area[ii] * j[ii] - # *** - - # Null radial stress at R(1) - aa[0, 0] = kk[0] * (1.0e0 + nu[0]) - aa[0, 1] = -kk[0] * (1.0e0 - nu[0]) / (rad[0] ** 2) - - # Inter-layer boundary conditions - if nlayers != 1: - for ii in range(nlayers - 1): - # Continuous radial normal stress at R(ii+1) - aa[2 * ii + 1, 2 * ii] = kk[ii] * (1.0e0 + nu[ii]) - aa[2 * ii + 1, 2 * ii + 1] = -kk[ii] * (1.0e0 - nu[ii]) / rad[ii + 1] ** 2 - aa[2 * ii + 1, 2 * ii + 2] = -kk[ii + 1] * (1.0e0 + nu[ii + 1]) - aa[2 * ii + 1, 2 * ii + 3] = ( - kk[ii + 1] * (1.0e0 - nu[ii + 1]) / rad[ii + 1] ** 2 - ) - - # Continuous displacement at R(ii+1) - aa[2 * ii + 2, 2 * ii] = rad[ii + 1] - aa[2 * ii + 2, 2 * ii + 1] = 1.0e0 / rad[ii + 1] - aa[2 * ii + 2, 2 * ii + 2] = -rad[ii + 1] - aa[2 * ii + 2, 2 * ii + 3] = -1.0e0 / rad[ii + 1] - - # Radial stress = 0 - aa[2 * (nlayers - 1) + 1, 2 * (nlayers - 1)] = kk[nlayers - 1] * ( - 1.0e0 + nu[nlayers - 1] - ) - aa[2 * (nlayers - 1) + 1, 2 * (nlayers - 1) + 1] = ( - -kk[nlayers - 1] * (1.0e0 - nu[nlayers - 1]) / rad[nlayers] ** 2 - ) - # *** - - # Right hand side vector bb - # *** - # Null radial stress at R(1) - bb[0] = -kk[0] * ( - 0.125e0 * alpha[0] * (3.0e0 + nu[0]) * rad[0] ** 2 - + 0.5e0 * beta[0] * (1.0e0 + (1.0e0 + nu[0]) * np.log(rad[0])) - ) - - # Inter-layer boundary conditions - if nlayers != 1: - for ii in range(nlayers - 1): - # Continuous radial normal stress at R[ii+1] - bb[2 * ii + 1] = -kk[ii] * ( - 0.125e0 * alpha[ii] * (3.0e0 + nu[ii]) * rad[ii + 1] ** 2 - + 0.5e0 * beta[ii] * (1.0e0 + (1.0e0 + nu[ii]) * np.log(rad[ii + 1])) - ) + kk[ii + 1] * ( - 0.125e0 * alpha[ii + 1] * (3.0e0 + nu[ii + 1]) * rad[ii + 1] ** 2 - + 0.5e0 - * beta[ii + 1] - * (1.0e0 + (1.0e0 + nu[ii + 1]) * np.log(rad[ii + 1])) - ) - - # Continuous displacement at R(ii+1) - bb[2 * ii + 2] = ( - -0.125e0 * alpha[ii] * rad[ii + 1] ** 3 - - 0.5e0 * beta[ii] * rad[ii + 1] * np.log(rad[ii + 1]) - + 0.125e0 * alpha[ii + 1] * rad[ii + 1] ** 3 - + 0.5e0 * beta[ii + 1] * rad[ii + 1] * np.log(rad[ii + 1]) - ) - - # Null radial stress at R(nlayers+1) - bb[2 * (nlayers - 1) + 1] = -kk[nlayers - 1] * ( - 0.125e0 * alpha[nlayers - 1] * (3.0e0 + nu[nlayers - 1]) * rad[nlayers] ** 2 - + 0.5e0 - * beta[nlayers - 1] - * (1.0e0 + (1.0e0 + nu[nlayers - 1]) * np.log(rad[nlayers])) - ) - # *** - - # Find solution vector cc - # *** - aa = np.asfortranarray(aa) - cc = np.linalg.solve(aa, bb) - - # Multiply c by (-1) (John Last, internal CCFE memorandum, 21/05/2013) - for ii in range(nlayers): - c1[ii] = cc[2 * ii] - c2[ii] = cc[2 * ii + 1] - # *** - # ------ - - # Radial/toroidal/vertical stress radial distribution - # ------ - - for ii in range(nlayers): - dradius = (rad[ii + 1] - rad[ii]) / n_radial_array - for jj in range(ii * n_radial_array, (ii + 1) * n_radial_array): - rad_c = rad[ii] + dradius * (jj - n_radial_array * ii) - rradius[jj] = rad_c - - # Radial stress radial distribution [Pa] - sigr[jj] = kk[ii] * ( - (1.0e0 + nu[ii]) * c1[ii] - - ((1.0e0 - nu[ii]) * c2[ii]) / rad_c**2 - + 0.125e0 * (3.0e0 + nu[ii]) * alpha[ii] * rad_c**2 - + 0.5e0 * beta[ii] * (1.0e0 + (1.0e0 + nu[ii]) * np.log(rad_c)) - ) - - # Radial stress radial distribution [Pa] - sigt[jj] = kk[ii] * ( - (1.0e0 + nu[ii]) * c1[ii] - + (1.0e0 - nu[ii]) * c2[ii] / rad_c**2 - + 0.125e0 * (1.0e0 + 3.0e0 * nu[ii]) * alpha[ii] * rad_c**2 - + 0.5e0 * beta[ii] * (nu[ii] + (1.0e0 + nu[ii]) * np.log(rad_c)) - ) - - # Deflection [m] - r_deflect[jj] = ( - c1[ii] * rad_c - + c2[ii] / rad_c - + 0.125e0 * alpha[ii] * rad_c**3 - + 0.5e0 * beta[ii] * rad_c * np.log(rad_c) - ) - - return sigr, sigt, r_deflect, rradius - - -@numba.njit(cache=True) -def eyoung_parallel_array(n, eyoung_j_in, a_in, poisson_j_perp_in): - """ - Author : C. Swanson, PPPL - January 2022 - See Issue #1205 for derivation PDF - This subroutine gives the smeared elastic properties of two - members that are carrying a force in parallel with each other. - The force goes in direction j. - Members 1 and 2 are the individual members to be smeared. - Member 3 is the effective smeared member (output). - This is pretty easy because the smeared properties are simply - the average weighted by the cross-sectional areas perpendicular - to j. - The assumption is that the strains in j are equal. - If you're dealing with anisotropy, please pay attention to the - fact that the specific Young's Modulus used here is that in - the j direction, and the specific Poisson's ratio used here is - that between the j and transverse directions in that order. - (transverse strain / j strain, under j stress) - The smeared Poisson's ratio is computed assuming the transverse - dynamics are isotropic, and that the two members are free to - shrink/expand under Poisson effects without interference from - each other. This may not be true of your case. - - To build up a composite smeared member of any number of - individual members, you can pass the same properties for - members 2 and 3, and call it successively, using the properties - of each member as the first triplet of arguments. This way, the - last triplet acts as a "working sum": - call eyoung_parallel(triplet1, triplet2, tripletOUT) - call eyoung_parallel(triplet3, tripletOUT, tripletOUT) - call eyoung_parallel(triplet4, tripletOUT, tripletOUT) - ... etc. - So that tripletOUT would eventually have the smeared properties - of the total composite member. - """ - eyoung_j_out = 0 - a_out = 0 - poisson_j_perp_out = 0 - - # Parallel-composite them all together - for ii in range(n): - eyoung_j_out, a_out, poisson_j_perp_out = eyoung_parallel( - eyoung_j_in[ii], - a_in[ii], - poisson_j_perp_in[ii], - eyoung_j_out, - a_out, - poisson_j_perp_out, - ) - - return eyoung_j_out, a_out, poisson_j_perp_out - - -def _inductance_factor( - H: float, ri: float, ro: float, rm: float, theta1: float -) -> float: - """Calculates the inductance factor for a toroidal structure - using surrogate 2 #1866. - - Author: Timothy Nunn, UKAEA - - :param H: the maximum height of the structure - :param ri: the radius of the inboard side of the structure CCL - :param ro: the radius of the outboard side of the structure CCL - :param rm: the radius at which `H` occurs - :param theta1: the polar angle of the point at which one circular arc is - joined to another circular arc in the approximation to the structure CCL, - using an arbitrary origin of coordinates (Rc2, Zc2). - """ - # NOTE: the following properties are not those of the plasma but of - # the VV/coil structures - major_radius = (ro + ri) / 2 - minor_radius = (ro - ri) / 2 - - aspect_ratio = major_radius / minor_radius - triangularity = (major_radius - rm) / minor_radius - elongation = H / minor_radius - - return ( - 4.933 - + 0.03728 * elongation - + 0.06980 * triangularity - - 3.551 * aspect_ratio - + 0.7629 * aspect_ratio**2 - - 0.06298 * (theta1 / 90) - ) - - -@staticmethod -def lambda_term(tau: float, omega: float) -> float: - """ - The lambda function used inegral in inductance calcuation found - in Y. Itoh et al. The full form of the functions are given in appendix A. - - Author: Alexander Pearce, UKAEA - - :param tau: tau_{s,k} = (R_{s,k} - R_{c,k}) / R_k - :param omega: omega_k = R_{c,k}/R_k - :returns: integral - """ - p = 1.0 - omega**2.0 - - if p < 0: - integral = (1.0 / np.sqrt(np.abs(p))) * np.arcsin( - (1.0 + omega * tau) / (tau + omega) - ) - else: - integral = (1.0 / np.sqrt(np.abs(p))) * np.log( - (2.0 * (1.0 + tau * omega - np.sqrt(p * (1 - tau**2.0)))) / (tau + omega) - ) - - return integral - - -@staticmethod -def _theta_factor_integral( - ro_vv: float, ri_vv: float, rm_vv: float, h_vv: float, theta1_vv: float -) -> float: - """ - The calcuation of the theta factor found in Eq 4 of Y. Itoh et al. The - full form of the integral is given in appendix A. - - Author: Alexander Pearce, UKAEA - - :param ro_vv: the radius of the outboard edge of the VV CCL - :param ri_vv: the radius of the inboard edge of the VV CCL - :param rm_vv: the radius where the maximum height of the VV CCL occurs - :param h_vv: the maximum height of the VV CCL - :param theta1_vv: the polar angle of the point at which one circular arc is - joined to another circular arc in the approximation to the VV CCL - :returns: theta factor. - """ - theta2 = np.pi / 2.0 + theta1_vv - a = (ro_vv - ri_vv) / 2.0 - rbar = (ro_vv + ri_vv) / 2.0 - delta = (rbar - rm_vv) / a - kappa = h_vv / a - iota = (1.0 + delta) / kappa - - denom = np.cos(theta1_vv) + np.sin(theta1_vv) - 1.0 - - r1 = h_vv * ((np.cos(theta1_vv) + iota * (np.sin(theta1_vv) - 1.0)) / denom) - r2 = h_vv * ((np.cos(theta1_vv) - 1.0 + iota * np.sin(theta1_vv)) / denom) - r3 = h_vv * (1 - delta) / kappa - - rc1 = (h_vv / kappa) * ((rbar / a) + 1.0) - r1 - rc2 = rc1 + (r1 - r2) * np.cos(theta1_vv) - rc3 = rc2 - zc2 = (r1 - r2) * np.sin(theta1_vv) - zc3 = zc2 + r2 - r3 - - tau = np.array([ - [np.cos(theta1_vv), np.cos(theta1_vv + theta2), -1.0], - [1.0, np.cos(theta1_vv), np.cos(theta1_vv + theta2)], - ]) - - omega = np.array([rc1 / r1, rc2 / r2, rc3 / r3]) - - # Assume up down symmetry and let Zc6 = - Zc3 - chi1 = (zc3 + np.abs(-zc3)) / ri_vv - chi2 = 0.0 - - for k in range(len(omega)): - chi2 = chi2 + np.abs( - lambda_term(tau[1, k], omega[k]) - lambda_term(tau[0, k], omega[k]) - ) - - return (chi1 + 2.0 * chi2) / (2.0 * np.pi) - - -def vv_stress_on_quench( - # TF shape - H_coil: float, - ri_coil: float, - ro_coil: float, - rm_coil: float, - ccl_length_coil: float, - theta1_coil: float, - # VV shape - H_vv: float, - ri_vv: float, - ro_vv: float, - rm_vv: float, - theta1_vv: float, - # TF properties - n_tf_coils: float, - n_tf_turn: float, - s_rp: float, - s_cc: float, - taud: float, - i_op: float, - # VV properties - d_vv: float, -) -> float: - """Generic model to calculate the Tresca stress of the - Vacuum Vessel (VV), experienced when the TF coil quenches. - - Author: Timothy Nunn, UKAEA - - The current center line (CCL) of a structure is an appoximation - of where the poloidal current of a structure acts. This model - considers how the current (self-)induced in the following structures - affects the Tresca stress on the VV (corresponding to the structure - index used in [1]): - - 0. the TF coil conductors - 1. the TF coil steel structure - 2. the VV - - :param H_coil: the maximum height of the TF coil CCL - :param ri_coil: the radius of the inboard edge of the TF coil CCL - :param ro_coil: the radius of the outboard edge of the TF coil CCL - :param rm_coil: the radius where the maximum height of the TF coil CCL occurs - :param ccl_length_coil: the length of the TF coil CCL - :param theta1_coil: the polar angle of the point at which one circular arc is - joined to another circular arc in the approximation to the coil CCL, - using an arbitrary origin of coordinates (Rc2, Zc2). - - :param H_vv: the maximum height of the VV CCL - :param ri_vv: the radius of the inboard edge of the VV CCL - :param ro_vv: the radius of the outboard edge of the VV CCL - :param rm_vv: the radius where the maximum height of the VV CCL occurs - :param theta1_vv: the polar angle of the point at which one circular arc is - joined to another circular arc in the approximation to the VV CCL, - using an arbitrary origin of coordinates (Rc2, Zc2). - - :param n_tf_coils: the number of TF coils - :param n_tf_turn: the number of turns per TF coil - :param s_rp: the cross-sectional area of the radial plates of the TF coil - :param s_cc: the cross-sectional area of the TF coil case - :param taud: the discharge time of the TF coil when quench occurs - :param i_op: the 'normal' operating current of the TF coil - - :param d_vv: the thickness of the vacuum vessel shell - - :returns: the maximum stress experienced by the vacuum vessel - - Notes - ----- - The theta1 quantity for the TF coil and VV is not very meaningful. The - impact of it of the inductance is rather small. Generally, the paper seems to - suggest the TF coil is between 40 and 60, as this is the range they calculate - the surrogates over. The thickness of the VV considers an ITER like design and - only the outer and inner shells that which act of conductuve structural material. - - References - ---------- - 1. ITOH, Yasuyuki & Utoh, Hiroyasu & SAKAMOTO, Yoshiteru & Hiwatari, Ryoji. (2020). - Empirical Formulas for Estimating Self and Mutual Inductances of Toroidal Field Coils and Structures. - Plasma and Fusion Research. 15. 1405078-1405078. 10.1585/pfr.15.1405078. - """ - # Convert angles into radians - theta1_vv_rad = np.pi * (theta1_vv / 180.0) - - # Poloidal loop resistance (PLR) in ohms - theta_vv = _theta_factor_integral(ro_vv, ri_vv, rm_vv, H_vv, theta1_vv_rad) - plr_coil = ((0.5 * ccl_length_coil) / (n_tf_coils * (s_cc + s_rp))) * 1e-6 - plr_vv = ((0.84 / d_vv) * theta_vv) * 1e-6 - - # relevant self-inductances in henry (H) - coil_structure_self_inductance = ( - (constants.rmu0 / np.pi) - * H_coil - * _inductance_factor(H_coil, ri_coil, ro_coil, rm_coil, theta1_coil) - ) - vv_self_inductance = ( - (constants.rmu0 / np.pi) - * H_vv - * _inductance_factor(H_vv, ri_vv, ro_vv, rm_vv, theta1_vv) - ) - - # s^-1 - lambda0 = 1 / taud - lambda1 = (plr_coil) / coil_structure_self_inductance - lambda2 = (plr_vv) / vv_self_inductance - - # approximate time at which the maximum force (and stress) will occur on the VV - tmaxforce = np.log((lambda0 + lambda1) / (2 * lambda0)) / (lambda1 - lambda0) - - i0 = i_op * np.exp(-lambda0 * tmaxforce) - i1 = ( - lambda0 - * n_tf_coils - * n_tf_turn - * i_op - * ( - (np.exp(-lambda1 * tmaxforce) - np.exp(-lambda0 * tmaxforce)) - / (lambda0 - lambda1) - ) - ) - i2 = (lambda1 / lambda2) * i1 - - a_vv = (ro_vv + ri_vv) / (ro_vv - ri_vv) - b_vvi = (constants.rmu0 * (n_tf_coils * n_tf_turn * i0 + i1 + (i2 / 2))) / ( - 2 * np.pi * ri_vv - ) - j_vvi = i2 / (2 * np.pi * d_vv * ri_vv) - - zeta = 1 + ((a_vv - 1) * np.log((a_vv + 1) / (a_vv - 1)) / (2 * a_vv)) - - return zeta * b_vvi * j_vvi * ri_vv diff --git a/process/superconducting_tf_coil.py b/process/superconducting_tf_coil.py new file mode 100644 index 0000000000..58ec92fbca --- /dev/null +++ b/process/superconducting_tf_coil.py @@ -0,0 +1,3831 @@ +import copy +import logging + +import numpy as np +from scipy import optimize + +import process.superconductors as superconductors +from process import process_output as po +from process.fortran import ( + build_variables, + constants, + divertor_variables, + error_handling, + global_variables, + numerics, + pfcoil_variables, + physics_variables, + rebco_variables, + sctfcoil_module, + tfcoil_variables, +) +from process.tf_coil import TFCoil +from process.utilities.f2py_string_patch import f2py_compatible_to_string + +logger = logging.getLogger(__name__) + +SUPERCONDUCTING_TF_TYPES = { + 1: "Nb3Sn ITER", + 2: "Bi-2212", + 3: "NbTi", + 4: "Nb3Sn user", + 5: "Nb3Sn WST", + 6: "REBCO Croco", + 7: "NbTi Ginzburg-Landau", + 8: "REBCO Ginzburg-Landau", + 9: "REBCO Hazelton-Zhai", +} + + +RMU0 = constants.rmu0 +EPS = np.finfo(1.0).eps + + +class SuperconductingTFCoil(TFCoil): + def __init__(self): + self.outfile = constants.nout + + def run(self, output: bool): + """ + Routine to call the superconductor module for the TF coils + """ + self.iprint = 0 + self.tf_global_geometry() + self.tf_current() + self.coilshap() + self.sc_tf_internal_geom( + tfcoil_variables.i_tf_wp_geom, + tfcoil_variables.i_tf_case_geom, + tfcoil_variables.i_tf_turns_integer, + ) + + if physics_variables.itart == 0 and tfcoil_variables.i_tf_shape == 1: + tfcoil_variables.tfind = self.tfcind( + build_variables.dr_tf_inboard, + tfcoil_variables.xarc, + tfcoil_variables.yarc, + ) + else: + tfcoil_variables.tfind = ( + (build_variables.hmax + build_variables.dr_tf_outboard) + * RMU0 + / constants.pi + * np.log( + build_variables.r_tf_outboard_mid / build_variables.r_tf_inboard_mid + ) + ) + + # Total TF coil stored magnetic energy [J] + sctfcoil_module.estotft = ( + 0.5e0 * tfcoil_variables.tfind * tfcoil_variables.c_tf_total**2 + ) + + # Total TF coil stored magnetic energy [Gigajoule] + tfcoil_variables.estotftgj = 1.0e-9 * sctfcoil_module.estotft + + self.tf_field_and_force() + + # Calculate TF coil areas and masses + self.tf_coil_area_and_masses() + + # Do stress calculations (writes the stress output) + if output: + tfcoil_variables.n_rad_per_layer = 500 + + try: + ( + sig_tf_r_max, + sig_tf_t_max, + sig_tf_z_max, + sig_tf_vmises_max, + s_shear_tf_peak, + deflect, + eyoung_axial, + eyoung_trans, + eyoung_wp_axial, + eyoung_wp_trans, + poisson_wp_trans, + radial_array, + s_shear_cea_tf_cond, + poisson_wp_axial, + sig_tf_r, + sig_tf_smeared_r, + sig_tf_smeared_t, + sig_tf_smeared_z, + sig_tf_t, + s_shear_tf, + sig_tf_vmises, + sig_tf_z, + str_tf_r, + str_tf_t, + str_tf_z, + n_radial_array, + n_tf_bucking, + tfcoil_variables.sig_tf_wp, + sig_tf_case, + sig_tf_cs_bucked, + str_wp, + casestr, + insstrain, + sig_tf_wp_av_z, + ) = self.stresscl( + int(tfcoil_variables.n_tf_stress_layers), + int(tfcoil_variables.n_rad_per_layer), + int(tfcoil_variables.n_tf_wp_layers), + int(tfcoil_variables.i_tf_bucking), + float(build_variables.r_tf_inboard_in), + build_variables.dr_bore, + build_variables.hmax, + pfcoil_variables.f_z_cs_tf_internal, + build_variables.dr_cs, + build_variables.i_tf_inside_cs, + build_variables.dr_tf_inboard, + build_variables.dr_cs_tf_gap, + pfcoil_variables.i_pf_conductor, + pfcoil_variables.j_cs_flat_top_end, + pfcoil_variables.j_cs_pulse_start, + pfcoil_variables.c_pf_coil_turn_peak_input, + pfcoil_variables.n_pf_coils_in_group, + pfcoil_variables.ld_ratio_cst, + pfcoil_variables.r_out_cst, + pfcoil_variables.f_a_cs_steel, + tfcoil_variables.eyoung_steel, + tfcoil_variables.poisson_steel, + tfcoil_variables.eyoung_cond_axial, + tfcoil_variables.poisson_cond_axial, + tfcoil_variables.eyoung_cond_trans, + tfcoil_variables.poisson_cond_trans, + tfcoil_variables.eyoung_ins, + tfcoil_variables.poisson_ins, + tfcoil_variables.thicndut, + tfcoil_variables.eyoung_copper, + tfcoil_variables.poisson_copper, + tfcoil_variables.i_tf_sup, + tfcoil_variables.eyoung_res_tf_buck, + sctfcoil_module.r_wp_inner, + sctfcoil_module.tan_theta_coil, + sctfcoil_module.theta_coil, + sctfcoil_module.r_wp_outer, + sctfcoil_module.a_tf_steel, + sctfcoil_module.a_case_front, + sctfcoil_module.a_case_nose, + tfcoil_variables.tfinsgap, + tfcoil_variables.tinstf, + tfcoil_variables.n_tf_turn, + int(tfcoil_variables.i_tf_turns_integer), + sctfcoil_module.t_cable, + sctfcoil_module.t_cable_radial, + tfcoil_variables.dhecoil, + tfcoil_variables.fcutfsu, + tfcoil_variables.thwcndut, + sctfcoil_module.t_lat_case_av, + sctfcoil_module.t_wp_toroidal_av, + sctfcoil_module.a_tf_ins, + tfcoil_variables.aswp, + tfcoil_variables.acond, + sctfcoil_module.awpc, + tfcoil_variables.eyoung_al, + tfcoil_variables.poisson_al, + tfcoil_variables.fcoolcp, + tfcoil_variables.n_tf_graded_layers, + tfcoil_variables.c_tf_total, + tfcoil_variables.casthi, + tfcoil_variables.i_tf_stress_model, + sctfcoil_module.vforce_inboard_tot, + tfcoil_variables.i_tf_tresca, + tfcoil_variables.acasetf, + tfcoil_variables.vforce, + tfcoil_variables.acndttf, + ) + + tfcoil_variables.sig_tf_case = ( + tfcoil_variables.sig_tf_case + if tfcoil_variables.sig_tf_case is None + else sig_tf_case + ) + + tfcoil_variables.sig_tf_cs_bucked = ( + tfcoil_variables.sig_tf_cs_bucked + if tfcoil_variables.sig_tf_cs_bucked is None + else sig_tf_cs_bucked + ) + + tfcoil_variables.str_wp = ( + tfcoil_variables.str_wp if tfcoil_variables.str_wp is None else str_wp + ) + + tfcoil_variables.casestr = ( + tfcoil_variables.casestr + if tfcoil_variables.casestr is None + else casestr + ) + + tfcoil_variables.insstrain = ( + tfcoil_variables.insstrain + if tfcoil_variables.insstrain is None + else insstrain + ) + + if output: + self.out_stress( + sig_tf_r_max, + sig_tf_t_max, + sig_tf_z_max, + sig_tf_vmises_max, + s_shear_tf_peak, + deflect, + eyoung_axial, + eyoung_trans, + eyoung_wp_axial, + eyoung_wp_trans, + poisson_wp_trans, + radial_array, + s_shear_cea_tf_cond, + poisson_wp_axial, + sig_tf_r, + sig_tf_smeared_r, + sig_tf_smeared_t, + sig_tf_smeared_z, + sig_tf_t, + s_shear_tf, + sig_tf_vmises, + sig_tf_z, + str_tf_r, + str_tf_t, + str_tf_z, + n_radial_array, + n_tf_bucking, + sig_tf_wp_av_z, + ) + except ValueError as e: + if e.args[1] == 245 and e.args[2] == 0: + error_handling.report_error(245) + tfcoil_variables.sig_tf_case = 0.0e0 + tfcoil_variables.sig_tf_wp = 0.0e0 + peaktfflag = 0 + self.vv_stress_on_quench() + + # Peak field including ripple + # Rem : as resistive magnets are axisymmetric, no inboard ripple is present + tfcoil_variables.bmaxtfrp, peaktfflag = self.peak_tf_with_ripple( + tfcoil_variables.n_tf_coils, + tfcoil_variables.wwp1, + tfcoil_variables.dr_tf_wp + - 2.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.tfinsgap), + sctfcoil_module.r_wp_centre, + tfcoil_variables.bmaxtf, + ) + + tfes = sctfcoil_module.estotft / tfcoil_variables.n_tf_coils + # Cross-sectional area per turn + aturn = tfcoil_variables.c_tf_total / ( + tfcoil_variables.jwptf + * tfcoil_variables.n_tf_coils + * tfcoil_variables.n_tf_turn + ) + + if tfcoil_variables.i_tf_sc_mat == 6: + (tfcoil_variables.jwdgcrt, tfcoil_variables.tmargtf) = self.supercon_croco( + aturn, + tfcoil_variables.bmaxtfrp, + tfcoil_variables.cpttf, + tfcoil_variables.tftmp, + output=output, + ) + + tfcoil_variables.vtfskv = ( + self.croco_voltage() / 1.0e3 + ) # TFC Quench voltage in kV + + else: + ( + tfcoil_variables.jwdgcrt, + vdump, + tfcoil_variables.tmargtf, + ) = self.supercon( + tfcoil_variables.acstf, + aturn, + tfcoil_variables.bmaxtfrp, + tfcoil_variables.vftf, + tfcoil_variables.fcutfsu, + tfcoil_variables.cpttf, + tfcoil_variables.jwptf, + tfcoil_variables.i_tf_sc_mat, + tfcoil_variables.fhts, + tfcoil_variables.tdmptf, + tfes, + tfcoil_variables.tftmp, + tfcoil_variables.tmaxpro, + tfcoil_variables.bcritsc, + tfcoil_variables.tcritsc, + output=output, + ) + + tfcoil_variables.vtfskv = vdump / 1.0e3 # TFC Quench voltage in kV + + if output: + self.outtf(peaktfflag) + + def croco_voltage(self) -> float: + if f2py_compatible_to_string(tfcoil_variables.quench_model) == "linear": + sctfcoil_module.time2 = tfcoil_variables.tdmptf + croco_voltage = ( + 2.0e0 + / sctfcoil_module.time2 + * (sctfcoil_module.estotft / tfcoil_variables.n_tf_coils) + / tfcoil_variables.cpttf + ) + elif f2py_compatible_to_string(tfcoil_variables.quench_model) == "exponential": + sctfcoil_module.tau2 = tfcoil_variables.tdmptf + croco_voltage = ( + 2.0e0 + / sctfcoil_module.tau2 + * (sctfcoil_module.estotft / tfcoil_variables.n_tf_coils) + / tfcoil_variables.cpttf + ) + else: + return 0.0 + + return croco_voltage + + def supercon_croco(self, aturn, bmax, iop, thelium, output: bool): + """TF superconducting CroCo conductor using REBCO tape + author: M Kovari, CCFE, Culham Science Centre + bmax : input real : Peak field at conductor (T) + iop : input real : Operating current per turn (A) + thelium : input real : He temperature at peak field point (K) + iprint : input integer : Switch for printing (1 = yes, 0 = no) + outfile : input integer : Fortran output unit identifier + jwdgcrt : output real : Critical winding pack current density (A/m2) + tmarg : output real : Temperature margin (K) + """ + + j_crit_sc: float = 0.0 + # Find critical current density in superconducting cable, j_crit_cable + j_crit_sc, _ = superconductors.jcrit_rebco(thelium, bmax) + # tfcoil_variables.acstf : Cable space - inside area (m2) + # Set new rebco_variables.croco_od + # allowing for scaling of rebco_variables.croco_od + rebco_variables.croco_od = ( + tfcoil_variables.t_conductor / 3.0e0 + - tfcoil_variables.thwcndut * (2.0e0 / 3.0e0) + ) + sctfcoil_module.conductor_acs = ( + 9.0e0 / 4.0e0 * np.pi * rebco_variables.croco_od**2 + ) + tfcoil_variables.acstf = sctfcoil_module.conductor_acs + sctfcoil_module.conductor_area = ( + tfcoil_variables.t_conductor**2 + ) # does this not assume it's a sqaure??? + + sctfcoil_module.conductor_jacket_area = ( + sctfcoil_module.conductor_area - sctfcoil_module.conductor_acs + ) + tfcoil_variables.acndttf = sctfcoil_module.conductor_jacket_area + + sctfcoil_module.conductor_jacket_fraction = ( + sctfcoil_module.conductor_jacket_area / sctfcoil_module.conductor_area + ) + ( + sctfcoil_module.croco_strand_area, + sctfcoil_module.croco_strand_critical_current, + sctfcoil_module.conductor_copper_area, + sctfcoil_module.conductor_copper_fraction, + sctfcoil_module.conductor_copper_bar_area, + sctfcoil_module.conductor_hastelloy_area, + sctfcoil_module.conductor_hastelloy_fraction, + sctfcoil_module.conductor_helium_area, + sctfcoil_module.conductor_helium_fraction, + sctfcoil_module.conductor_solder_area, + sctfcoil_module.conductor_solder_fraction, + sctfcoil_module.conductor_rebco_area, + sctfcoil_module.conductor_rebco_fraction, + sctfcoil_module.conductor_critical_current, + ) = superconductors.croco( + j_crit_sc, + sctfcoil_module.conductor_area, + rebco_variables.croco_od, + rebco_variables.croco_thick, + ) + + rebco_variables.coppera_m2 = iop / sctfcoil_module.conductor_copper_area + + icrit = sctfcoil_module.conductor_critical_current + j_crit_cable = ( + sctfcoil_module.croco_strand_critical_current + / sctfcoil_module.croco_strand_area + ) + + # Critical current density in winding pack + # aturn : Area per turn (i.e. entire jacketed conductor with insulation) (m2) + jwdgcrt = icrit / aturn + # Ratio of operating / critical current + iooic = iop / icrit + # Operating current density + jwdgop = iop / aturn + # Actual current density in superconductor, + # which should be equal to jcrit(thelium+tmarg) + + # when we have found the desired value of tmarg + jsc = iooic * j_crit_sc + + # Temperature margin + current_sharing_t = superconductors.current_sharing_rebco(bmax, jsc) + tmarg = current_sharing_t - thelium + tfcoil_variables.temp_margin = ( + tmarg # Only used in the availabilty routine - see comment to Issue #526 + ) + + if output: # Output ---------------------------------- + total = ( + sctfcoil_module.conductor_copper_area + + sctfcoil_module.conductor_hastelloy_area + + sctfcoil_module.conductor_solder_area + + sctfcoil_module.conductor_jacket_area + + sctfcoil_module.conductor_helium_area + + sctfcoil_module.conductor_rebco_area + ) + + if tfcoil_variables.temp_margin <= 0.0e0: + logger.warning( + f"""Negative TFC temperature margin + temp_margin: {tfcoil_variables.temp_margin} + bmax: {bmax}""" + ) + + po.oheadr(self.outfile, "Superconducting TF Coils") + po.ovarin(self.outfile, "Superconductor switch", "(isumat)", 6) + po.ocmmnt( + self.outfile, "Superconductor used: REBCO HTS tape in CroCo strand" + ) + + po.ovarre( + self.outfile, + "Thickness of REBCO layer in tape (m)", + "(rebco_thickness)", + rebco_variables.rebco_thickness, + ) + po.ovarre( + self.outfile, + "Thickness of copper layer in tape (m)", + "(copper_thick )", + rebco_variables.copper_thick, + ) + po.ovarre( + self.outfile, + "Thickness of Hastelloy layer in tape (m) ", + "(hastelloy_thickness)", + rebco_variables.hastelloy_thickness, + ) + + po.ovarre( + self.outfile, + "Mean width of tape (m)", + "(tape_width)", + rebco_variables.tape_width, + "OP ", + ) + po.ovarre( + self.outfile, + "Outer diameter of CroCo copper tube (m) ", + "(croco_od)", + rebco_variables.croco_od, + "OP ", + ) + po.ovarre( + self.outfile, + "Inner diameter of CroCo copper tube (m) ", + "(croco_id)", + rebco_variables.croco_id, + "OP ", + ) + po.ovarre( + self.outfile, + "Thickness of CroCo copper tube (m) ", + "(croco_thick)", + rebco_variables.croco_thick, + ) + + po.ovarre( + self.outfile, + "Thickness of each HTS tape ", + "(tape_thickness)", + rebco_variables.tape_thickness, + "OP ", + ) + po.ovarre( + self.outfile, + "Thickness of stack of rebco_variables.tapes (m) ", + "(stack_thickness)", + rebco_variables.stack_thickness, + "OP ", + ) + po.ovarre( + self.outfile, + "Number of rebco_variables.tapes in strand", + "(tapes)", + rebco_variables.tapes, + "OP ", + ) + po.oblnkl(self.outfile) + po.ovarre( + self.outfile, + "Area of REBCO in strand (m2)", + "(rebco_area)", + rebco_variables.rebco_area, + "OP ", + ) + po.ovarre( + self.outfile, + "Area of copper in strand (m2)", + "(copper_area)", + rebco_variables.copper_area, + "OP ", + ) + po.ovarre( + self.outfile, + "Area of hastelloy substrate in strand (m2) ", + "(hastelloy_area)", + rebco_variables.hastelloy_area, + "OP ", + ) + po.ovarre( + self.outfile, + "Area of solder in strand (m2) ", + "(solder_area)", + rebco_variables.solder_area, + "OP ", + ) + po.ovarre( + self.outfile, + "Total: area of CroCo strand (m2) ", + "(croco_strand_area)", + sctfcoil_module.croco_strand_area, + "OP ", + ) + if ( + abs( + sctfcoil_module.croco_strand_area + - ( + rebco_variables.rebco_area + + rebco_variables.copper_area + + rebco_variables.hastelloy_area + + rebco_variables.solder_area + ) + ) + > 1e-6 + ): + po.ocmmnt(self.outfile, "ERROR: Areas in CroCo strand do not add up") + logger.warning("Areas in CroCo strand do not add up - see OUT.DAT") + + po.oblnkl(self.outfile) + po.ocmmnt(self.outfile, "Cable information") + po.ovarin( + self.outfile, + "Number of CroCo strands in the cable (fixed) ", + "", + 6, + "OP ", + ) + po.ovarre( + self.outfile, + "Total area of cable space (m2)", + "(acstf)", + tfcoil_variables.acstf, + "OP ", + ) + + po.oblnkl(self.outfile) + po.ocmmnt( + self.outfile, + "Conductor information (includes jacket, not including insulation)", + ) + po.ovarre( + self.outfile, + "Width of square conductor (cable + steel jacket) (m)", + "(t_conductor)", + tfcoil_variables.t_conductor, + "OP ", + ) + po.ovarre( + self.outfile, + "Area of conductor (m2)", + "(area)", + sctfcoil_module.conductor_area, + "OP ", + ) + po.ovarre( + self.outfile, + "REBCO area of conductor (mm2)", + "(rebco_area)", + sctfcoil_module.conductor_rebco_area, + "OP ", + ) + po.ovarre( + self.outfile, + "Area of central copper bar (mm2)", + "(copper_bar_area)", + sctfcoil_module.conductor_copper_bar_area, + "OP ", + ) + po.ovarre( + self.outfile, + "Total copper area of conductor, total (mm2)", + "(copper_area)", + sctfcoil_module.conductor_copper_area, + "OP ", + ) + po.ovarre( + self.outfile, + "Hastelloy area of conductor (mm2)", + "(hastelloy_area)", + sctfcoil_module.conductor_hastelloy_area, + "OP ", + ) + po.ovarre( + self.outfile, + "Solder area of conductor (mm2)", + "(solder_area)", + sctfcoil_module.conductor_solder_area, + "OP ", + ) + po.ovarre( + self.outfile, + "Jacket area of conductor (mm2)", + "(jacket_area)", + sctfcoil_module.conductor_jacket_area, + "OP ", + ) + po.ovarre( + self.outfile, + "Helium area of conductor (mm2)", + "(helium_area)", + sctfcoil_module.conductor_helium_area, + "OP ", + ) + if abs(total - sctfcoil_module.conductor_area) > 1e-8: + po.ovarre( + self.outfile, + "ERROR: conductor areas do not add up:", + "(total)", + total, + "OP ", + ) + logger.warning(f"conductor areas do not add up. total: {total}") + + po.ovarre( + self.outfile, + "Critical current of CroCo strand (A)", + "(croco_strand_critical_current)", + sctfcoil_module.croco_strand_critical_current, + "OP ", + ) + po.ovarre( + self.outfile, + "Critical current of conductor (A) ", + "(conductor_critical_current)", + sctfcoil_module.conductor_critical_current, + "OP ", + ) + + if global_variables.run_tests == 1: + po.oblnkl(self.outfile) + po.ocmmnt( + self.outfile, + "PROCESS TF Coil peak field fit. Values for t, z and y:", + ) + po.oblnkl(self.outfile) + po.ovarre( + self.outfile, + "Dimensionless winding pack width", + "(tf_fit_t)", + sctfcoil_module.tf_fit_t, + "OP ", + ) + po.ovarre( + self.outfile, + "Dimensionless winding pack radial thickness", + "(tf_fit_z)", + sctfcoil_module.tf_fit_z, + "OP ", + ) + po.ovarre( + self.outfile, + "Ratio of actual peak field to nominal axisymmetric peak field", + "(tf_fit_y)", + sctfcoil_module.tf_fit_y, + "OP ", + ) + + po.oblnkl(self.outfile) + po.ovarre( + self.outfile, + "Helium temperature at peak field (= superconductor temperature) (K)", + "(thelium)", + thelium, + ) + po.ovarre( + self.outfile, + "Critical current density in superconductor (A/m2)", + "(j_crit_sc)", + j_crit_sc, + "OP ", + ) + po.ovarre( + self.outfile, + "Critical current density in cable (A/m2)", + "(j_crit_cable)", + j_crit_cable, + "OP ", + ) + po.ovarre( + self.outfile, + "Critical current density in winding pack (A/m2)", + "(jwdgcrt)", + jwdgcrt, + "OP ", + ) + po.ovarre( + self.outfile, + "Actual current density in winding pack (A/m2)", + "(jwdgop)", + jwdgop, + "OP ", + ) + + po.ovarre( + self.outfile, + "Minimum allowed temperature margin in superconductor (K)", + "(tmargmin_tf)", + tfcoil_variables.tmargmin_tf, + ) + + po.ovarre( + self.outfile, + "Actual temperature margin in superconductor (K)", + "(tmarg)", + tmarg, + "OP ", + ) + po.ovarre( + self.outfile, + "Current sharing temperature (K)", + "(current_sharing_t)", + current_sharing_t, + "OP ", + ) + po.ovarre(self.outfile, "Critical current (A)", "(icrit)", icrit, "OP ") + po.ovarre( + self.outfile, + "Actual current (A)", + "(cpttf)", + tfcoil_variables.cpttf, + "OP ", + ) + po.ovarre( + self.outfile, + "Actual current / critical current", + "(iooic)", + iooic, + "OP ", + ) + + return jwdgcrt, tmarg + + def supercon( + self, + acs, + aturn, + bmax, + fhe, + fcu, + iop, + jwp, + isumat, + fhts, + tdmptf, + tfes, + thelium, + tmax, + bcritsc, + tcritsc, + output: bool, + ): + """Routine to calculate the TF superconducting conductor properties + author: P J Knight, CCFE, Culham Science Centre + author: J Galambos, ORNL + author: R Kemp, CCFE, Culham Science Centre + author: M Kovari, CCFE, Culham Science Centre + author: J Miller, ORNL + acs : input real : Cable space - inside area (m2) + aturn : input real : Area per turn (i.e. entire jacketed conductor) (m2) + bmax : input real : Peak field at conductor (T) + fhe : input real : Fraction of cable space that is for He cooling + fcu : input real : Fraction of conductor that is copper + iop : input real : Operating current per turn (A) + jwp : input real : Actual winding pack current density (A/m2) + isumat : input integer : Switch for conductor type: + 1 = ITER Nb3Sn, standard parameters, + 2 = Bi-2212 High Temperature Superconductor, + 3 = NbTi, + 4 = ITER Nb3Sn, user-defined parameters + 5 = WST Nb3Sn parameterisation + 7 = Durham Ginzburg-Landau Nb-Ti parameterisation + 8 = Durham Ginzburg-Landau critical surface model for REBCO + 9 = Hazelton experimental data + Zhai conceptual model for REBCO + fhts : input real : Adjustment factor (<= 1) to account for strain, + radiation damage, fatigue or AC losses + tdmptf : input real : Dump time (sec) + tfes : input real : Energy stored in one TF coil (J) + thelium : input real : He temperature at peak field point (K) + tmax : input real : Max conductor temperature during quench (K) + bcritsc : input real : Critical field at zero temperature and strain (T) (isumat=4 only) + tcritsc : input real : Critical temperature at zero field and strain (K) (isumat=4 only) + iprint : input integer : Switch for printing (1 = yes, 0 = no) + outfile : input integer : Fortran output unit identifier + jwdgpro : output real : Winding pack current density from temperature + rise protection (A/m2) + jwdgcrt : output real : Critical winding pack current density (A/m2) + vd : output real : Discharge voltage imposed on a TF coil (V) + tmarg : output real : Temperature margin (K) + This routine calculates the superconductor properties for the TF coils. + It was originally programmed by J. Galambos 1991, from algorithms provided + by J. Miller. +

The routine calculates the critical current density (winding pack) + and also the protection information (for a quench). + NOT used for the Croco conductor + + N.B. critical current density for a super conductor (j_crit_sc) + is for the superconducting strands/tape, not including copper. + Critical current density for a cable (j_crit_cable) acounts for + both the fraction of the cable taken up by helium coolant channels, + and the cable conductor copper fraction - i.e., the copper in the + superconducting strands AND any addtional copper, such as REBCO + tape support. + """ + tdump = tdmptf + + # Helium channel + fhetot = ( + fhe + + (np.pi / 4.0e0) + * tfcoil_variables.dhecoil + * tfcoil_variables.dhecoil + / acs + ) + + # Guard against negative conductor fraction fcond + # Kludge to allow solver to continue and hopefully be constrained away + # from this point + if fhetot > 0.99: + fhetot = 0.99 + + # Conductor fraction (including central helium channel) + fcond = 1.0e0 - fhetot + + if tfcoil_variables.i_str_wp == 0: + strain = tfcoil_variables.str_tf_con_res + else: + strain = tfcoil_variables.str_wp + + # Find critical current density in the superconducter (j_crit_sc) + # and the superconducting cable (j_crit_cable) + if isumat == 1: # ITER Nb3Sn critical surface parameterization + bc20m = 32.97e0 + tc0m = 16.06e0 + # If strain limit achieved, throw a warning and use the lower strain + if abs(strain) > 0.5e-2: + error_handling.fdiags[0] = strain + error_handling.report_error(261) + strain = np.sign(strain) * 0.5e-2 + + # j_crit_sc returned by superconductors.itersc is the critical current density in the + # superconductor - not the whole strand, which contains copper + j_crit_sc, _, _ = superconductors.itersc(thelium, bmax, strain, bc20m, tc0m) + # j_crit_cable = j_crit_sc * non-copper fraction of conductor * conductor fraction of cable + j_crit_cable = j_crit_sc * (1.0e0 - fcu) * fcond + # Critical current in cable + icrit = j_crit_cable * acs + + # Strand critical current calculation for costing in $/kAm + # = Superconducting filaments jc * (1 - strand copper fraction) + tfcoil_variables.j_crit_str_tf = j_crit_sc * (1.0e0 - fcu) + + elif isumat == 2: # Bi-2212 high temperature superconductor parameterization + # Current density in a strand of Bi-2212 conductor + # N.B. jcrit returned by superconductors.bi2212 is the critical current density + # in the strand, not just the superconducting portion. + # The parameterization for j_crit_cable assumes a particular strand + # composition that does not require a user-defined copper fraction, + # so this is irrelevant in this model + jstrand = jwp * aturn / (acs * fcond) + + j_crit_cable, tmarg = superconductors.bi2212(bmax, jstrand, thelium, fhts) + j_crit_sc = j_crit_cable / (1.0e0 - fcu) + # Critical current in cable + icrit = j_crit_cable * acs * fcond + + # Strand critical current calulation for costing in $ / kAm + # Copper in the strand is already accounted for + tfcoil_variables.j_crit_str_tf = j_crit_sc + + elif isumat == 3: # NbTi data + bc20m = 15.0e0 + tc0m = 9.3e0 + c0 = 1.0e10 + j_crit_sc, _ = superconductors.jcrit_nbti(thelium, bmax, c0, bc20m, tc0m) + # j_crit_cable = j_crit_sc * non-copper fraction of conductor * conductor fraction of cable + j_crit_cable = j_crit_sc * (1.0e0 - fcu) * fcond + # Critical current in cable + icrit = j_crit_cable * acs + + # Strand critical current calulation for costing in $ / kAm + # = superconducting filaments jc * (1 -strand copper fraction) + tfcoil_variables.j_crit_str_tf = j_crit_sc * (1.0e0 - fcu) + + elif isumat == 4: # ITER Nb3Sn parameterization, but user-defined parameters + bc20m = bcritsc + tc0m = tcritsc + # If strain limit achieved, throw a warning and use the lower strain + if abs(strain) > 0.5e-2: + error_handling.fdiags[0] = strain + error_handling.report_error(261) + strain = np.sign(strain) * 0.5e-2 + + j_crit_sc, _, _ = superconductors.itersc(thelium, bmax, strain, bc20m, tc0m) + # j_crit_cable = j_crit_sc * non-copper fraction of conductor * conductor fraction of cable + j_crit_cable = j_crit_sc * (1.0e0 - fcu) * fcond + # Critical current in cable + icrit = j_crit_cable * acs + + # Strand critical current calulation for costing in $ / kAm + # = superconducting filaments jc * (1 -strand copper fraction) + tfcoil_variables.j_crit_str_tf = j_crit_sc * (1.0e0 - fcu) + + elif isumat == 5: # WST Nb3Sn parameterisation + bc20m = 32.97e0 + tc0m = 16.06e0 + # If strain limit achieved, throw a warning and use the lower strain + if abs(strain) > 0.5e-2: + error_handling.fdiags[0] = strain + error_handling.report_error(261) + strain = np.sign(strain) * 0.5e-2 + + # j_crit_sc returned by superconductors.itersc is the critical current density in the + # superconductor - not the whole strand, which contains copper + j_crit_sc, _, _ = superconductors.wstsc(thelium, bmax, strain, bc20m, tc0m) + # j_crit_cable = j_crit_sc * non-copper fraction of conductor * conductor fraction of cable + j_crit_cable = j_crit_sc * (1.0e0 - fcu) * fcond + # Critical current in cable + icrit = j_crit_cable * acs + + # Strand critical current calulation for costing in $ / kAm + # = superconducting filaments jc * (1 -strand copper fraction) + tfcoil_variables.j_crit_str_tf = j_crit_sc * (1.0e0 - fcu) + + elif isumat == 6: # "REBCO" 2nd generation HTS superconductor in CrCo strand + raise ValueError( + "sctfcoil.supercon has been called but tfcoil_variables.i_tf_sc_mat=6" + ) + + elif isumat == 7: # Durham Ginzburg-Landau Nb-Ti parameterisation + bc20m = tfcoil_variables.b_crit_upper_nbti + tc0m = tfcoil_variables.t_crit_nbti + j_crit_sc, _, _ = superconductors.gl_nbti( + thelium, bmax, strain, bc20m, tc0m + ) + # j_crit_cable = j_crit_sc * non-copper fraction of conductor * conductor fraction of cable + j_crit_cable = j_crit_sc * (1.0e0 - fcu) * fcond + # Critical current in cable + icrit = j_crit_cable * acs + + # Strand critical current calulation for costing in $ / kAm + # = superconducting filaments jc * (1 -strand copper fraction) + tfcoil_variables.j_crit_str_tf = j_crit_sc * (1.0e0 - fcu) + + elif isumat == 8: # Durham Ginzburg-Landau critical surface model for REBCO + bc20m = 430 + tc0m = 185 + # If strain limit achieved, throw a warning and use the lower strain + if abs(strain) > 0.7e-2: + error_handling.fdiags[0] = strain + error_handling.report_error(261) + strain = np.sign(strain) * 0.7e-2 + + j_crit_sc, _, _ = superconductors.gl_rebco( + thelium, bmax, strain, bc20m, tc0m + ) + # A0 calculated for tape cross section already + # j_crit_cable = j_crit_sc * non-copper fraction of conductor * conductor fraction of cable + j_crit_cable = j_crit_sc * (1.0e0 - fcu) * fcond + # Critical current in cable (copper added at this stage in HTS cables) + icrit = j_crit_cable * acs + + # Strand critical current calulation for costing in $ / kAm + # Already includes buffer and support layers so no need to include fcu here + tfcoil_variables.j_crit_str_tf = j_crit_sc + + elif ( + isumat == 9 + ): # Hazelton experimental data + Zhai conceptual model for REBCO + bc20m = 138 + tc0m = 92 + # If strain limit achieved, throw a warning and use the lower strain + if abs(strain) > 0.7e-2: + error_handling.fdiags[0] = strain + error_handling.report_error(261) + strain = np.sign(strain) * 0.7e-2 + + # 'high current density' as per parameterisation described in Wolf, + # and based on Hazelton experimental data and Zhai conceptual model; + # see subroutine for full references + j_crit_sc, _, _ = superconductors.hijc_rebco( + thelium, bmax, strain, bc20m, tc0m + ) + # j_crit_cable = j_crit_sc * non-copper fraction of conductor * conductor fraction of cable + j_crit_cable = j_crit_sc * (1.0e0 - fcu) * fcond + # Critical current in cable (copper added at this stage in HTS cables) + icrit = j_crit_cable * acs + + # Strand critical current calulation for costing in $ / kAm + # = superconducting filaments jc * (1 -strand copper fraction) + tfcoil_variables.j_crit_str_tf = j_crit_sc * (1.0e0 - fcu) + + else: + error_handling.idiags[0] = isumat + error_handling.report_error(105) + + # Critical current density in winding pack + # aturn : Area per turn (i.e. entire jacketed conductor with insulation) (m2) + jwdgcrt = icrit / aturn + # Ratio of operating / critical current + iooic = iop / icrit + # Operating current density + jwdgop = iop / aturn + # Actual current density in superconductor, which should be equal to jcrit(thelium+tmarg) + # when we have found the desired value of tmarg + jsc = iooic * j_crit_sc + + if iooic <= 0e0: + logger.warning( + f"""Negative Iop/Icrit for TF coil + jsc: {jsc} + iooic: {iooic} + j_crit_sc: {j_crit_sc} + Check conductor dimensions. Cable space area acs likely gone negative. acs: {acs} + This is likely because t_cable_radial or t_cable_toroidal has gone negative: + t_cable_radial: {sctfcoil_module.t_cable_radial} + t_cable_toroidal: {sctfcoil_module.t_cable_toroidal} + """ + ) + + # REBCO measurements from 2 T to 14 T, extrapolating outside this + if (isumat == 8) and (tfcoil_variables.bmaxtfrp >= 14): + error_handling.report_error(266) + + # Temperature margin (already calculated in superconductors.bi2212 for isumat=2) + + if ( + (isumat == 1) + or (isumat == 3) + or (isumat == 4) + or (isumat == 5) + or (isumat == 7) + or (isumat == 8) + or (isumat == 9) + ): # Find temperature at which current density margin = 0 + if isumat == 3: + arguments = (isumat, jsc, bmax, strain, bc20m, tc0m, c0) + else: + arguments = (isumat, jsc, bmax, strain, bc20m, tc0m) + + another_estimate = 2 * thelium + t_zero_margin, root_result = optimize.newton( + superconductors.current_density_margin, + thelium, + fprime=None, + args=arguments, + # args=(isumat, jsc, bmax, strain, bc20m, tc0m,), + tol=1.0e-06, + maxiter=50, + fprime2=None, + x1=another_estimate, + rtol=1.0e-6, + full_output=True, + disp=True, + ) + # print(root_result) # Diagnostic for newton method + tmarg = t_zero_margin - thelium + tfcoil_variables.temp_margin = tmarg + + # Find the current density limited by the protection limit + # (N.B. Unclear of this routine's relevance for Bi-2212 (isumat=2), due + # to presence of fcu argument, which is not used for this model above) + + tfcoil_variables.jwdgpro, vd = self.protect( + iop, tfes, acs, aturn, tdump, fcond, fcu, thelium, tmax + ) + + if output: # Output -------------------------- + if tmarg <= 0.0e0: + logger.warning( + """Negative TFC temperature margin + tmarg: {tmarg} + bmax: {bmax} + jcrit0: {jcrit0} + jsc: {jsc} + """ + ) + + po.oheadr(self.outfile, "Superconducting TF Coils") + po.ovarin(self.outfile, "Superconductor switch", "(isumat)", isumat) + + if isumat == 1: + po.ocmmnt(self.outfile, "Superconductor used: Nb3Sn") + po.ocmmnt(self.outfile, " (ITER Jcrit model, standard parameters)") + po.ovarre( + self.outfile, + "Critical field at zero temperature and strain (T)", + "(bc20m)", + bc20m, + ) + po.ovarre( + self.outfile, + "Critical temperature at zero field and strain (K)", + "(tc0m)", + tc0m, + ) + if isumat == 2: + po.ocmmnt(self.outfile, "Superconductor used: Bi-2212 HTS") + if isumat == 3: + po.ocmmnt(self.outfile, "Superconductor used: NbTi") + po.ovarre( + self.outfile, + "Critical field at zero temperature and strain (T)", + "(bc20m)", + bc20m, + ) + po.ovarre( + self.outfile, + "Critical temperature at zero field and strain (K)", + "(tc0m)", + tc0m, + ) + if isumat == 4: + po.ocmmnt(self.outfile, "Superconductor used: Nb3Sn") + po.ocmmnt(self.outfile, " (ITER Jcrit model, user-defined parameters)") + po.ovarre( + self.outfile, + "Critical field at zero temperature and strain (T)", + "(bc20m)", + bc20m, + ) + po.ovarre( + self.outfile, + "Critical temperature at zero field and strain (K)", + "(tc0m)", + tc0m, + ) + if isumat == 5: + po.ocmmnt(self.outfile, "Superconductor used: Nb3Sn") + po.ocmmnt(self.outfile, " (WST Nb3Sn critical surface model)") + po.ovarre( + self.outfile, + "Critical field at zero temperature and strain (T)", + "(bc20m)", + bc20m, + ) + po.ovarre( + self.outfile, + "Critical temperature at zero field and strain (K)", + "(tc0m)", + tc0m, + ) + if isumat == 7: + po.ocmmnt(self.outfile, "Superconductor used: Nb-Ti") + po.ocmmnt( + self.outfile, " (Durham Ginzburg-Landau critical surface model)" + ) + po.ovarre( + self.outfile, + "Critical field at zero temperature and strain (T)", + "(bc20m)", + bc20m, + ) + po.ovarre( + self.outfile, + "Critical temperature at zero field and strain (K)", + "(tc0m)", + tc0m, + ) + if isumat == 8: + po.ocmmnt(self.outfile, "Superconductor used: REBCO") + po.ocmmnt( + self.outfile, " (Durham Ginzburg-Landau critical surface model)" + ) + po.ovarre( + self.outfile, + "Critical field at zero temperature and strain (T)", + "(bc20m)", + bc20m, + ) + po.ovarre( + self.outfile, + "Critical temperature at zero field and strain (K)", + "(tc0m)", + tc0m, + ) + if isumat == 9: + po.ocmmnt(self.outfile, "Superconductor used: REBCO") + po.ocmmnt( + self.outfile, + " (Hazelton experimental data + Zhai conceptual model)", + ) + po.ovarre( + self.outfile, + "Critical field at zero temperature and strain (T)", + "(bc20m)", + bc20m, + ) + po.ovarre( + self.outfile, + "Critical temperature at zero field and strain (K)", + "(tc0m)", + tc0m, + ) + + if global_variables.run_tests == 1: + po.oblnkl(self.outfile) + po.ocmmnt( + self.outfile, + "PROCESS TF Coil peak field fit. Values for t, z and y:", + ) + po.oblnkl(self.outfile) + po.ovarre( + self.outfile, + "Dimensionless winding pack width", + "(tf_fit_t)", + sctfcoil_module.tf_fit_t, + "OP ", + ) + po.ovarre( + self.outfile, + "Dimensionless winding pack radial thickness", + "(tf_fit_z)", + sctfcoil_module.tf_fit_z, + "OP ", + ) + po.ovarre( + self.outfile, + "Ratio of peak field with ripple to nominal axisymmetric peak field", + "(tf_fit_y)", + sctfcoil_module.tf_fit_y, + "OP ", + ) + + po.oblnkl(self.outfile) + po.ovarre( + self.outfile, + "Helium temperature at peak field (= superconductor temperature) (K)", + "(thelium)", + thelium, + ) + po.ovarre( + self.outfile, + "Total helium fraction inside cable space", + "(fhetot)", + fhetot, + "OP ", + ) + po.ovarre(self.outfile, "Copper fraction of conductor", "(fcutfsu)", fcu) + po.ovarre( + self.outfile, + "Residual manufacturing strain on superconductor", + "(str_tf_con_res)", + tfcoil_variables.str_tf_con_res, + ) + po.ovarre( + self.outfile, + "Self-consistent strain on superconductor", + "(str_wp)", + tfcoil_variables.str_wp, + ) + po.ovarre( + self.outfile, + "Critical current density in superconductor (A/m2)", + "(j_crit_sc)", + j_crit_sc, + "OP ", + ) + po.ovarre( + self.outfile, + "Critical current density in cable (A/m2)", + "(j_crit_cable)", + j_crit_cable, + "OP ", + ) + po.ovarre( + self.outfile, + "Critical current density in winding pack (A/m2)", + "(jwdgcrt)", + jwdgcrt, + "OP ", + ) + po.ovarre( + self.outfile, + "Actual current density in winding pack (A/m2)", + "(jwdgop)", + jwdgop, + "OP ", + ) + + po.ovarre( + self.outfile, + "Minimum allowed temperature margin in superconductor (K)", + "(tmargmin_tf)", + tfcoil_variables.tmargmin_tf, + ) + po.ovarre( + self.outfile, + "Actual temperature margin in superconductor (K)", + "(tmarg)", + tmarg, + "OP ", + ) + po.ovarre(self.outfile, "Critical current (A)", "(icrit)", icrit, "OP ") + po.ovarre( + self.outfile, + "Actual current (A)", + "(cpttf)", + tfcoil_variables.cpttf, + "OP ", + ) + po.ovarre( + self.outfile, + "Actual current / critical current", + "(iooic)", + iooic, + "OP ", + ) + + return jwdgcrt, vd, tmarg + + def protect(self, aio, tfes, acs, aturn, tdump, fcond, fcu, tba, tmax): + """Finds the current density limited by the protection limit + author: P J Knight, CCFE, Culham Science Centre + author: J Miller, ORNL + aio : input real : Operating current (A) + tfes : input real : Energy stored in one TF coil (J) + acs : input real : Cable space - inside area (m2) + aturn : input real : Area per turn (i.e. entire cable) (m2) + tdump : input real : Dump time (sec) + fcond : input real : Fraction of cable space containing conductor + fcu : input real : Fraction of conductor that is copper + tba : input real : He temperature at peak field point (K) + tmax : input real : Max conductor temperature during quench (K) + ajwpro : output real : Winding pack current density from temperature + rise protection (A/m2) + vd : output real : Discharge voltage imposed on a TF coil (V) + This routine calculates maximum conductor current density which + limits the peak temperature in the winding to a given limit (tmax). + It also finds the dump voltage. +

These calculations are based on Miller's formulations. + """ + # Integration coefficients p1,p2,p3 + p1 = ( + 0.0e0, + 0.8e0, + 1.75e0, + 2.4e0, + 2.7e0, + 2.95e0, + 3.1e0, + 3.2e0, + 3.3e0, + 3.4e0, + 3.5e0, + ) + p2 = ( + 0.0e0, + 0.05e0, + 0.5e0, + 1.4e0, + 2.6e0, + 3.7e0, + 4.6e0, + 5.3e0, + 5.95e0, + 6.55e0, + 7.1e0, + ) + p3 = ( + 0.0e0, + 0.05e0, + 0.5e0, + 1.4e0, + 2.6e0, + 3.7e0, + 4.6e0, + 5.4e0, + 6.05e0, + 6.8e0, + 7.2e0, + ) + + # Dump voltage + + vd = 2.0e0 * tfes / (tdump * aio) + + # Current density limited by temperature rise during quench + + tav = 1.0e0 + (tmax - tba) / 20.0e0 + n_o = int(tav) + n_p = n_o + 1 + n_p = min(n_p, 11) + + ai1 = 1.0e16 * (p1[n_o - 1] + (p1[n_p - 1] - p1[n_o - 1]) * (tav - n_o)) + ai2 = 1.0e16 * (p2[n_o - 1] + (p2[n_p - 1] - p2[n_o - 1]) * (tav - n_o)) + ai3 = 1.0e16 * (p3[n_o - 1] + (p3[n_p - 1] - p3[n_o - 1]) * (tav - n_o)) + + aa = vd * aio / tfes + bb = (1.0e0 - fcond) * fcond * fcu * ai1 + cc = (fcu * fcond) ** 2 * ai2 + dd = (1.0e0 - fcu) * fcu * fcond**2 * ai3 + ajcp = np.sqrt(aa * (bb + cc + dd)) + ajwpro = ajcp * (acs / aturn) + + return ajwpro, vd + + # --- + + def vv_stress_on_quench(self): + """Calculate the Tresca stress [Pa] of the Vacuum Vessel (VV) + experienced when the TF coil quenches. + + Author: Timothy Nunn, UKAEA + + Assumes the current center line (CCL) of the TF coil is the + middle of the coil. + + We assume vertical symmetry which is only true for double null + machines. + """ + H_coil = build_variables.hmax + (build_variables.dr_tf_inboard / 2) + ri_coil = build_variables.r_tf_inboard_mid + ro_coil = build_variables.r_tf_outboard_mid + # NOTE: rm is measured from the outside edge of the coil because thats where + # the radius of the first ellipse is measured from + rm_coil = build_variables.r_tf_inboard_out + tfcoil_variables.tfa[0] + + H_vv = ( + build_variables.z_plasma_xpoint_upper + + build_variables.dz_xpoint_divertor + + divertor_variables.dz_divertor + + build_variables.dz_shld_upper + + (build_variables.dz_vv_upper / 2) + ) + # ri and ro for VV dont consider the shield widths + # because it is assumed the shield is on the plasma side + # of the VV + ri_vv = build_variables.r_vv_inboard_out - (build_variables.dr_vv_outboard / 2) + ro_vv = ( + build_variables.r_tf_outboard_mid + - (build_variables.dr_tf_outboard / 2) + - build_variables.dr_tf_shld_gap + - build_variables.dr_shld_thermal_outboard + - build_variables.dr_shld_vv_gap_outboard + - (build_variables.dr_vv_outboard / 2) + ) + + # Assume the radius of the first ellipse of the VV is in the same proportion to + # that of the plasma facing radii of the two structures + tf_vv_frac = build_variables.r_tf_inboard_out / build_variables.r_vv_inboard_out + rm_vv = build_variables.r_vv_inboard_out + ( + tfcoil_variables.tfa[0] * tf_vv_frac + ) + + sctfcoil_module.vv_stress_quench = vv_stress_on_quench( + # TF shape + H_coil=H_coil, + ri_coil=ri_coil, + ro_coil=ro_coil, + rm_coil=rm_coil, + ccl_length_coil=tfcoil_variables.len_tf_coil, + theta1_coil=tfcoil_variables.theta1_coil, + # VV shape + H_vv=H_vv, + ri_vv=ri_vv, + ro_vv=ro_vv, + rm_vv=rm_vv, + theta1_vv=tfcoil_variables.theta1_vv, + # TF properties + n_tf_coils=tfcoil_variables.n_tf_coils, + n_tf_turn=tfcoil_variables.n_tf_turn, + # Area of the radial plate taken to be the area of steel in the WP + # TODO: value clipped due to #1883 + s_rp=np.clip(sctfcoil_module.a_tf_steel, 0, None), + s_cc=sctfcoil_module.a_case_front + + sctfcoil_module.a_case_nose + + 2.0 * sctfcoil_module.t_lat_case_av, + taud=tfcoil_variables.tdmptf, + # TODO: is this the correct current? + i_op=sctfcoil_module.tfc_current / tfcoil_variables.n_tf_turn, + # VV properties + d_vv=build_variables.dr_vv_shells, + ) + + def peak_tf_with_ripple(self, n_tf_coils, wwp1, dr_tf_wp, tfin, bmaxtf): + """Peak toroidal field on the conductor + author: P J Knight, CCFE, Culham Science Centre + This subroutine calculates the peak toroidal field at the + outboard edge of the inboard TF coil winding pack, including + the effects of ripple. +

For 16, 18 or 20 coils, the calculation uses fitting formulae + derived by M. Kovari using MAGINT calculations on coil sets based + on a DEMO1 case. +

For other numbers of coils, the original estimate using a 9% + increase due to ripple from the axisymmetric calculation is used. + M. Kovari, Toroidal Field Coils - Maximum Field and Ripple - + Parametric Calculation, July 2014 + + :param n_tf_coils: number of TF coils + :type n_tf_coils: float + :param wwp1: width of plasma-facing face of winding pack (m) + :type wwp1: float + :param dr_tf_wp: radial thickness of winding pack (m) + :type dr_tf_wp: float + :param tfin: major radius of centre of winding pack (m) + :type tfin: float + :param bmaxtf: nominal (axisymmetric) peak toroidal field (T) + :type bmaxtf: float + + :returns: (bmaxtfrp, flag) + * bmaxtfrp: peak toroidal field including ripple (T) + * flag: flag warning of applicability problems + + :rtype: Tuple[float, int] + + """ + a = np.zeros((4,)) + flag = 0 + + # Set fitting coefficients for different numbers of TF coils + + int_n_tf = np.round(n_tf_coils) + + if int_n_tf == 16: + a[0] = 0.28101e0 + a[1] = 1.8481e0 + a[2] = -0.88159e0 + a[3] = 0.93834e0 + elif int_n_tf == 18: + a[0] = 0.29153e0 + a[1] = 1.81600e0 + a[2] = -0.84178e0 + a[3] = 0.90426e0 + elif int_n_tf == 20: + a[0] = 0.29853e0 + a[1] = 1.82130e0 + a[2] = -0.85031e0 + a[3] = 0.89808e0 + + else: + bmaxtfrp = 1.09e0 * bmaxtf + return bmaxtfrp, flag + + # Maximum winding pack width before adjacent packs touch + # (ignoring the external case and ground wall thicknesses) + + wmax = (2.0e0 * tfin + dr_tf_wp) * np.tan(np.pi / n_tf_coils) + + # Dimensionless winding pack width + + sctfcoil_module.tf_fit_t = wwp1 / wmax + if (sctfcoil_module.tf_fit_t < 0.3e0) or (sctfcoil_module.tf_fit_t > 1.1e0): + # write(*,*) 'PEAK_TF_WITH_RIPPLE: fitting problem; t = ',t + flag = 1 + + # Dimensionless winding pack radial thickness + + sctfcoil_module.tf_fit_z = dr_tf_wp / wmax + if (sctfcoil_module.tf_fit_z < 0.26e0) or (sctfcoil_module.tf_fit_z > 0.7e0): + # write(*,*) 'PEAK_TF_WITH_RIPPLE: fitting problem; z = ',z + flag = 2 + + # Ratio of peak field with ripple to nominal axisymmetric peak field + + sctfcoil_module.tf_fit_y = ( + a[0] + + a[1] * np.exp(-sctfcoil_module.tf_fit_t) + + a[2] * sctfcoil_module.tf_fit_z + + a[3] * sctfcoil_module.tf_fit_z * sctfcoil_module.tf_fit_t + ) + + bmaxtfrp = sctfcoil_module.tf_fit_y * bmaxtf + + return bmaxtfrp, flag + + def sc_tf_internal_geom(self, i_tf_wp_geom, i_tf_case_geom, i_tf_turns_integer): + """ + Author : S. Kahn, CCFE + Seting the WP, case and turns geometry for SC magnets + """ + + # Calculating the WP / ground insulation areas + self.tf_wp_geom(i_tf_wp_geom) + + # Calculating the TF steel casing areas + self.tf_case_geom(i_tf_wp_geom, i_tf_case_geom) + + # WP/trun currents + self.tf_wp_currents() + + # sctfcoil_module.sc_tf_internal_geom( + # i_tf_wp_geom, i_tf_case_geom, i_tf_turns_integer + # ) + + # Setting the WP turn geometry / areas + if i_tf_turns_integer == 0: + # Non-ingeger number of turns + ( + tfcoil_variables.acstf, + tfcoil_variables.acndttf, + tfcoil_variables.insulation_area, + tfcoil_variables.n_tf_turn, + ) = self.tf_averaged_turn_geom( + tfcoil_variables.jwptf, + tfcoil_variables.thwcndut, + tfcoil_variables.thicndut, + tfcoil_variables.i_tf_sc_mat, + ) + + else: + # Integer number of turns + ( + tfcoil_variables.acstf, + tfcoil_variables.acndttf, + tfcoil_variables.insulation_area, + tfcoil_variables.cpttf, + tfcoil_variables.n_tf_turn, + ) = self.tf_integer_turn_geom( + tfcoil_variables.n_layer, + tfcoil_variables.n_pancake, + tfcoil_variables.thwcndut, + tfcoil_variables.thicndut, + ) + + # Areas and fractions + # ------------------- + # Central helium channel down the conductor core [m2] + tfcoil_variables.awphec = ( + 0.25e0 * tfcoil_variables.n_tf_turn * np.pi * tfcoil_variables.dhecoil**2 + ) + + # Total conductor cross-sectional area, taking account of void area + # and central helium channel [m2] + tfcoil_variables.acond = ( + tfcoil_variables.acstf + * tfcoil_variables.n_tf_turn + * (1.0e0 - tfcoil_variables.vftf) + - tfcoil_variables.awphec + ) + + # Void area in conductor for He, not including central channel [m2] + tfcoil_variables.avwp = ( + tfcoil_variables.acstf * tfcoil_variables.n_tf_turn * tfcoil_variables.vftf + ) + + # Area of inter-turn insulation: total [m2] + tfcoil_variables.aiwp = ( + tfcoil_variables.n_tf_turn * tfcoil_variables.insulation_area + ) + + # Area of steel structure in winding pack [m2] + tfcoil_variables.aswp = tfcoil_variables.n_tf_turn * tfcoil_variables.acndttf + + # Inboard coil steel area [m2] + sctfcoil_module.a_tf_steel = tfcoil_variables.acasetf + tfcoil_variables.aswp + + # Inboard coil steel fraction [-] + sctfcoil_module.f_tf_steel = ( + tfcoil_variables.n_tf_coils + * sctfcoil_module.a_tf_steel + / tfcoil_variables.tfareain + ) + + # Inboard coil insulation cross-section [m2] + sctfcoil_module.a_tf_ins = tfcoil_variables.aiwp + sctfcoil_module.a_ground_ins + + # Inboard coil insulation fraction [-] + sctfcoil_module.f_tf_ins = ( + tfcoil_variables.n_tf_coils + * sctfcoil_module.a_tf_ins + / tfcoil_variables.tfareain + ) + + # Negative areas or fractions error reporting + if ( + tfcoil_variables.acond <= 0.0e0 + or tfcoil_variables.avwp <= 0.0e0 + or tfcoil_variables.aiwp <= 0.0e0 + or tfcoil_variables.aswp <= 0.0e0 + or sctfcoil_module.a_tf_steel <= 0.0e0 + or sctfcoil_module.f_tf_steel <= 0.0e0 + or sctfcoil_module.a_tf_ins <= 0.0e0 + or sctfcoil_module.f_tf_ins <= 0.0e0 + ): + error_handling.fdiags[0] = tfcoil_variables.acond + error_handling.fdiags[1] = tfcoil_variables.avwp + error_handling.fdiags[2] = tfcoil_variables.aiwp + error_handling.fdiags[3] = tfcoil_variables.aswp + error_handling.fdiags[4] = sctfcoil_module.a_tf_steel + error_handling.fdiags[5] = sctfcoil_module.f_tf_steel + error_handling.fdiags[6] = sctfcoil_module.a_tf_ins + error_handling.fdiags[7] = sctfcoil_module.f_tf_ins + error_handling.report_error(276) + + def tf_wp_geom(self, i_tf_wp_geom): + """ + Author : S. Kahn, CCFE + Seting the WP geometry and area for SC magnets + """ + sctfcoil_module.r_wp_inner = ( + build_variables.r_tf_inboard_in + tfcoil_variables.thkcas + ) + + # Radial position of outer edge of winding pack [m] + sctfcoil_module.r_wp_outer = ( + sctfcoil_module.r_wp_inner + tfcoil_variables.dr_tf_wp + ) + + # Radius of geometrical centre of winding pack [m] + sctfcoil_module.r_wp_centre = 0.5e0 * ( + sctfcoil_module.r_wp_inner + sctfcoil_module.r_wp_outer + ) + + # TF toroidal thickness at the WP inner radius [m] + t_tf_at_wp = 2.0e0 * sctfcoil_module.r_wp_inner * sctfcoil_module.tan_theta_coil + + # Minimal toroidal thickness of winding pack [m] + sctfcoil_module.t_wp_toroidal = t_tf_at_wp - 2.0e0 * tfcoil_variables.casths + + # Rectangular WP + # -------------- + if i_tf_wp_geom == 0: + # Outer WP layer toroidal thickness [m] + tfcoil_variables.wwp1 = sctfcoil_module.t_wp_toroidal + + # Averaged toroidal thickness of of winding pack [m] + sctfcoil_module.t_wp_toroidal_av = sctfcoil_module.t_wp_toroidal + + # Total cross-sectional area of winding pack [m2] + sctfcoil_module.awpc = ( + tfcoil_variables.dr_tf_wp * sctfcoil_module.t_wp_toroidal + ) + + # WP cross-section without insertion gap and ground insulation [m2] + sctfcoil_module.awptf = ( + tfcoil_variables.dr_tf_wp + - 2.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.tfinsgap) + ) * ( + sctfcoil_module.t_wp_toroidal + - 2.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.tfinsgap) + ) + + # Cross-section area of the WP ground insulation [m2] + sctfcoil_module.a_ground_ins = ( + tfcoil_variables.dr_tf_wp - 2.0e0 * tfcoil_variables.tfinsgap + ) * ( + sctfcoil_module.t_wp_toroidal - 2.0e0 * tfcoil_variables.tfinsgap + ) - sctfcoil_module.awptf + + # Double rectangular WP + # --------------------- + elif i_tf_wp_geom == 1: + # Thickness of winding pack section at R > sctfcoil_module.r_wp_centre [m] + tfcoil_variables.wwp1 = 2.0e0 * ( + sctfcoil_module.r_wp_centre * sctfcoil_module.tan_theta_coil + - tfcoil_variables.casths + ) + + # Thickness of winding pack section at R < sctfcoil_module.r_wp_centre [m] + tfcoil_variables.wwp2 = 2.0e0 * ( + sctfcoil_module.r_wp_inner * sctfcoil_module.tan_theta_coil + - tfcoil_variables.casths + ) + + # Averaged toroidal thickness of of winding pack [m] + sctfcoil_module.t_wp_toroidal_av = 0.5e0 * ( + tfcoil_variables.wwp1 + tfcoil_variables.wwp2 + ) + + # Total cross-sectional area of winding pack [m2] + # Including ground insulation and insertion gap + sctfcoil_module.awpc = ( + tfcoil_variables.dr_tf_wp * sctfcoil_module.t_wp_toroidal_av + ) + + # WP cross-section without insertion gap and ground insulation [m2] + sctfcoil_module.awptf = ( + 0.5e0 + * ( + tfcoil_variables.dr_tf_wp + - 2.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.tfinsgap) + ) + * ( + tfcoil_variables.wwp1 + + tfcoil_variables.wwp2 + - 4.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.tfinsgap) + ) + ) + + # Cross-section area of the WP ground insulation [m2] + sctfcoil_module.a_ground_ins = ( + 0.5e0 + * (tfcoil_variables.dr_tf_wp - 2.0e0 * tfcoil_variables.tfinsgap) + * ( + tfcoil_variables.wwp1 + + tfcoil_variables.wwp2 + - 4.0e0 * tfcoil_variables.tfinsgap + ) + - sctfcoil_module.awptf + ) + + # Trapezoidal WP + # -------------- + else: + # Thickness of winding pack section at sctfcoil_module.r_wp_outer [m] + tfcoil_variables.wwp1 = 2.0e0 * ( + sctfcoil_module.r_wp_outer * sctfcoil_module.tan_theta_coil + - tfcoil_variables.casths + ) + + # Thickness of winding pack section at sctfcoil_module.r_wp_inner [m] + tfcoil_variables.wwp2 = 2.0e0 * ( + sctfcoil_module.r_wp_inner * sctfcoil_module.tan_theta_coil + - tfcoil_variables.casths + ) + + # Averaged toroidal thickness of of winding pack [m] + sctfcoil_module.t_wp_toroidal_av = 0.5e0 * ( + tfcoil_variables.wwp1 + tfcoil_variables.wwp2 + ) + + # Total cross-sectional area of winding pack [m2] + # Including ground insulation and insertion gap + sctfcoil_module.awpc = tfcoil_variables.dr_tf_wp * ( + tfcoil_variables.wwp2 + + 0.5e0 * (tfcoil_variables.wwp1 - tfcoil_variables.wwp2) + ) + + # WP cross-section without insertion gap and ground insulation [m2] + sctfcoil_module.awptf = ( + tfcoil_variables.dr_tf_wp + - 2.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.tfinsgap) + ) * ( + tfcoil_variables.wwp2 + - 2.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.tfinsgap) + + 0.5e0 * (tfcoil_variables.wwp1 - tfcoil_variables.wwp2) + ) + + # Cross-section area of the WP ground insulation [m2] + sctfcoil_module.a_ground_ins = ( + tfcoil_variables.dr_tf_wp - 2.0e0 * tfcoil_variables.tfinsgap + ) * ( + tfcoil_variables.wwp2 + - 2.0e0 * tfcoil_variables.tfinsgap + + 0.5e0 * (tfcoil_variables.wwp1 - tfcoil_variables.wwp2) + ) - sctfcoil_module.awptf + + # -------------- + + # Negative WP area error reporting + if sctfcoil_module.awptf <= 0.0e0 or sctfcoil_module.awpc <= 0.0e0: + error_handling.fdiags[0] = sctfcoil_module.awptf + error_handling.fdiags[1] = sctfcoil_module.awpc + error_handling.report_error(99) + + def tf_case_geom(self, i_tf_wp_geom, i_tf_case_geom): + """ + Author : S. Kahn, CCFE + Setting the case geometry and area for SC magnets + """ + tfcoil_variables.acasetf = ( + tfcoil_variables.tfareain / tfcoil_variables.n_tf_coils + ) - sctfcoil_module.awpc + + # Outboard leg cross-sectional area of surrounding case [m2] + tfcoil_variables.acasetfo = ( + tfcoil_variables.a_tf_leg_outboard - sctfcoil_module.awpc + ) + + # Front casing area [m2] + if i_tf_case_geom == 0: + # Circular front case + sctfcoil_module.a_case_front = ( + sctfcoil_module.theta_coil * build_variables.r_tf_inboard_out**2 + - sctfcoil_module.tan_theta_coil * sctfcoil_module.r_wp_outer**2 + ) + else: + # Straight front case + sctfcoil_module.a_case_front = ( + (sctfcoil_module.r_wp_outer + tfcoil_variables.casthi) ** 2 + - sctfcoil_module.r_wp_outer**2 + ) * sctfcoil_module.tan_theta_coil + + # Nose casing area [m2] + sctfcoil_module.a_case_nose = ( + sctfcoil_module.tan_theta_coil * sctfcoil_module.r_wp_inner**2 + - sctfcoil_module.theta_coil * build_variables.r_tf_inboard_in**2 + ) + + # Report error if the casing area is negative + if tfcoil_variables.acasetf <= 0.0e0 or tfcoil_variables.acasetfo <= 0.0e0: + error_handling.fdiags[0] = tfcoil_variables.acasetf + error_handling.fdiags[1] = tfcoil_variables.acasetfo + error_handling.report_error(99) + + # Average lateral casing thickness + # -------------- + # Rectangular casing + if i_tf_wp_geom == 0: + sctfcoil_module.t_lat_case_av = ( + tfcoil_variables.casths + + 0.5e0 * sctfcoil_module.tan_theta_coil * tfcoil_variables.dr_tf_wp + ) + + # Double rectangular WP + elif i_tf_wp_geom == 1: + sctfcoil_module.t_lat_case_av = ( + tfcoil_variables.casths + + 0.25e0 * sctfcoil_module.tan_theta_coil * tfcoil_variables.dr_tf_wp + ) + + # Trapezoidal WP + else: + sctfcoil_module.t_lat_case_av = tfcoil_variables.casths + + # -------------- + + def tf_integer_turn_geom(self, n_layer, n_pancake, thwcndut, thicndut): + """ + Authors: J Morris & S Khan + Setting the TF WP turn geometry for SC magnets from the number + of turns rows in the radial direction. The turns can have any + rectangular shapes. + This calculation has two purposes, first to check if a turn can exist + (positive cable space) and the second to provide its dimenesions, + areas and the its associated current + + """ + sctfcoil_module.rbcndut = thwcndut * 0.75e0 + + # Radial turn dimension [m] + sctfcoil_module.t_turn_radial = ( + tfcoil_variables.dr_tf_wp + - 2.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.tfinsgap) + ) / n_layer + + if sctfcoil_module.t_turn_radial <= (2.0e0 * thicndut + 2.0e0 * thwcndut): + error_handling.fdiags[0] = sctfcoil_module.t_turn_radial + error_handling.fdiags[1] = thicndut + error_handling.fdiags[2] = thwcndut + error_handling.report_error(100) + + # Toroidal turn dimension [m] + sctfcoil_module.t_turn_toroidal = ( + sctfcoil_module.t_wp_toroidal + - 2.0e0 * (tfcoil_variables.tinstf + tfcoil_variables.tfinsgap) + ) / n_pancake + + if sctfcoil_module.t_turn_toroidal <= (2.0e0 * thicndut + 2.0e0 * thwcndut): + error_handling.fdiags[0] = sctfcoil_module.t_turn_toroidal + error_handling.fdiags[1] = thicndut + error_handling.fdiags[2] = thwcndut + error_handling.report_error(100) + + tfcoil_variables.t_turn_tf = np.sqrt( + sctfcoil_module.t_turn_radial * sctfcoil_module.t_turn_toroidal + ) + + # Number of TF turns + n_tf_turn = np.double(n_layer * n_pancake) + + # Current per turn [A/turn] + cpttf = sctfcoil_module.tfc_current / n_tf_turn + + # Radial and toroidal dimension of conductor [m] + sctfcoil_module.t_conductor_radial = ( + sctfcoil_module.t_turn_radial - 2.0e0 * thicndut + ) + sctfcoil_module.t_conductor_toroidal = ( + sctfcoil_module.t_turn_toroidal - 2.0e0 * thicndut + ) + tfcoil_variables.t_conductor = np.sqrt( + sctfcoil_module.t_conductor_radial * sctfcoil_module.t_conductor_toroidal + ) + + # Dimension of square cable space inside conduit [m] + sctfcoil_module.t_cable_radial = ( + sctfcoil_module.t_conductor_radial - 2.0e0 * thwcndut + ) + sctfcoil_module.t_cable_toroidal = ( + sctfcoil_module.t_conductor_toroidal - 2.0e0 * thwcndut + ) + sctfcoil_module.t_cable = np.sqrt( + sctfcoil_module.t_cable_radial * sctfcoil_module.t_cable_toroidal + ) + + # Cross-sectional area of cable space per turn + # taking account of rounded inside corners [m2] + acstf = (sctfcoil_module.t_cable_radial * sctfcoil_module.t_cable_toroidal) - ( + 4.0e0 - np.pi + ) * sctfcoil_module.rbcndut**2 + + if acstf <= 0.0e0: + if (sctfcoil_module.t_cable_radial < 0.0e0) or ( + sctfcoil_module.t_cable_toroidal < 0.0e0 + ): + error_handling.fdiags[0] = acstf + error_handling.fdiags[1] = sctfcoil_module.t_cable_radial + error_handling.fdiags[2] = sctfcoil_module.t_cable_toroidal + error_handling.report_error(101) + else: + error_handling.fdiags[0] = acstf + error_handling.fdiags[1] = sctfcoil_module.t_cable_radial + error_handling.fdiags[1] = sctfcoil_module.t_cable_toroidal + error_handling.report_error(102) + sctfcoil_module.rbcndut = 0.0e0 + acstf = ( + sctfcoil_module.t_cable_radial * sctfcoil_module.t_cable_toroidal + ) + + # Cross-sectional area of conduit jacket per turn [m2] + acndttf = ( + sctfcoil_module.t_conductor_radial * sctfcoil_module.t_conductor_toroidal + - acstf + ) + + # Area of inter-turn insulation: single turn [m2] + insulation_area = ( + sctfcoil_module.t_turn_radial * sctfcoil_module.t_turn_toroidal + - acndttf + - acstf + ) + return acstf, acndttf, insulation_area, cpttf, n_tf_turn + + # ------------- + + def tf_wp_currents(self): + """ + Author : S. Kahn, CCFE + Turn engineering turn currents/densities + """ + tfcoil_variables.jwptf = max( + 1.0e0, + tfcoil_variables.c_tf_total + / (tfcoil_variables.n_tf_coils * sctfcoil_module.awptf), + ) + + def outtf(self, peaktfflag): + """Writes superconducting TF coil output to file + author: P J Knight, CCFE, Culham Science Centre + outfile : input integer : output file unit + peaktfflag : input integer : warning flag from peak TF calculation + This routine writes the superconducting TF coil results + to the output file. + PROCESS Superconducting TF Coil Model, J. Morris, CCFE, 1st May 2014 + """ + + # General coil parameters + po.osubhd(self.outfile, "TF design") + po.ovarin( + self.outfile, + "Conductor technology", + "(i_tf_sup)", + tfcoil_variables.i_tf_sup, + ) + + if tfcoil_variables.i_tf_sup == 0: + po.ocmmnt( + self.outfile, " -> Resitive coil : Water cooled copper (GLIDCOP AL-15)" + ) + elif tfcoil_variables.i_tf_sup == 1: + po.ocmmnt(self.outfile, " -> Superconducting coil (SC)") + elif tfcoil_variables.i_tf_sup == 2: + po.ocmmnt(self.outfile, " -> Reisitive coil : Helium cooled aluminium") + + # SC material scaling + if tfcoil_variables.i_tf_sup == 1: + po.ovarin( + self.outfile, + "Superconductor material", + "(i_tf_sc_mat)", + tfcoil_variables.i_tf_sc_mat, + ) + + if tfcoil_variables.i_tf_sc_mat == 1: + po.ocmmnt(self.outfile, " -> ITER Nb3Sn critical surface model") + elif tfcoil_variables.i_tf_sc_mat == 2: + po.ocmmnt(self.outfile, " -> Bi-2212 high temperature superconductor") + elif tfcoil_variables.i_tf_sc_mat == 3: + po.ocmmnt(self.outfile, " -> NbTi") + elif tfcoil_variables.i_tf_sc_mat == 4: + po.ocmmnt( + self.outfile, + " -> ITER Nb3Sn critical surface model, user-defined parameters", + ) + elif tfcoil_variables.i_tf_sc_mat == 5: + po.ocmmnt(self.outfile, " -> WST Nb3Sn") + elif tfcoil_variables.i_tf_sc_mat == 6: + po.ocmmnt( + self.outfile, + " -> High temperature superconductor: REBCO HTS tape in CroCo strand", + ) + elif tfcoil_variables.i_tf_sc_mat == 7: + po.ocmmnt( + self.outfile, + " -> Durham Ginzburg-Landau critical surface model for Nb-Ti", + ) + elif tfcoil_variables.i_tf_sc_mat == 8: + po.ocmmnt( + self.outfile, + " -> Durham Ginzburg-Landau critical surface model for REBCO", + ) + elif tfcoil_variables.i_tf_sc_mat == 9: + po.ocmmnt( + self.outfile, + " -> Hazelton experimental data + Zhai conceptual model for REBCO", + ) + + # Joints strategy + po.ovarin( + self.outfile, + "Presence of TF demountable joints", + "(itart)", + physics_variables.itart, + ) + if physics_variables.itart == 1: + po.ocmmnt( + self.outfile, " -> TF coil made of a Centerpost (CP) and outer legs" + ) + po.ocmmnt(self.outfile, " interfaced with demountable joints") + else: + po.ocmmnt(self.outfile, " -> Coils without demountable joints") + + # Centring forces support strategy + po.ovarin( + self.outfile, + "TF inboard leg support strategy", + "(i_tf_bucking)", + tfcoil_variables.i_tf_bucking, + ) + + if tfcoil_variables.i_tf_bucking == 0: + po.ocmmnt(self.outfile, " -> No support structure") + elif tfcoil_variables.i_tf_bucking == 1: + if tfcoil_variables.i_tf_sup == 1: + po.ocmmnt(self.outfile, " -> Steel casing") + elif ( + abs(tfcoil_variables.eyoung_res_tf_buck - 205.0e9) + < np.finfo(float(tfcoil_variables.eyoung_res_tf_buck)).eps + ): + po.ocmmnt(self.outfile, " -> Steel bucking cylinder") + else: + po.ocmmnt(self.outfile, " -> Bucking cylinder") + + elif ( + tfcoil_variables.i_tf_bucking in (2, 3) + and build_variables.i_tf_inside_cs == 1 + ): + po.ocmmnt( + self.outfile, + " -> TF in contact with dr_bore filler support (bucked and weged design)", + ) + + elif ( + tfcoil_variables.i_tf_bucking in (2, 3) + and build_variables.i_tf_inside_cs == 0 + ): + po.ocmmnt( + self.outfile, " -> TF in contact with CS (bucked and weged design)" + ) + + # TF coil geometry + po.osubhd(self.outfile, "TF coil Geometry :") + po.ovarin( + self.outfile, + "Number of TF coils", + "(n_tf_coils)", + int(tfcoil_variables.n_tf_coils), + ) + po.ovarre( + self.outfile, + "Inboard leg centre radius (m)", + "(r_tf_inboard_mid)", + build_variables.r_tf_inboard_mid, + "OP ", + ) + po.ovarre( + constants.mfile, + "Inboard leg inner radius (m)", + "(r_tf_inboard_in)", + build_variables.r_tf_inboard_in, + "OP ", + ) + po.ovarre( + constants.mfile, + "Inboard leg outer radius (m)", + "(r_tf_inboard_out)", + build_variables.r_tf_inboard_out, + "OP ", + ) + po.ovarin( + self.outfile, + "WP shape selection switch", + "(i_tf_wp_geom)", + tfcoil_variables.i_tf_wp_geom, + ) + po.ovarre( + constants.mfile, + "Radial position of inner edge and centre of winding pack (m)", + "(r_wp_inner)", + sctfcoil_module.r_wp_inner, + "OP ", + ) + + po.ovarre( + self.outfile, + "Outboard leg centre radius (m)", + "(r_tf_outboard_mid)", + build_variables.r_tf_outboard_mid, + "OP ", + ) + po.ovarin( + self.outfile, + "Outboard leg nose case type", + "(i_tf_case_geom)", + tfcoil_variables.i_tf_case_geom, + ) + po.ovarre( + self.outfile, + "Total inboard leg radial thickness (m)", + "(dr_tf_inboard)", + build_variables.dr_tf_inboard, + ) + po.ovarre( + self.outfile, + "Total outboard leg radial thickness (m)", + "(dr_tf_outboard)", + build_variables.dr_tf_outboard, + ) + po.ovarre( + self.outfile, + "Outboard leg toroidal thickness (m)", + "(tftort)", + tfcoil_variables.tftort, + "OP ", + ) + po.ovarre( + self.outfile, + "Maximum inboard edge height (m)", + "(hmax)", + build_variables.hmax, + "OP ", + ) + if physics_variables.itart == 1: + po.ovarre( + self.outfile, + "Mean coil circumference (inboard leg not included) (m)", + "(len_tf_coil)", + tfcoil_variables.len_tf_coil, + "OP ", + ) + po.ovarre( + self.outfile, + "Length of the inboard segment (m)", + "(cplen)", + tfcoil_variables.cplen, + "OP ", + ) + else: + po.ovarre( + self.outfile, + "Mean coil circumference (including inboard leg length) (m)", + "(len_tf_coil)", + tfcoil_variables.len_tf_coil, + "OP ", + ) + + # Vertical shape + po.ovarin( + self.outfile, + "Vertical TF shape", + "(i_tf_shape)", + tfcoil_variables.i_tf_shape, + ) + if tfcoil_variables.i_tf_shape == 1: + po.oblnkl(self.outfile) + po.ocmmnt(self.outfile, "D-shape coil, inner surface shape approximated by") + po.ocmmnt( + self.outfile, + "by a straight segment and elliptical arcs between the following points:", + ) + po.oblnkl(self.outfile) + elif tfcoil_variables.i_tf_shape == 2: + po.oblnkl(self.outfile) + po.ocmmnt(self.outfile, "Picture frame coil, inner surface approximated by") + po.ocmmnt( + self.outfile, "by a straight segment between the following points:" + ) + po.oblnkl(self.outfile) + + po.write(self.outfile, " point x(m) y(m)") + for ii in range(5): + po.write( + self.outfile, + f" {ii} {tfcoil_variables.xarc[ii]} {tfcoil_variables.yarc[ii]}", + ) + po.ovarre( + constants.mfile, + f"TF coil arc point {ii} R (m)", + f"(xarc({ii + 1}))", + tfcoil_variables.xarc[ii], + ) + po.ovarre( + constants.mfile, + f"TF coil arc point {ii} Z (m)", + f"(yarc({ii + 1}))", + tfcoil_variables.yarc[ii], + ) + + # CP tapering geometry + if physics_variables.itart == 1 and tfcoil_variables.i_tf_sup != 1: + po.osubhd(self.outfile, "Tapered Centrepost TF coil Dimensions:") + po.ovarre( + self.outfile, + "TF coil centrepost outer radius at midplane (m)", + "(r_tf_inboard_out)", + build_variables.r_tf_inboard_out, + ) + po.ovarre( + self.outfile, + "TF coil centrepost outer radius at its top (m)", + "(r_cp_top)", + build_variables.r_cp_top, + ) + po.ovarre( + self.outfile, + "Top/miplane TF CP radius ratio (-)", + "(f_r_cp)", + build_variables.f_r_cp, + ) + po.ovarre( + self.outfile, + "Distance from the midplane to the top of the tapered section (m)", + "(h_cp_top)", + sctfcoil_module.h_cp_top, + ) + po.ovarre( + self.outfile, + "Distance from the midplane to the top of the centrepost (m)", + "(hmax + dr_tf_outboard)", + build_variables.hmax + build_variables.dr_tf_outboard, + ) + + # Turn/WP gemoetry + if tfcoil_variables.i_tf_sup == 1: + # Total material fraction + po.osubhd(self.outfile, "Global material area/fractions:") + po.ovarre( + self.outfile, + "TF cross-section (total) (m2)", + "(tfareain)", + tfcoil_variables.tfareain, + ) + po.ovarre( + self.outfile, + "Total steel cross-section (m2)", + "(a_tf_steel*n_tf_coils)", + sctfcoil_module.a_tf_steel * tfcoil_variables.n_tf_coils, + ) + po.ovarre( + self.outfile, + "Total steel TF fraction", + "(f_tf_steel)", + sctfcoil_module.f_tf_steel, + ) + po.ovarre( + self.outfile, + "Total Insulation cross-section (total) (m2)", + "(a_tf_ins*n_tf_coils)", + sctfcoil_module.a_tf_ins * tfcoil_variables.n_tf_coils, + ) + po.ovarre( + self.outfile, + "Total Insulation fraction", + "(f_tf_ins)", + sctfcoil_module.f_tf_ins, + ) + + # External casing + po.osubhd(self.outfile, "External steel Case Information :") + po.ovarre( + self.outfile, + "Casing cross section area (per leg) (m2)", + "(acasetf)", + tfcoil_variables.acasetf, + ) + po.ovarre( + self.outfile, + "Inboard leg case plasma side wall thickness (m)", + "(casthi)", + tfcoil_variables.casthi, + ) + po.ovarre( + self.outfile, + 'Inboard leg case inboard "nose" thickness (m)', + "(thkcas)", + tfcoil_variables.thkcas, + ) + po.ovarre( + self.outfile, + "Inboard leg case sidewall thickness at its narrowest point (m)", + "(casths)", + tfcoil_variables.casths, + ) + po.ovarre( + self.outfile, + "External case mass per coil (kg)", + "(whtcas)", + tfcoil_variables.whtcas, + "OP ", + ) + + # Winding pack structure + po.osubhd(self.outfile, "TF winding pack (WP) geometry:") + po.ovarre( + self.outfile, + "WP cross section area with insulation and insertion (per coil) (m2)", + "(awpc)", + sctfcoil_module.awpc, + ) + po.ovarre( + self.outfile, + "WP cross section area (per coil) (m2)", + "(aswp)", + sctfcoil_module.awptf, + ) + po.ovarre( + self.outfile, + "Winding pack radial thickness (m)", + "(dr_tf_wp)", + tfcoil_variables.dr_tf_wp, + "OP ", + ) + if tfcoil_variables.i_tf_turns_integer == 1: + po.ovarre( + self.outfile, + "Winding pack toroidal width (m)", + "(wwp1)", + tfcoil_variables.wwp1, + "OP ", + ) + else: + po.ovarre( + self.outfile, + "Winding pack toroidal width 1 (m)", + "(wwp1)", + tfcoil_variables.wwp1, + "OP ", + ) + po.ovarre( + self.outfile, + "Winding pack toroidal width 2 (m)", + "(wwp2)", + tfcoil_variables.wwp2, + "OP ", + ) + + po.ovarre( + self.outfile, + "Ground wall insulation thickness (m)", + "(tinstf)", + tfcoil_variables.tinstf, + ) + po.ovarre( + self.outfile, + "Winding pack insertion gap (m)", + "(tfinsgap)", + tfcoil_variables.tfinsgap, + ) + + # WP material fraction + po.osubhd(self.outfile, "TF winding pack (WP) material area/fractions:") + po.ovarre( + self.outfile, + "Steel WP cross-section (total) (m2)", + "(aswp*n_tf_coils)", + tfcoil_variables.aswp * tfcoil_variables.n_tf_coils, + ) + po.ovarre( + self.outfile, + "Steel WP fraction", + "(aswp/awpc)", + tfcoil_variables.aswp / sctfcoil_module.awpc, + ) + po.ovarre( + self.outfile, + "Insulation WP fraction", + "(aiwp/awpc)", + tfcoil_variables.aiwp / sctfcoil_module.awpc, + ) + po.ovarre( + self.outfile, + "Cable WP fraction", + "((awpc-aswp-aiwp)/awpc)", + (sctfcoil_module.awpc - tfcoil_variables.aswp - tfcoil_variables.aiwp) + / sctfcoil_module.awpc, + ) + + # Number of turns + po.osubhd(self.outfile, "WP turn information:") + po.ovarin( + self.outfile, + "Turn parametrisation", + "(i_tf_turns_integer)", + tfcoil_variables.i_tf_turns_integer, + ) + if tfcoil_variables.i_tf_turns_integer == 0: + po.ocmmnt(self.outfile, " Non-integer number of turns") + else: + po.ocmmnt(self.outfile, " Integer number of turns") + + po.ovarre( + self.outfile, + "Number of turns per TF coil", + "(n_tf_turn)", + tfcoil_variables.n_tf_turn, + "OP ", + ) + if tfcoil_variables.i_tf_turns_integer == 1: + po.ovarin( + self.outfile, + "Number of TF pancakes", + "(n_pancake)", + tfcoil_variables.n_pancake, + ) + po.ovarin( + self.outfile, + "Number of TF layers", + "(n_layer)", + tfcoil_variables.n_layer, + ) + + po.oblnkl(self.outfile) + + if tfcoil_variables.i_tf_turns_integer == 1: + po.ovarre( + self.outfile, + "Radial width of turn (m)", + "(t_turn_radial)", + sctfcoil_module.t_turn_radial, + ) + po.ovarre( + self.outfile, + "Toroidal width of turn (m)", + "(t_turn_toroidal)", + sctfcoil_module.t_turn_toroidal, + ) + po.ovarre( + self.outfile, + "Radial width of conductor (m)", + "(t_conductor_radial)", + sctfcoil_module.t_conductor_radial, + "OP ", + ) + po.ovarre( + self.outfile, + "Toroidal width of conductor (m)", + "(t_conductor_toroidal)", + sctfcoil_module.t_conductor_toroidal, + "OP ", + ) + po.ovarre( + self.outfile, + "Radial width of cable space", + "(t_cable_radial)", + sctfcoil_module.t_cable_radial, + ) + po.ovarre( + self.outfile, + "Toroidal width of cable space", + "(t_cable_toroidal)", + sctfcoil_module.t_cable_toroidal, + ) + else: + po.ovarre( + self.outfile, + "Width of turn including inter-turn insulation (m)", + "(t_turn_tf)", + tfcoil_variables.t_turn_tf, + "OP ", + ) + po.ovarre( + self.outfile, + "Width of conductor (square) (m)", + "(t_conductor)", + tfcoil_variables.t_conductor, + "OP ", + ) + po.ovarre( + self.outfile, + "Width of space inside conductor (m)", + "(t_cable)", + sctfcoil_module.t_cable, + "OP ", + ) + + po.ovarre( + self.outfile, + "Steel conduit thickness (m)", + "(thwcndut)", + tfcoil_variables.thwcndut, + ) + po.ovarre( + self.outfile, + "Inter-turn insulation thickness (m)", + "(thicndut)", + tfcoil_variables.thicndut, + ) + + if tfcoil_variables.i_tf_sc_mat in (1, 2, 3, 4, 5, 7, 8, 9): + po.osubhd(self.outfile, "Conductor information:") + po.ovarre( + self.outfile, + "Diameter of central helium channel in cable", + "(dhecoil)", + tfcoil_variables.dhecoil, + ) + po.ocmmnt(self.outfile, "Fractions by area") + po.ovarre( + self.outfile, + "internal area of the cable space", + "(acstf)", + tfcoil_variables.acstf, + ) + po.ovarre( + self.outfile, + "Coolant fraction in conductor excluding central channel", + "(vftf)", + tfcoil_variables.vftf, + ) + po.ovarre( + self.outfile, + "Copper fraction of conductor", + "(fcutfsu)", + tfcoil_variables.fcutfsu, + ) + po.ovarre( + self.outfile, + "Superconductor fraction of conductor", + "(1-fcutfsu)", + 1 - tfcoil_variables.fcutfsu, + ) + # TODO + # po.ovarre(self.outfile,'Conductor fraction of winding pack','(tfcoil_variables.acond/ap)',acond/ap, 'OP ') + # po.ovarre(self.outfile,'Conduit fraction of winding pack','(tfcoil_variables.n_tf_turn*tfcoil_variables.acndttf/ap)',n_tf_turn*tfcoil_variables.acndttf/ap, 'OP ') + # po.ovarre(self.outfile,'Insulator fraction of winding pack','(tfcoil_variables.aiwp/ap)',aiwp/ap, 'OP ') + # po.ovarre(self.outfile,'Helium area fraction of winding pack excluding central channel','(tfcoil_variables.avwp/ap)',avwp/ap, 'OP ') + # po.ovarre(self.outfile,'Central helium channel area as fraction of winding pack','(tfcoil_variables.awphec/ap)',awphec/ap, 'OP ') + ap = ( + tfcoil_variables.acond + + tfcoil_variables.n_tf_turn * tfcoil_variables.acndttf + + tfcoil_variables.aiwp + + tfcoil_variables.avwp + + tfcoil_variables.awphec + ) + po.ovarrf( + self.outfile, + "Check total area fractions in winding pack = 1", + "", + ( + tfcoil_variables.acond + + tfcoil_variables.n_tf_turn * tfcoil_variables.acndttf + + tfcoil_variables.aiwp + + tfcoil_variables.avwp + + tfcoil_variables.awphec + ) + / ap, + ) + po.ovarrf( + self.outfile, + "minimum TF conductor temperature margin (K)", + "(tmargmin_tf)", + tfcoil_variables.tmargmin_tf, + ) + po.ovarrf( + self.outfile, + "TF conductor temperature margin (K)", + "(tmargtf)", + tfcoil_variables.tmargtf, + ) + + po.ovarin( + self.outfile, + "Elastic properties behavior", + "(i_tf_cond_eyoung_axial)", + tfcoil_variables.i_tf_cond_eyoung_axial, + ) + if tfcoil_variables.i_tf_cond_eyoung_axial == 0: + po.ocmmnt(self.outfile, " Conductor stiffness neglected") + elif tfcoil_variables.i_tf_cond_eyoung_axial == 1: + po.ocmmnt(self.outfile, " Conductor stiffness is user-input") + elif tfcoil_variables.i_tf_cond_eyoung_axial == 2: + po.ocmmnt( + self.outfile, + " Conductor stiffness is set by material-specific default", + ) + + po.ovarre( + self.outfile, + "Conductor axial Youngs modulus", + "(eyoung_cond_axial)", + tfcoil_variables.eyoung_cond_axial, + ) + po.ovarre( + self.outfile, + "Conductor transverse Youngs modulus", + "(eyoung_cond_trans)", + tfcoil_variables.eyoung_cond_trans, + ) + else: + # External casing + po.osubhd(self.outfile, "Bucking cylinder information:") + po.ovarre( + self.outfile, + "Casing cross section area (per leg) (m2)", + "(acasetf)", + tfcoil_variables.acasetf, + ) + po.ovarre( + self.outfile, + "Inboard leg case plasma side wall thickness (m)", + "(casthi)", + tfcoil_variables.casthi, + ) + po.ovarre( + self.outfile, + "Inboard leg bucking cylinder thickness (m)", + "(thkcas)", + tfcoil_variables.thkcas, + ) + + # Conductor layer geometry + po.osubhd(self.outfile, "Inboard TFC conductor sector geometry:") + po.ovarre( + self.outfile, + "Inboard TFC conductor sector area with gr insulation (per leg) (m2)", + "(awpc)", + sctfcoil_module.awpc, + ) + po.ovarre( + self.outfile, + "Inboard TFC conductor sector area, NO ground & gap (per leg) (m2)", + "(awptf)", + sctfcoil_module.awptf, + ) + po.ovarre( + self.outfile, + "Inboard conductor sector radial thickness (m)", + "(dr_tf_wp)", + tfcoil_variables.dr_tf_wp, + ) + if physics_variables.itart == 1: + po.ovarre( + self.outfile, + "Central collumn top conductor sector radial thickness (m)", + "(dr_tf_wp_top)", + sctfcoil_module.dr_tf_wp_top, + ) + + po.ovarre( + self.outfile, + "Ground wall insulation thickness (m)", + "(tinstf)", + tfcoil_variables.tinstf, + ) + # Turn info + po.osubhd(self.outfile, "Coil turn information:") + po.ovarre( + self.outfile, + "Number of turns per TF leg", + "(n_tf_turn)", + tfcoil_variables.n_tf_turn, + ) + po.ovarre( + self.outfile, + "Turn insulation thickness", + "(thicndut)", + tfcoil_variables.thicndut, + ) + po.ovarre( + self.outfile, + "Mid-plane CP cooling fraction", + "(fcoolcp)", + tfcoil_variables.fcoolcp, + ) + po.ovarre( + self.outfile, + "Outboard leg current per turn (A)", + "(cpttf)", + tfcoil_variables.cpttf, + ) + po.ovarre( + self.outfile, + "Inboard leg conductor volume (m3)", + "(vol_cond_cp)", + tfcoil_variables.vol_cond_cp, + ) + po.ovarre( + self.outfile, + "Outboard leg volume per coil (m3)", + "(voltfleg)", + tfcoil_variables.voltfleg, + ) + + # Coil masses + po.osubhd(self.outfile, "TF coil mass:") + po.ovarre( + self.outfile, + "Superconductor mass per coil (kg)", + "(whtconsc)", + tfcoil_variables.whtconsc, + "OP ", + ) + po.ovarre( + self.outfile, + "Copper mass per coil (kg)", + "(whtconcu)", + tfcoil_variables.whtconcu, + "OP ", + ) + po.ovarre( + self.outfile, + "Steel conduit mass per coil (kg)", + "(whtconsh)", + tfcoil_variables.whtconsh, + "OP ", + ) + po.ovarre( + self.outfile, + "Conduit insulation mass per coil (kg)", + "(whtconin)", + tfcoil_variables.whtconin, + "OP ", + ) + if tfcoil_variables.i_tf_sup == 1: + po.ovarre( + self.outfile, + "Total conduit mass per coil (kg)", + "(whtcon)", + tfcoil_variables.whtcon, + "OP ", + ) + + if physics_variables.itart == 1: + po.ovarre( + self.outfile, + "Mass of inboard legs (kg)", + "(whtcp)", + tfcoil_variables.whtcp, + "OP ", + ) + po.ovarre( + self.outfile, + "Mass of outboard legs (kg)", + "(whttflgs)", + tfcoil_variables.whttflgs, + "OP ", + ) + + po.ovarre( + self.outfile, + "Mass of each TF coil (kg)", + "(whttf/n_tf_coils)", + tfcoil_variables.whttf / tfcoil_variables.n_tf_coils, + "OP ", + ) + po.ovarre( + self.outfile, + "Total TF coil mass (kg)", + "(whttf)", + tfcoil_variables.whttf, + "OP ", + ) + + # TF current and field + po.osubhd(self.outfile, "Maximum B field and currents:") + po.ovarre( + self.outfile, + "Nominal peak field assuming toroidal symmetry (T)", + "(bmaxtf)", + tfcoil_variables.bmaxtf, + "OP ", + ) + po.ovarre( + self.outfile, + "Total current in all TF coils (MA)", + "(c_tf_total/1.D6)", + 1.0e-6 * tfcoil_variables.c_tf_total, + "OP ", + ) + po.ovarre( + self.outfile, + "TF coil current (summed over all coils) (A)", + "(c_tf_total)", + tfcoil_variables.c_tf_total, + ) + if tfcoil_variables.i_tf_sup == 1: + po.ovarre( + self.outfile, + "Actual peak field at discrete conductor (T)", + "(bmaxtfrp)", + tfcoil_variables.bmaxtfrp, + "OP ", + ) + po.ovarre( + self.outfile, + "Winding pack current density (A/m2)", + "(jwptf)", + tfcoil_variables.jwptf, + "OP ", + ) + + po.ovarre( + self.outfile, + "Inboard leg mid-plane conductor current density (A/m2)", + "(oacdcp)", + tfcoil_variables.oacdcp, + ) + if physics_variables.itart == 1: + po.ovarre( + self.outfile, + "Outboard leg conductor current density (A/m2)", + "(cdtfleg)", + tfcoil_variables.cdtfleg, + ) + + po.ovarre( + self.outfile, + "Total stored energy in TF coils (GJ)", + "(estotftgj)", + tfcoil_variables.estotftgj, + "OP ", + ) + + # TF forces + po.osubhd(self.outfile, "TF Forces:") + po.ovarre( + self.outfile, + "Inboard vertical tension per coil (N)", + "(vforce)", + tfcoil_variables.vforce, + "OP ", + ) + po.ovarre( + self.outfile, + "Outboard vertical tension per coil (N)", + "(vforce_outboard)", + tfcoil_variables.vforce_outboard, + "OP ", + ) + po.ovarre( + self.outfile, + "inboard vertical tension fraction (-)", + "(f_vforce_inboard)", + tfcoil_variables.f_vforce_inboard, + "OP ", + ) + po.ovarre( + self.outfile, + "Centring force per coil (N/m)", + "(cforce)", + tfcoil_variables.cforce, + "OP ", + ) + + # Resistive coil parameters + if tfcoil_variables.i_tf_sup != 1: + po.osubhd(self.outfile, "Resitive loss parameters:") + if tfcoil_variables.i_tf_sup == 0: + po.ocmmnt( + self.outfile, + "Resistive Material : GLIDCOP AL-15 - Dispersion Strengthened Copper", + ) + elif tfcoil_variables.i_tf_sup == 2: + po.ocmmnt( + self.outfile, "Resistive Material : Pure Aluminium (99.999+ %)" + ) + + if physics_variables.itart == 1: + po.ovarre( + self.outfile, + "CP resistivity (ohm.m)", + "(rho_cp)", + tfcoil_variables.rho_cp, + ) + po.ovarre( + self.outfile, + "Leg resistivity (ohm.m)", + "(rho_tf_leg)", + tfcoil_variables.rho_tf_leg, + ) + po.ovarre( + self.outfile, + "CP resistive power loss (W)", + "(p_cp_resistive)", + tfcoil_variables.p_cp_resistive, + ) + po.ovarre( + self.outfile, + "Total legs resitive power loss, (W)", + "(p_tf_leg_resistive)", + tfcoil_variables.p_tf_leg_resistive, + ) + po.ovarre( + self.outfile, + "joints resistive power loss (W)", + "(pres_joints)", + tfcoil_variables.pres_joints, + ) + po.ovarre( + self.outfile, + "Outboard leg resistance per coil (ohm)", + "(res_tf_leg)", + tfcoil_variables.res_tf_leg, + ) + po.ovarre( + self.outfile, + "Average CP temperature (K)", + "(temp_cp_average)", + tfcoil_variables.temp_cp_average, + ) + po.ovarre( + self.outfile, + "Average leg temperature (K)", + "(temp_tf_legs_outboard)", + tfcoil_variables.temp_tf_legs_outboard, + ) + + else: + po.ovarre( + self.outfile, + "TF resistivity (ohm.m)", + "(p_cp_resistive)", + tfcoil_variables.rho_cp, + ) + po.ovarre( + self.outfile, + "TF coil resistive power less (total) (ohm.m)", + "(p_cp_resistive)", + tfcoil_variables.p_cp_resistive, + ) + po.ovarre( + self.outfile, + "Average coil temperature (K)", + "(temp_cp_average)", + tfcoil_variables.temp_cp_average, + ) + + # Ripple calculations + po.osubhd(self.outfile, "Ripple information:") + if tfcoil_variables.i_tf_shape == 1: + if peaktfflag == 1: + error_handling.report_error(144) + elif peaktfflag == 2: + error_handling.report_error(145) + + po.ovarre( + self.outfile, + "Max allowed tfcoil_variables.ripple amplitude at plasma outboard midplane (%)", + "(ripmax)", + tfcoil_variables.ripmax, + ) + po.ovarre( + self.outfile, + "Ripple amplitude at plasma outboard midplane (%)", + "(ripple)", + tfcoil_variables.ripple, + "OP ", + ) + else: + po.ovarre( + self.outfile, + "Max allowed tfcoil_variables.ripple amplitude at plasma (%)", + "(ripmax)", + tfcoil_variables.ripmax, + ) + po.ovarre( + self.outfile, + "Ripple at plasma edge (%)", + "(ripple)", + tfcoil_variables.ripple, + ) + po.ocmmnt( + self.outfile, + " Ripple calculation to be re-defined for picure frame coils", + ) + + # Quench information + if tfcoil_variables.i_tf_sup == 1: + po.osubhd(self.outfile, "Quench information :") + po.ovarre( + self.outfile, + "Actual quench time (or time constant) (s)", + "(tdmptf)", + tfcoil_variables.tdmptf, + ) + po.ovarre( + self.outfile, + "Vacuum Vessel stress on quench (Pa)", + "(vv_stress_quench)", + sctfcoil_module.vv_stress_quench, + "OP ", + ) + po.ovarre( + self.outfile, + "Maximum allowed voltage during quench due to insulation (kV)", + "(vdalw)", + tfcoil_variables.vdalw, + ) + po.ovarre( + self.outfile, + "Actual quench voltage (kV)", + "(vtfskv)", + tfcoil_variables.vtfskv, + "OP ", + ) + + if tfcoil_variables.i_tf_sc_mat in (1, 2, 3, 4, 5): + po.ovarre( + self.outfile, + "Maximum allowed temp rise during a quench (K)", + "(tmaxpro)", + tfcoil_variables.tmaxpro, + ) + elif tfcoil_variables == 6: + po.ocmmnt(self.outfile, "CroCo cable with jacket: ") + + if 75 in numerics.icc: + po.ovarre( + self.outfile, + "Maximum permitted TF coil current / copper area (A/m2)", + "(copperA_m2_max)", + rebco_variables.copperA_m2_max, + ) + + po.ovarre( + self.outfile, + "Actual TF coil current / copper area (A/m2)", + "(copperA_m2)", + rebco_variables.copperA_m2, + ) + + # TF coil radial build + po.osubhd(self.outfile, "Radial build of TF coil centre-line :") + # po.write(self.outfile,5) + # 5 format(t43,'Thickness (m)',t60,'Outer radius (m)') + + radius = build_variables.r_tf_inboard_in + po.obuild(self.outfile, "Innermost edge of TF coil", radius, radius) + + # Radial build for SC TF coils + if tfcoil_variables.i_tf_sup == 1: + radius = radius + tfcoil_variables.thkcas + po.obuild( + self.outfile, + 'Coil case ("nose")', + tfcoil_variables.thkcas, + radius, + "(thkcas)", + ) + + radius = radius + tfcoil_variables.tfinsgap + po.obuild( + self.outfile, + "Insertion gap for winding pack", + tfcoil_variables.tfinsgap, + radius, + "(tfinsgap)", + ) + + radius = radius + tfcoil_variables.tinstf + po.obuild( + self.outfile, + "Winding pack ground insulation", + tfcoil_variables.tinstf, + radius, + "(tinstf)", + ) + + radius = ( + radius + + 0.5e0 * tfcoil_variables.dr_tf_wp + - tfcoil_variables.tinstf + - tfcoil_variables.tfinsgap + ) + po.obuild( + self.outfile, + "Winding - first half", + tfcoil_variables.dr_tf_wp / 2e0 + - tfcoil_variables.tinstf + - tfcoil_variables.tfinsgap, + radius, + "(dr_tf_wp/2-tinstf-tfinsgap)", + ) + + radius = ( + radius + + 0.5e0 * tfcoil_variables.dr_tf_wp + - tfcoil_variables.tinstf + - tfcoil_variables.tfinsgap + ) + po.obuild( + self.outfile, + "Winding - second half", + tfcoil_variables.dr_tf_wp / 2e0 + - tfcoil_variables.tinstf + - tfcoil_variables.tfinsgap, + radius, + "(dr_tf_wp/2-tinstf-tfinsgap)", + ) + + radius = radius + tfcoil_variables.tinstf + po.obuild( + self.outfile, + "Winding pack insulation", + tfcoil_variables.tinstf, + radius, + "(tinstf)", + ) + + radius = radius + tfcoil_variables.tfinsgap + po.obuild( + self.outfile, + "Insertion gap for winding pack", + tfcoil_variables.tfinsgap, + radius, + "(tfinsgap)", + ) + + radius = radius + tfcoil_variables.casthi + po.obuild( + self.outfile, + "Plasma side case min radius", + tfcoil_variables.casthi, + radius, + "(casthi)", + ) + + radius = radius / np.cos(np.pi / tfcoil_variables.n_tf_coils) + po.obuild( + self.outfile, + "Plasma side case max radius", + build_variables.r_tf_inboard_out, + radius, + "(r_tf_inboard_out)", + ) + + # Radial build for restive coil + else: + radius = radius + tfcoil_variables.thkcas + po.obuild( + self.outfile, + "Coil bucking cylindre", + tfcoil_variables.thkcas, + radius, + "(thkcas)", + ) + + radius = radius + tfcoil_variables.tinstf + po.obuild( + self.outfile, + "Conductor ground insulation", + tfcoil_variables.tinstf, + radius, + "(tinstf)", + ) + + radius = ( + radius + 0.5e0 * tfcoil_variables.dr_tf_wp - tfcoil_variables.tinstf + ) + po.obuild( + self.outfile, + "Conductor - first half", + tfcoil_variables.dr_tf_wp / 2e0 - tfcoil_variables.tinstf, + radius, + "(tfcoil_variables.dr_tf_wp/2-tfcoil_variables.tinstf)", + ) + + radius = ( + radius + 0.5e0 * tfcoil_variables.dr_tf_wp - tfcoil_variables.tinstf + ) + po.obuild( + self.outfile, + "Conductor - second half", + tfcoil_variables.dr_tf_wp / 2e0 - tfcoil_variables.tinstf, + radius, + "(tfcoil_variables.dr_tf_wp/2-tfcoil_variables.tinstf)", + ) + + radius = radius + tfcoil_variables.tinstf + po.obuild( + self.outfile, + "Conductor ground insulation", + tfcoil_variables.tinstf, + radius, + "(tinstf)", + ) + + radius = radius + tfcoil_variables.casthi + po.obuild( + self.outfile, + "Plasma side TF coil support", + tfcoil_variables.casthi, + radius, + "(casthi)", + ) + + # Radial build consistency check + if ( + abs( + radius - build_variables.r_tf_inboard_in - build_variables.dr_tf_inboard + ) + < 10.0e0 * np.finfo(float(radius)).eps + ): + po.ocmmnt(self.outfile, "TF coil dimensions are consistent") + else: + po.ocmmnt(self.outfile, "ERROR: TF coil dimensions are NOT consistent:") + po.ovarre( + self.outfile, + "Radius of plasma-facing side of inner leg SHOULD BE [m]", + "", + build_variables.r_tf_inboard_in + build_variables.dr_tf_inboard, + ) + po.ovarre( + self.outfile, + "Inboard TF coil radial thickness [m]", + "(dr_tf_inboard)", + build_variables.dr_tf_inboard, + ) + po.oblnkl(self.outfile) + + tf_total_height = ( + build_variables.dh_tf_inner_bore + 2 * build_variables.dr_tf_inboard + ) + tf_total_width = ( + build_variables.dr_tf_inner_bore + + build_variables.dr_tf_inboard + + build_variables.dr_tf_outboard + ) + po.oblnkl(self.outfile) + po.obuild( + self.outfile, + "Total height and width of TFC [m]", + tf_total_height, + tf_total_width, + ) + + # Top section TF coil radial build (physics_variables.itart = 1 only) + if physics_variables.itart == 1 and tfcoil_variables.i_tf_sup != 1: + po.osubhd(self.outfile, "Radial build of TF coil at central collumn top :") + # write(self.outfile,5) + + # Restart the radial build at bucking cylindre inner radius + radius = build_variables.r_tf_inboard_in + po.obuild(self.outfile, "Innermost edge of TF coil", radius, radius) + + radius = radius + tfcoil_variables.thkcas + po.obuild( + self.outfile, + "Coil bucking cylindre", + tfcoil_variables.thkcas, + radius, + "(thkcas)", + ) + + radius = radius + tfcoil_variables.tinstf + po.obuild( + self.outfile, + "Conductor ground insulation", + tfcoil_variables.tinstf, + radius, + "(tinstf)", + ) + + radius = ( + radius + 0.5e0 * sctfcoil_module.dr_tf_wp_top - tfcoil_variables.tinstf + ) + po.obuild( + self.outfile, + "Conductor - first half", + 0.5e0 * sctfcoil_module.dr_tf_wp_top - tfcoil_variables.tinstf, + radius, + "(dr_tf_wp_top/2-tinstf)", + ) + + radius = ( + radius + 0.5e0 * sctfcoil_module.dr_tf_wp_top - tfcoil_variables.tinstf + ) + po.obuild( + self.outfile, + "Conductor - second half", + 0.5e0 * sctfcoil_module.dr_tf_wp_top - tfcoil_variables.tinstf, + radius, + "(dr_tf_wp_top/2-tinstf)", + ) + + radius = radius + tfcoil_variables.tinstf + po.obuild( + self.outfile, + "Conductor ground insulation", + tfcoil_variables.tinstf, + radius, + "(tinstf)", + ) + + radius = radius + tfcoil_variables.casthi + po.obuild( + self.outfile, + "Plasma side TF coil support", + tfcoil_variables.casthi, + radius, + "(casthi)", + ) + + # Consistency check + if abs(radius - build_variables.r_cp_top) < np.finfo(float(radius)).eps: + po.ocmmnt(self.outfile, "Top TF coil dimensions are consistent") + else: + po.ocmmnt(self.outfile, "ERROR: TF coil dimensions are NOT consistent:") + po.ovarre( + self.outfile, + "Radius of plasma-facing side of inner leg SHOULD BE [m]", + "", + build_variables.r_cp_top, + ) + po.oblnkl(self.outfile) + + def tf_averaged_turn_geom(self, jwptf, thwcndut, thicndut, i_tf_sc_mat): + """ + subroutine straight from Python, see comments in tf_averaged_turn_geom_wrapper + Authors : J. Morris, CCFE + Authors : S. Kahn, CCFE + Setting the TF WP turn geometry for SC magnets from the number + the current per turn. + This calculation has two purposes, first to check if a turn can exist + (positive cable space) and the second to provide its dimensions, + areas and the (float) number of turns + """ + + # Turn dimension is a an input + if tfcoil_variables.t_turn_tf_is_input: + # Turn area [m2] + a_turn = tfcoil_variables.t_turn_tf**2 + + # Current per turn [A] + tfcoil_variables.cpttf = a_turn * jwptf + + # Turn cable dimension is an input + elif tfcoil_variables.t_cable_tf_is_input: + # Turn squared dimension [m] + tfcoil_variables.t_turn_tf = tfcoil_variables.t_cable_tf + 2.0e0 * ( + thicndut + thwcndut + ) + + # Turn area [m2] + a_turn = tfcoil_variables.t_turn_tf**2 + + # Current per turn [A] + tfcoil_variables.cpttf = a_turn * jwptf + + # Current per turn is an input + else: + # Turn area [m2] + # Allow for additional inter-layer insulation MDK 13/11/18 + # Area of turn including conduit and inter-layer insulation + a_turn = tfcoil_variables.cpttf / jwptf + + # Dimension of square cross-section of each turn including inter-turn insulation [m] + tfcoil_variables.t_turn_tf = np.sqrt(a_turn) + + # Square turn assumption + sctfcoil_module.t_turn_radial = tfcoil_variables.t_turn_tf + sctfcoil_module.t_turn_toroidal = tfcoil_variables.t_turn_tf + + # See derivation in the following document + # k:\power plant physics and technology\process\hts\hts coil module for process.docx + tfcoil_variables.t_conductor = ( + -tfcoil_variables.layer_ins + + np.sqrt(tfcoil_variables.layer_ins**2 + 4.0e00 * a_turn) + ) / 2 - 2.0e0 * thicndut + + # Total number of turns per TF coil (not required to be an integer) + n_tf_turn = sctfcoil_module.awptf / a_turn + + # Area of inter-turn insulation: single turn [m2] + insulation_area = a_turn - tfcoil_variables.t_conductor**2 + + # NOTE: Fortran has acstf as an intent(out) variable that was outputting + # into tfcoil_variables.acstf. The local variable, however, appears to + # initially hold the value of tfcoil_variables.acstf despite not being + # intent(in). I have replicated this behaviour in Python for now. + acstf = copy.copy(tfcoil_variables.acstf) + + # ITER like turn structure + if i_tf_sc_mat != 6: + # Radius of rounded corners of cable space inside conduit [m] + rbcndut = thwcndut * 0.75e0 + + # Dimension of square cable space inside conduit [m] + sctfcoil_module.t_cable = tfcoil_variables.t_conductor - 2.0e0 * thwcndut + + # Cross-sectional area of cable space per turn + # taking account of rounded inside corners [m2] + acstf = sctfcoil_module.t_cable**2 - (4.0e0 - np.pi) * rbcndut**2 + + if acstf <= 0.0e0: + if tfcoil_variables.t_conductor < 0.0e0: + error_handling.fdiags[0] = acstf + error_handling.fdiags[1] = sctfcoil_module.t_cable + error_handling.report_error(101) + else: + error_handling.fdiags[0] = acstf + error_handling.fdiags[1] = sctfcoil_module.t_cable + error_handling.report_error(102) + rbcndut = 0.0e0 + acstf = sctfcoil_module.t_cable**2 + + # Cross-sectional area of conduit jacket per turn [m2] + acndttf = tfcoil_variables.t_conductor**2 - acstf + + # REBCO turn structure + elif i_tf_sc_mat == 6: + # Diameter of circular cable space inside conduit [m] + sctfcoil_module.t_cable = tfcoil_variables.t_conductor - 2.0e0 * thwcndut + + # Cross-sectional area of conduit jacket per turn [m2] + acndttf = tfcoil_variables.t_conductor**2 - acstf + + return acstf, acndttf, insulation_area, n_tf_turn + + +@staticmethod +def lambda_term(tau: float, omega: float) -> float: + """ + The lambda function used inegral in inductance calcuation found + in Y. Itoh et al. The full form of the functions are given in appendix A. + + Author: Alexander Pearce, UKAEA + + :param tau: tau_{s,k} = (R_{s,k} - R_{c,k}) / R_k + :param omega: omega_k = R_{c,k}/R_k + :returns: integral + """ + p = 1.0 - omega**2.0 + + if p < 0: + integral = (1.0 / np.sqrt(np.abs(p))) * np.arcsin( + (1.0 + omega * tau) / (tau + omega) + ) + else: + integral = (1.0 / np.sqrt(np.abs(p))) * np.log( + (2.0 * (1.0 + tau * omega - np.sqrt(p * (1 - tau**2.0)))) / (tau + omega) + ) + + return integral + + +@staticmethod +def _theta_factor_integral( + ro_vv: float, ri_vv: float, rm_vv: float, h_vv: float, theta1_vv: float +) -> float: + """ + The calcuation of the theta factor found in Eq 4 of Y. Itoh et al. The + full form of the integral is given in appendix A. + + Author: Alexander Pearce, UKAEA + + :param ro_vv: the radius of the outboard edge of the VV CCL + :param ri_vv: the radius of the inboard edge of the VV CCL + :param rm_vv: the radius where the maximum height of the VV CCL occurs + :param h_vv: the maximum height of the VV CCL + :param theta1_vv: the polar angle of the point at which one circular arc is + joined to another circular arc in the approximation to the VV CCL + :returns: theta factor. + """ + theta2 = np.pi / 2.0 + theta1_vv + a = (ro_vv - ri_vv) / 2.0 + rbar = (ro_vv + ri_vv) / 2.0 + delta = (rbar - rm_vv) / a + kappa = h_vv / a + iota = (1.0 + delta) / kappa + + denom = np.cos(theta1_vv) + np.sin(theta1_vv) - 1.0 + + r1 = h_vv * ((np.cos(theta1_vv) + iota * (np.sin(theta1_vv) - 1.0)) / denom) + r2 = h_vv * ((np.cos(theta1_vv) - 1.0 + iota * np.sin(theta1_vv)) / denom) + r3 = h_vv * (1 - delta) / kappa + + rc1 = (h_vv / kappa) * ((rbar / a) + 1.0) - r1 + rc2 = rc1 + (r1 - r2) * np.cos(theta1_vv) + rc3 = rc2 + zc2 = (r1 - r2) * np.sin(theta1_vv) + zc3 = zc2 + r2 - r3 + + tau = np.array([ + [np.cos(theta1_vv), np.cos(theta1_vv + theta2), -1.0], + [1.0, np.cos(theta1_vv), np.cos(theta1_vv + theta2)], + ]) + + omega = np.array([rc1 / r1, rc2 / r2, rc3 / r3]) + + # Assume up down symmetry and let Zc6 = - Zc3 + chi1 = (zc3 + np.abs(-zc3)) / ri_vv + chi2 = 0.0 + + for k in range(len(omega)): + chi2 = chi2 + np.abs( + lambda_term(tau[1, k], omega[k]) - lambda_term(tau[0, k], omega[k]) + ) + + return (chi1 + 2.0 * chi2) / (2.0 * np.pi) + + +def vv_stress_on_quench( + # TF shape + H_coil: float, + ri_coil: float, + ro_coil: float, + rm_coil: float, + ccl_length_coil: float, + theta1_coil: float, + # VV shape + H_vv: float, + ri_vv: float, + ro_vv: float, + rm_vv: float, + theta1_vv: float, + # TF properties + n_tf_coils: float, + n_tf_turn: float, + s_rp: float, + s_cc: float, + taud: float, + i_op: float, + # VV properties + d_vv: float, +) -> float: + """Generic model to calculate the Tresca stress of the + Vacuum Vessel (VV), experienced when the TF coil quenches. + + Author: Timothy Nunn, UKAEA + + The current center line (CCL) of a structure is an appoximation + of where the poloidal current of a structure acts. This model + considers how the current (self-)induced in the following structures + affects the Tresca stress on the VV (corresponding to the structure + index used in [1]): + + 0. the TF coil conductors + 1. the TF coil steel structure + 2. the VV + + :param H_coil: the maximum height of the TF coil CCL + :param ri_coil: the radius of the inboard edge of the TF coil CCL + :param ro_coil: the radius of the outboard edge of the TF coil CCL + :param rm_coil: the radius where the maximum height of the TF coil CCL occurs + :param ccl_length_coil: the length of the TF coil CCL + :param theta1_coil: the polar angle of the point at which one circular arc is + joined to another circular arc in the approximation to the coil CCL, + using an arbitrary origin of coordinates (Rc2, Zc2). + + :param H_vv: the maximum height of the VV CCL + :param ri_vv: the radius of the inboard edge of the VV CCL + :param ro_vv: the radius of the outboard edge of the VV CCL + :param rm_vv: the radius where the maximum height of the VV CCL occurs + :param theta1_vv: the polar angle of the point at which one circular arc is + joined to another circular arc in the approximation to the VV CCL, + using an arbitrary origin of coordinates (Rc2, Zc2). + + :param n_tf_coils: the number of TF coils + :param n_tf_turn: the number of turns per TF coil + :param s_rp: the cross-sectional area of the radial plates of the TF coil + :param s_cc: the cross-sectional area of the TF coil case + :param taud: the discharge time of the TF coil when quench occurs + :param i_op: the 'normal' operating current of the TF coil + + :param d_vv: the thickness of the vacuum vessel shell + + :returns: the maximum stress experienced by the vacuum vessel + + Notes + ----- + The theta1 quantity for the TF coil and VV is not very meaningful. The + impact of it of the inductance is rather small. Generally, the paper seems to + suggest the TF coil is between 40 and 60, as this is the range they calculate + the surrogates over. The thickness of the VV considers an ITER like design and + only the outer and inner shells that which act of conductuve structural material. + + References + ---------- + 1. ITOH, Yasuyuki & Utoh, Hiroyasu & SAKAMOTO, Yoshiteru & Hiwatari, Ryoji. (2020). + Empirical Formulas for Estimating Self and Mutual Inductances of Toroidal Field Coils and Structures. + Plasma and Fusion Research. 15. 1405078-1405078. 10.1585/pfr.15.1405078. + """ + # Convert angles into radians + theta1_vv_rad = np.pi * (theta1_vv / 180.0) + + # Poloidal loop resistance (PLR) in ohms + theta_vv = _theta_factor_integral(ro_vv, ri_vv, rm_vv, H_vv, theta1_vv_rad) + plr_coil = ((0.5 * ccl_length_coil) / (n_tf_coils * (s_cc + s_rp))) * 1e-6 + plr_vv = ((0.84 / d_vv) * theta_vv) * 1e-6 + + # relevant self-inductances in henry (H) + coil_structure_self_inductance = ( + (constants.rmu0 / np.pi) + * H_coil + * _inductance_factor(H_coil, ri_coil, ro_coil, rm_coil, theta1_coil) + ) + vv_self_inductance = ( + (constants.rmu0 / np.pi) + * H_vv + * _inductance_factor(H_vv, ri_vv, ro_vv, rm_vv, theta1_vv) + ) + + # s^-1 + lambda0 = 1 / taud + lambda1 = (plr_coil) / coil_structure_self_inductance + lambda2 = (plr_vv) / vv_self_inductance + + # approximate time at which the maximum force (and stress) will occur on the VV + tmaxforce = np.log((lambda0 + lambda1) / (2 * lambda0)) / (lambda1 - lambda0) + + i0 = i_op * np.exp(-lambda0 * tmaxforce) + i1 = ( + lambda0 + * n_tf_coils + * n_tf_turn + * i_op + * ( + (np.exp(-lambda1 * tmaxforce) - np.exp(-lambda0 * tmaxforce)) + / (lambda0 - lambda1) + ) + ) + i2 = (lambda1 / lambda2) * i1 + + a_vv = (ro_vv + ri_vv) / (ro_vv - ri_vv) + b_vvi = (constants.rmu0 * (n_tf_coils * n_tf_turn * i0 + i1 + (i2 / 2))) / ( + 2 * np.pi * ri_vv + ) + j_vvi = i2 / (2 * np.pi * d_vv * ri_vv) + + zeta = 1 + ((a_vv - 1) * np.log((a_vv + 1) / (a_vv - 1)) / (2 * a_vv)) + + return zeta * b_vvi * j_vvi * ri_vv + + +def _inductance_factor( + H: float, ri: float, ro: float, rm: float, theta1: float +) -> float: + """Calculates the inductance factor for a toroidal structure + using surrogate 2 #1866. + + Author: Timothy Nunn, UKAEA + + :param H: the maximum height of the structure + :param ri: the radius of the inboard side of the structure CCL + :param ro: the radius of the outboard side of the structure CCL + :param rm: the radius at which `H` occurs + :param theta1: the polar angle of the point at which one circular arc is + joined to another circular arc in the approximation to the structure CCL, + using an arbitrary origin of coordinates (Rc2, Zc2). + """ + # NOTE: the following properties are not those of the plasma but of + # the VV/coil structures + major_radius = (ro + ri) / 2 + minor_radius = (ro - ri) / 2 + + aspect_ratio = major_radius / minor_radius + triangularity = (major_radius - rm) / minor_radius + elongation = H / minor_radius + + return ( + 4.933 + + 0.03728 * elongation + + 0.06980 * triangularity + - 3.551 * aspect_ratio + + 0.7629 * aspect_ratio**2 + - 0.06298 * (theta1 / 90) + ) diff --git a/process/tf_coil.py b/process/tf_coil.py new file mode 100644 index 0000000000..206773a01e --- /dev/null +++ b/process/tf_coil.py @@ -0,0 +1,3709 @@ +import copy +import json + +import numba +import numpy as np + +from process import fortran as ft +from process import process_output as po +from process.build import Build +from process.fortran import ( + build_variables, + constants, + error_handling, + fwbs_variables, + global_variables, + pfcoil_variables, + physics_variables, + sctfcoil_module, + tfcoil_variables, +) +from process.fortran import build_variables as bv +from process.fortran import error_handling as eh +from process.fortran import fwbs_variables as fwbsv +from process.fortran import tfcoil_variables as tfv +from process.utilities.f2py_string_patch import f2py_compatible_to_string + +RMU0 = constants.rmu0 + + +class TFCoil: + """Calculates the parameters of a resistive TF coil system for a fusion power plant""" + + def __init__(self, build: Build): + """Initialise Fortran module variables.""" + self.outfile = ft.constants.nout # output file unit + self.iprint = 0 # switch for writing to output file (1=yes) + self.build = build + + def run(self, output): + """Run main tfcoil subroutine without outputting.""" + self.iprint = 0 + self.tf_global_geometry() + self.tf_current() + self.coilshap() + + if physics_variables.itart == 0 and tfcoil_variables.i_tf_shape == 1: + tfcoil_variables.tfind = self.tfcind( + build_variables.dr_tf_inboard, + tfcoil_variables.xarc, + tfcoil_variables.yarc, + ) + else: + tfcoil_variables.tfind = ( + (build_variables.hmax + build_variables.dr_tf_outboard) + * RMU0 + / constants.pi + * np.log( + build_variables.r_tf_outboard_mid / build_variables.r_tf_inboard_mid + ) + ) + + # Total TF coil stored magnetic energy [J] + sctfcoil_module.estotft = ( + 0.5e0 * tfcoil_variables.tfind * tfcoil_variables.c_tf_total**2 + ) + + # Total TF coil stored magnetic energy [Gigajoule] + tfcoil_variables.estotftgj = 1.0e-9 * sctfcoil_module.estotft + + self.tf_field_and_force() + + # Calculate TF coil areas and masses + self.tf_coil_area_and_masses() + + # Do stress calculations (writes the stress output) + if output: + tfcoil_variables.n_rad_per_layer = 500 + + try: + ( + sig_tf_r_max, + sig_tf_t_max, + sig_tf_z_max, + sig_tf_vmises_max, + s_shear_tf_peak, + deflect, + eyoung_axial, + eyoung_trans, + eyoung_wp_axial, + eyoung_wp_trans, + poisson_wp_trans, + radial_array, + s_shear_cea_tf_cond, + poisson_wp_axial, + sig_tf_r, + sig_tf_smeared_r, + sig_tf_smeared_t, + sig_tf_smeared_z, + sig_tf_t, + s_shear_tf, + sig_tf_vmises, + sig_tf_z, + str_tf_r, + str_tf_t, + str_tf_z, + n_radial_array, + n_tf_bucking, + tfcoil_variables.sig_tf_wp, + sig_tf_case, + sig_tf_cs_bucked, + str_wp, + casestr, + insstrain, + sig_tf_wp_av_z, + ) = self.stresscl( + int(tfcoil_variables.n_tf_stress_layers), + int(tfcoil_variables.n_rad_per_layer), + int(tfcoil_variables.n_tf_wp_layers), + int(tfcoil_variables.i_tf_bucking), + float(build_variables.r_tf_inboard_in), + build_variables.dr_bore, + build_variables.hmax, + pfcoil_variables.f_z_cs_tf_internal, + build_variables.dr_cs, + build_variables.i_tf_inside_cs, + build_variables.dr_tf_inboard, + build_variables.dr_cs_tf_gap, + pfcoil_variables.i_pf_conductor, + pfcoil_variables.j_cs_flat_top_end, + pfcoil_variables.j_cs_pulse_start, + pfcoil_variables.c_pf_coil_turn_peak_input, + pfcoil_variables.n_pf_coils_in_group, + pfcoil_variables.ld_ratio_cst, + pfcoil_variables.r_out_cst, + pfcoil_variables.f_a_cs_steel, + tfcoil_variables.eyoung_steel, + tfcoil_variables.poisson_steel, + tfcoil_variables.eyoung_cond_axial, + tfcoil_variables.poisson_cond_axial, + tfcoil_variables.eyoung_cond_trans, + tfcoil_variables.poisson_cond_trans, + tfcoil_variables.eyoung_ins, + tfcoil_variables.poisson_ins, + tfcoil_variables.thicndut, + tfcoil_variables.eyoung_copper, + tfcoil_variables.poisson_copper, + tfcoil_variables.i_tf_sup, + tfcoil_variables.eyoung_res_tf_buck, + sctfcoil_module.r_wp_inner, + sctfcoil_module.tan_theta_coil, + sctfcoil_module.theta_coil, + sctfcoil_module.r_wp_outer, + sctfcoil_module.a_tf_steel, + sctfcoil_module.a_case_front, + sctfcoil_module.a_case_nose, + tfcoil_variables.tfinsgap, + tfcoil_variables.tinstf, + tfcoil_variables.n_tf_turn, + int(tfcoil_variables.i_tf_turns_integer), + sctfcoil_module.t_cable, + sctfcoil_module.t_cable_radial, + tfcoil_variables.dhecoil, + tfcoil_variables.fcutfsu, + tfcoil_variables.thwcndut, + sctfcoil_module.t_lat_case_av, + sctfcoil_module.t_wp_toroidal_av, + sctfcoil_module.a_tf_ins, + tfcoil_variables.aswp, + tfcoil_variables.acond, + sctfcoil_module.awpc, + tfcoil_variables.eyoung_al, + tfcoil_variables.poisson_al, + tfcoil_variables.fcoolcp, + tfcoil_variables.n_tf_graded_layers, + tfcoil_variables.c_tf_total, + tfcoil_variables.casthi, + tfcoil_variables.i_tf_stress_model, + sctfcoil_module.vforce_inboard_tot, + tfcoil_variables.i_tf_tresca, + tfcoil_variables.acasetf, + tfcoil_variables.vforce, + tfcoil_variables.acndttf, + ) + + tfcoil_variables.sig_tf_case = ( + tfcoil_variables.sig_tf_case + if tfcoil_variables.sig_tf_case is None + else sig_tf_case + ) + + tfcoil_variables.sig_tf_cs_bucked = ( + tfcoil_variables.sig_tf_cs_bucked + if tfcoil_variables.sig_tf_cs_bucked is None + else sig_tf_cs_bucked + ) + + tfcoil_variables.str_wp = ( + tfcoil_variables.str_wp if tfcoil_variables.str_wp is None else str_wp + ) + + tfcoil_variables.casestr = ( + tfcoil_variables.casestr + if tfcoil_variables.casestr is None + else casestr + ) + + tfcoil_variables.insstrain = ( + tfcoil_variables.insstrain + if tfcoil_variables.insstrain is None + else insstrain + ) + + if output: + self.out_stress( + sig_tf_r_max, + sig_tf_t_max, + sig_tf_z_max, + sig_tf_vmises_max, + s_shear_tf_peak, + deflect, + eyoung_axial, + eyoung_trans, + eyoung_wp_axial, + eyoung_wp_trans, + poisson_wp_trans, + radial_array, + s_shear_cea_tf_cond, + poisson_wp_axial, + sig_tf_r, + sig_tf_smeared_r, + sig_tf_smeared_t, + sig_tf_smeared_z, + sig_tf_t, + s_shear_tf, + sig_tf_vmises, + sig_tf_z, + str_tf_r, + str_tf_t, + str_tf_z, + n_radial_array, + n_tf_bucking, + sig_tf_wp_av_z, + ) + except ValueError as e: + if e.args[1] == 245 and e.args[2] == 0: + error_handling.report_error(245) + tfcoil_variables.sig_tf_case = 0.0e0 + tfcoil_variables.sig_tf_wp = 0.0e0 + + def output(self): + """Run main tfcoil subroutine and write output.""" + self.iprint = 1 + self.tfcoil(output=bool(self.iprint)) + + def tf_global_geometry(self): + """Subroutine for calculating the TF coil geometry + This includes: + - Overall geometry of coil (radii and toroidal planes area) + - Winding Pack NOT included + """ + + sctfcoil_module.theta_coil = np.pi / tfcoil_variables.n_tf_coils + sctfcoil_module.tan_theta_coil = np.tan(sctfcoil_module.theta_coil) + + # TF coil inboard legs mid-plane cross-section area (WP + casing ) [m2] + if tfcoil_variables.i_tf_case_geom == 0: + # Circular front case + tfcoil_variables.tfareain = np.pi * ( + build_variables.r_tf_inboard_out**2 - build_variables.r_tf_inboard_in**2 + ) + else: + # Straight front case + tfcoil_variables.tfareain = ( + tfcoil_variables.n_tf_coils + * np.sin(sctfcoil_module.theta_coil) + * np.cos(sctfcoil_module.theta_coil) + * build_variables.r_tf_inboard_out**2 + - np.pi * build_variables.r_tf_inboard_in**2 + ) + + # Vertical distance from the midplane to the top of the tapered section [m] + if physics_variables.itart == 1: + sctfcoil_module.h_cp_top = ( + build_variables.z_plasma_xpoint_upper + tfcoil_variables.dztop + ) + # --- + + # Outer leg geometry + # --- + # Mid-plane inner/out radial position of the TF coil outer leg [m] + + sctfcoil_module.r_tf_outboard_in = ( + build_variables.r_tf_outboard_mid - build_variables.dr_tf_outboard * 0.5e0 + ) + sctfcoil_module.r_tf_outboard_out = ( + build_variables.r_tf_outboard_mid + build_variables.dr_tf_outboard * 0.5e0 + ) + + # TF coil width in toroidal direction at inboard leg outer edge [m] + # *** + # Sliding joints geometry + if physics_variables.itart == 1 and tfcoil_variables.i_tf_sup != 1: + tfcoil_variables.tftort = ( + 2.0e0 * build_variables.r_cp_top * np.sin(sctfcoil_module.theta_coil) + ) + + # Default thickness, initially written for DEMO SC magnets + elif physics_variables.itart == 1 and tfcoil_variables.i_tf_sup == 1: + tfcoil_variables.tftort = ( + 2.0e0 + * build_variables.r_tf_inboard_out + * np.sin(sctfcoil_module.theta_coil) + ) + else: + tfcoil_variables.tftort = ( + 2.0e0 + * build_variables.r_tf_inboard_out + * np.sin(sctfcoil_module.theta_coil) + ) + + # Area of rectangular cross-section TF outboard leg [m2] + tfcoil_variables.a_tf_leg_outboard = ( + tfcoil_variables.tftort * build_variables.dr_tf_outboard + ) + # --- + + def tf_current(self): + """ + Calculation of the maximum B field and the corresponding TF current + """ + if tfcoil_variables.casthi_is_fraction: + tfcoil_variables.casthi = ( + tfcoil_variables.casthi_fraction * build_variables.dr_tf_inboard + ) + + # Case thickness of side wall [m] + if tfcoil_variables.tfc_sidewall_is_fraction: + tfcoil_variables.casths = ( + tfcoil_variables.casths_fraction + * (build_variables.r_tf_inboard_in + tfcoil_variables.thkcas) + * np.tan(np.pi / tfcoil_variables.n_tf_coils) + ) + + # Radial position of peak toroidal field [m] + if tfcoil_variables.i_tf_sup == 1: + # SC : conservative assumption as the radius is calculated with the + # WP radial distances defined at the TF middle (cos) + tfcoil_variables.rbmax = ( + build_variables.r_tf_inboard_out * np.cos(sctfcoil_module.theta_coil) + - tfcoil_variables.casthi + - tfcoil_variables.tinstf + - tfcoil_variables.tfinsgap + ) + else: + # Resistive coils : No approx necessary as the symmetry is cylindrical + # The turn insulation th (tfcoil_variables.thicndut) is also subtracted too here + tfcoil_variables.rbmax = ( + build_variables.r_tf_inboard_out + - tfcoil_variables.casthi + - tfcoil_variables.thicndut + - tfcoil_variables.tinstf + ) + + # Calculation of the maximum B field on the magnet [T] + tfcoil_variables.bmaxtf = ( + physics_variables.bt * physics_variables.rmajor / tfcoil_variables.rbmax + ) + + # Total current in TF coils [A] + # rem SK : ritcf is no longer an input + tfcoil_variables.c_tf_total = ( + tfcoil_variables.bmaxtf * tfcoil_variables.rbmax * 5.0e6 + ) + + # Current per TF coil [A] + sctfcoil_module.tfc_current = ( + tfcoil_variables.c_tf_total / tfcoil_variables.n_tf_coils + ) + + # Global inboard leg average current in TF coils [A/m2] + tfcoil_variables.oacdcp = ( + tfcoil_variables.c_tf_total / tfcoil_variables.tfareain + ) + + def coilshap(self): + """Calculates the TF coil shape + Calculates the shape of the INSIDE of the TF coil. The coil is + approximated by a straight inboard section and four elliptical arcs + This is a totally ad hoc model, with no physics or engineering basis. + + The referenced equations can be found in draft/unpublished document + attached in GitLab to issue #1328. + """ + FSTRAIGHT = 0.6 + if tfcoil_variables.i_tf_shape == 1 and physics_variables.itart == 0: + # PROCESS D-shape parametrisation + + # X position of the arcs, eq(21) + # The tfcoil_variables.xarc/tfcoil_variables.yarc are defined in the INSIDE part of the TF + tfcoil_variables.xarc[0] = build_variables.r_tf_inboard_out + tfcoil_variables.xarc[1] = ( + physics_variables.rmajor - 0.2e0 * physics_variables.rminor + ) + tfcoil_variables.xarc[2] = sctfcoil_module.r_tf_outboard_in + tfcoil_variables.xarc[3] = tfcoil_variables.xarc[1] + tfcoil_variables.xarc[4] = tfcoil_variables.xarc[0] + + # Height of straight section as a fraction of the coil inner height + if physics_variables.i_single_null == 0: + # Double null + tfcoil_variables.yarc[0] = FSTRAIGHT * build_variables.hmax + tfcoil_variables.yarc[1] = build_variables.hmax + tfcoil_variables.yarc[2] = 0 + tfcoil_variables.yarc[3] = -build_variables.hmax + tfcoil_variables.yarc[4] = -FSTRAIGHT * build_variables.hmax + else: + # Single null + tfcoil_variables.yarc[0] = FSTRAIGHT * ( + build_variables.hpfu - build_variables.dr_tf_inboard + ) + tfcoil_variables.yarc[1] = ( + build_variables.hpfu - build_variables.dr_tf_inboard + ) + tfcoil_variables.yarc[2] = 0 + tfcoil_variables.yarc[3] = -build_variables.hmax + tfcoil_variables.yarc[4] = -FSTRAIGHT * build_variables.hmax + + # Horizontal and vertical radii of inside edge of TF coil + # Arcs are numbered clockwise: + # 1=upper inboard, 2=upper outboard, 3=lower ouboard, 4=lower inboard + # 'len_tf_coil' is the length of the coil midline. + tfcoil_variables.len_tf_coil = ( + tfcoil_variables.yarc[0] - tfcoil_variables.yarc[4] + ) + for ii in range(4): + tfcoil_variables.tfa[ii] = abs( + tfcoil_variables.xarc[ii + 1] - tfcoil_variables.xarc[ii] + ) + tfcoil_variables.tfb[ii] = abs( + tfcoil_variables.yarc[ii + 1] - tfcoil_variables.yarc[ii] + ) + # Radii and length of midline of coil segments + aa = tfcoil_variables.tfa[ii] + 0.5e0 * build_variables.dr_tf_inboard + bb = tfcoil_variables.tfb[ii] + 0.5e0 * build_variables.dr_tf_inboard + tfcoil_variables.len_tf_coil = ( + tfcoil_variables.len_tf_coil + 0.25e0 * self.circumference(aa, bb) + ) + # note: final tfcoil_variables.len_tf_coil includes inboard leg length; eq(22) + + # Centrepost with D-shaped + # --- + elif tfcoil_variables.i_tf_shape == 1 and physics_variables.itart == 1: + # X position of the arcs, eq(23) and text before it + tfcoil_variables.yarc[0] = build_variables.r_cp_top + tfcoil_variables.yarc[1] = ( + physics_variables.rmajor - 0.2e0 * physics_variables.rminor + ) + tfcoil_variables.yarc[2] = sctfcoil_module.r_tf_outboard_in + tfcoil_variables.yarc[3] = tfcoil_variables.xarc[1] + tfcoil_variables.yarc[4] = tfcoil_variables.xarc[0] + + # Double null, eq(23) and text before it + tfcoil_variables.yarc[0] = ( + build_variables.hpfu - build_variables.dr_tf_inboard + ) + tfcoil_variables.yarc[1] = ( + build_variables.hpfu - build_variables.dr_tf_inboard + ) + tfcoil_variables.yarc[2] = 0 + tfcoil_variables.yarc[3] = -build_variables.hmax + tfcoil_variables.yarc[4] = -build_variables.hmax + + # TF middle circumference + tfcoil_variables.len_tf_coil = 2 * ( + tfcoil_variables.xarc[1] - tfcoil_variables.xarc[0] + ) + + for ii in range(1, 3): + tfcoil_variables.tfa[ii] = abs( + tfcoil_variables.xarc[ii + 1] - tfcoil_variables.xarc[ii] + ) + tfcoil_variables.tfb[ii] = abs( + tfcoil_variables.yarc[ii + 1] - tfcoil_variables.yarc[ii] + ) + + # Radii and length of midline of coil segments + aa = tfcoil_variables.tfa[ii] + 0.5e0 * build_variables.dr_tf_outboard + bb = tfcoil_variables.tfb[ii] + 0.5e0 * build_variables.dr_tf_outboard + tfcoil_variables.len_tf_coil = ( + tfcoil_variables.len_tf_coil + 0.25e0 * self.circumference(aa, bb) + ) + # IMPORTANT : THE CENTREPOST LENGTH IS NOT INCLUDED IN len_tf_coil FOR TART; eq(24) + # --- + + # Picture frame coil + # --- + elif tfcoil_variables.i_tf_shape == 2: + # X position of the arcs + if physics_variables.itart == 0: + tfcoil_variables.xarc[0] = build_variables.r_tf_inboard_out + if physics_variables.itart == 1: + tfcoil_variables.xarc[0] = build_variables.r_cp_top + tfcoil_variables.xarc[1] = sctfcoil_module.r_tf_outboard_in + tfcoil_variables.xarc[2] = tfcoil_variables.xarc[1] + tfcoil_variables.xarc[3] = tfcoil_variables.xarc[1] + tfcoil_variables.xarc[4] = tfcoil_variables.xarc[0] + + # Y position of the arcs + tfcoil_variables.yarc[0] = ( + build_variables.hpfu - build_variables.dr_tf_inboard + ) + tfcoil_variables.yarc[1] = ( + build_variables.hpfu - build_variables.dr_tf_inboard + ) + tfcoil_variables.yarc[2] = 0 + tfcoil_variables.yarc[3] = -build_variables.hmax + tfcoil_variables.yarc[4] = -build_variables.hmax + + # TF middle circumference + # IMPORTANT : THE CENTREPOST LENGTH IS NOT INCLUDED IN len_tf_coil FOR TART + if physics_variables.itart == 0: + tfcoil_variables.len_tf_coil = 2.0e0 * ( + 2.0e0 * build_variables.hmax + + build_variables.dr_tf_inboard + + build_variables.r_tf_outboard_mid + - build_variables.r_tf_inboard_mid + ) # eq(25) + elif physics_variables.itart == 1: + tfcoil_variables.len_tf_coil = ( + build_variables.hmax + + build_variables.hpfu + + 2.0e0 + * (build_variables.r_tf_outboard_mid - build_variables.r_cp_top) + ) # eq(26) + + @staticmethod + def circumference(aaa, bbb): + """Calculate ellipse arc circumference using Ramanujan approximation (m) + See https://www.johndcook.com/blog/2013/05/05/ramanujan-circumference-ellipse/ + for a discussion of the precision of the formula + + An ellipse has the following formula: (x/a)² + (y/b)² = 1 + + :param aaa: the value of a in the formula of the ellipse. + :type aaa: float + + :param bbb: the value of b in the formula of the ellipse. + :type bbb: float + + :returns: an approximation of the circumference of the ellipse + :rtype: float + """ + hh = (aaa - bbb) ** 2 / (aaa + bbb) ** 2 + return ( + np.pi + * (aaa + bbb) + * (1.0e0 + (3.0e0 * hh) / (10.0e0 + np.sqrt(4.0e0 - 3.0e0 * hh))) + ) + + def cntrpst(self): + """ + Evaluates the properties of a TART centrepost + author: P J Knight, CCFE, Culham Science Centre + outfile : input integer : output file unit + iprint : input integer : switch for writing to output file (1=yes) + This subroutine evaluates the parameters of the centrepost for a + tight aspect ratio tokamak. The centrepost is assumed to be tapered, + i.e. narrowest on the midplane (z=0). + """ + + # Temperature margin used in calculations (K) + tmarg = 10.0e0 + + # Number of integral step used for the coolant temperature rise + n_tcool_it = 20 + + # Coolant channels: + acool = tfv.a_cp_cool * tfv.n_tf_coils # Cooling cross-sectional area + dcool = 2.0e0 * tfv.rcool # Diameter + lcool = 2.0e0 * (bv.hmax + bv.dr_tf_outboard) # Length + tfv.ncool = acool / (constants.pi * tfv.rcool**2) # Number + + # Average conductor cross-sectional area to cool (with cooling area) + acpav = 0.5e0 * tfv.vol_cond_cp / (bv.hmax + bv.dr_tf_outboard) + acool + ro = (acpav / (constants.pi * tfv.ncool)) ** 0.5 + + # Inner legs total heating power (to be removed by coolant) + ptot = tfv.p_cp_resistive + fwbsv.pnuc_cp_tf * 1.0e6 + + # Temperature calculations + # ------------------------- + # Temperature rise in coolant (inlet to outlet) + # ********************************************** + # Water coollant + # -------------- + if tfv.i_tf_sup == 0: + # Water coolant physical properties + coolant_density = constants.denh2o + coolant_cp = constants.cph2o + coolant_visco = constants.muh2o + coolant_th_cond = constants.kh2o + + # Mass flow rate [kg/s] + cool_mass_flow = acool * coolant_density * tfv.vcool + + # Water temperature rise + tfv.dtiocool = ptot / (cool_mass_flow * coolant_cp) + + # Constant coolant velocity + vcool_max = tfv.vcool + # -------------- + + # Helium coolant + # -------------- + elif tfv.i_tf_sup == 2: + # Inlet coolant density [kg/m3] + coolant_density = self.he_density(tfv.tcoolin) + + # Mass flow rate [kg/s] + cool_mass_flow = acool * coolant_density * tfv.vcool + + # Infinitesimal power deposition used in the integral + dptot = ptot / n_tcool_it + + tcool_calc = copy.copy(tfv.tcoolin) # K + for _i in range(n_tcool_it): + # Thermal capacity Cp + coolant_cp = self.he_cp(tcool_calc) + + # Temperature infinitesimal increase + tcool_calc += dptot / (cool_mass_flow * coolant_cp) + + # Outlet coolant density (minimal coolant density value) + coolant_density = self.he_density(tcool_calc) + + # Maxium coolant velocity + vcool_max = cool_mass_flow / (acool * coolant_density) + + # Getting the global in-outlet temperature increase + tfv.dtiocool = tcool_calc - tfv.tcoolin + # -------------- + + # Average coolant temperature + tcool_av = tfv.tcoolin + 0.5e0 * tfv.dtiocool + # ********************************************** + + # Film temperature rise + # ********************* + # Rem : The helium cooling properties are calculated using the outlet ones + # this is not an exact approximation for average temperature rise + + # Helium viscosity + if tfv.i_tf_sup == 2: + coolant_visco = self.he_visco(tcool_av) + + # Reynolds number + reyn = coolant_density * tfv.vcool * dcool / coolant_visco + + # Helium thermal conductivity [W/(m.K)] + if tfv.i_tf_sup == 2: + coolant_th_cond = self.he_th_cond(tcool_av) + + # Prandlt number + prndtl = coolant_cp * coolant_visco / coolant_th_cond + + # Film temperature difference calculations + # Originally prandtl was prndtl**0.3e0 but this is incorrect as from + # Dittus-Boelter correlation where the fluid is being heated it should be as below + nuselt = 0.023e0 * reyn**0.8e0 * prndtl**0.4e0 + h = nuselt * coolant_th_cond / dcool + dtfilmav = ptot / (h * 2.0e0 * constants.pi * tfv.rcool * tfv.ncool * lcool) + + # Average film temperature (in contact with te conductor) + tcool_film = tcool_av + dtfilmav + # ********************* + + # Temperature rise in conductor + # ------------------------------ + # Conductor thermal conductivity + # ****** + # Copper conductor + if tfv.i_tf_sup == 0: + conductor_th_cond = constants.k_copper + + # Aluminium + elif tfv.i_tf_sup == 2: + conductor_th_cond = self.al_th_cond(tcool_film) + # ****** + + # Average temperature rise : To be changed with Garry Voss' better documented formula ? + dtcncpav = ( + (ptot / tfv.vol_cond_cp) + / (2.0e0 * conductor_th_cond * (ro**2 - tfv.rcool**2)) + * ( + ro**2 * tfv.rcool**2 + - 0.25e0 * tfv.rcool**4 + - 0.75e0 * ro**4 + + ro**4 * np.log(ro / tfv.rcool) + ) + ) + + # Peak temperature rise : To be changed with Garry Voss' better documented formula ? + dtconcpmx = ( + (ptot / tfv.vol_cond_cp) + / (2.0e0 * conductor_th_cond) + * ((tfv.rcool**2 - ro**2) / 2.0e0 + ro**2 * np.log(ro / tfv.rcool)) + ) + + # If the average conductor temperature difference is negative, set it to 0 + if dtcncpav < 0.0e0: + eh.report_error(249) + dtcncpav = 0.0e0 + + # If the average conductor temperature difference is negative, set it to 0 + if dtconcpmx < 0.0e0: + eh.report_error(250) + dtconcpmx = 0.0e0 + + # Average conductor temperature + tfv.tcpav2 = tfv.tcoolin + dtcncpav + dtfilmav + 0.5e0 * tfv.dtiocool + + # Peak wall temperature + tfv.tcpmax = tfv.tcoolin + tfv.dtiocool + dtfilmav + dtconcpmx + tcoolmx = tfv.tcoolin + tfv.dtiocool + dtfilmav + # ------------------------- + + # Thermal hydraulics: friction factor from Z. Olujic, Chemical + # Engineering, Dec. 1981, p. 91 + roughrat = 4.6e-5 / dcool + fricfac = ( + 1.0e0 + / ( + -2.0e0 + * np.log10( + roughrat / 3.7e0 + - 5.02e0 / reyn * np.log10(roughrat / 3.7e0 + 14.5e0 / reyn) + ) + ) + ** 2 + ) + + # Pumping efficiency + if tfv.i_tf_sup == 0: # Water cooled + tfv.etapump = 0.8e0 + elif tfv.i_tf_sup == 2: # Cryogenic helium + tfv.etapump = 0.6e0 + + # Pressure drop calculation + dpres = fricfac * (lcool / dcool) * coolant_density * 0.5e0 * tfv.vcool**2 + tfv.ppump = dpres * acool * tfv.vcool / tfv.etapump + + # Critical pressure in saturation pressure calculations (Pa) + pcrt = 2.24e7 + + # Saturation pressure + # Ref : Keenan, Keyes, Hill, Moore, steam tables, Wiley & Sons, 1969 + # Rem 1 : ONLY VALID FOR WATER ! + # Rem 2 : Not used anywhere else in the code ... + tclmx = tcoolmx + tmarg + tclmxs = min(tclmx, 374.0e0) + fc = 0.65e0 - 0.01e0 * tclmxs + sum_ = ( + -741.9242e0 + - 29.721e0 * fc + - 11.55286e0 * fc**2 + - 0.8685635e0 * fc**3 + + 0.1094098e0 * fc**4 + + 0.439993e0 * fc**5 + + 0.2520658e0 * fc**6 + + 0.0518684e0 * fc**7 + ) + psat = pcrt * np.exp(0.01e0 / (tclmxs + 273.0e0) * (374.0e0 - tclmxs) * sum_) + presin = psat + dpres + + # Output section + if self.iprint == 1: + po.oheadr(self.outfile, "Centrepost Coolant Parameters") + po.ovarre( + self.outfile, "Centrepost coolant fraction", "(fcoolcp)", tfv.fcoolcp + ) + po.ovarre( + self.outfile, "Average coolant channel diameter (m)", "(dcool)", dcool + ) + po.ovarre(self.outfile, "Coolant channel length (m)", "(lcool)", lcool) + po.ovarre( + self.outfile, "Inlet coolant flow speed (m/s)", "(vcool)", tfv.vcool + ) + po.ovarre( + self.outfile, + "Outlet coolant flow speed (m/s)", + "(vcool_max)", + vcool_max, + ) + po.ovarre( + self.outfile, + "Coolant mass flow rate (kg/s)", + "(cool_mass_flow)", + cool_mass_flow, + ) + po.ovarre(self.outfile, "Number of coolant tubes", "(ncool)", tfv.ncool) + po.ovarre(self.outfile, "Reynolds number", "(reyn)", reyn) + po.ovarre(self.outfile, "Prandtl number", "(prndtl)", prndtl) + po.ovarre(self.outfile, "Nusselt number", "(nuselt)", nuselt) + + po.osubhd(self.outfile, "Resistive Heating :") + po.ovarre( + self.outfile, + "Average conductor resistivity (ohm.m)", + "(rho_cp)", + tfv.rho_cp, + ) + po.ovarre( + self.outfile, + "Resistive heating (MW)", + "(p_cp_resistive/1.0e6)", + tfv.p_cp_resistive / 1.0e6, + ) + po.ovarre( + self.outfile, "Nuclear heating (MW)", "(pnuc_cp_tf)", fwbsv.pnuc_cp_tf + ) + po.ovarre(self.outfile, "Total heating (MW)", "(ptot/1.0e6)", ptot / 1.0e6) + + po.osubhd(self.outfile, "Temperatures :") + po.ovarre( + self.outfile, + "Input coolant temperature (K)", + "(tfv.tcoolin)", + tfv.tcoolin, + ) + po.ovarre( + self.outfile, + "Input-output coolant temperature rise (K)", + "(dtiocool)", + tfv.dtiocool, + ) + po.ovarre(self.outfile, "Film temperature rise (K)", "(dtfilmav)", dtfilmav) + po.ovarre( + self.outfile, + "Average temp gradient in conductor (K/m)", + "(dtcncpav)", + dtcncpav, + ) + po.ovarre( + self.outfile, + "Average centrepost temperature (K)", + "(tcpav2)", + tfv.tcpav2, + ) + po.ovarre( + self.outfile, "Peak centrepost temperature (K)", "(tcpmax)", tfv.tcpmax + ) + + po.osubhd(self.outfile, "Pump Power :") + po.ovarre(self.outfile, "Coolant pressure drop (Pa)", "(dpres)", dpres) + if tfv.i_tf_sup == 0: # Saturation pressure calculated with Water data ... + po.ovarre( + self.outfile, "Coolant inlet pressure (Pa)", "(presin)", presin + ) + + po.ovarre(self.outfile, "Pump power (W)", "(ppump)", tfv.ppump) + + def tf_field_and_force(self): + """ + Calculate the TF coil field, force and VV quench consideration, and the resistive magnets resistance/volume + """ + + # Outer/inner WP radius removing the ground insulation layer and the insertion gap [m] + if tfcoil_variables.i_tf_sup == 1: + r_out_wp = ( + sctfcoil_module.r_wp_outer + - tfcoil_variables.tinstf + - tfcoil_variables.tfinsgap + ) + r_in_wp = ( + sctfcoil_module.r_wp_inner + + tfcoil_variables.tinstf + + tfcoil_variables.tfinsgap + ) + else: + r_out_wp = sctfcoil_module.r_wp_outer - tfcoil_variables.tinstf + r_in_wp = sctfcoil_module.r_wp_inner + tfcoil_variables.tinstf + + # Associated WP thickness + dr_wp = r_out_wp - r_in_wp + + # In plane forces + # --- + # Centering force = net inwards radial force per meters per TF coil [N/m] + tfcoil_variables.cforce = ( + 0.5e0 + * tfcoil_variables.bmaxtf + * tfcoil_variables.c_tf_total + / tfcoil_variables.n_tf_coils + ) + + # Vertical force per coil [N] + # *** + # Rem : this force does not depends on the TF shape or the presence of + # sliding joints, the in/outboard vertical tension repartition is + # -# + # Ouboard leg WP plasma side radius without ground insulation/insertion gat [m] + if tfcoil_variables.i_tf_sup == 1: + r_in_outwp = ( + sctfcoil_module.r_tf_outboard_in + + tfcoil_variables.casthi + + tfcoil_variables.tinstf + + tfcoil_variables.tfinsgap + ) + else: + r_in_outwp = sctfcoil_module.r_tf_outboard_in + tfcoil_variables.tinstf + + # If the TF coil has no dr_bore it would induce division by 0. + # In this situation, the dr_bore radius is set to a very small value : 1.0e-9 m + if abs(r_in_wp) < np.finfo(float(r_in_wp)).eps: + r_in_wp = 1.0e-9 + + # May the force be with you + vforce_tot = ( + 0.5e0 + * ( + physics_variables.bt + * physics_variables.rmajor + * tfcoil_variables.c_tf_total + ) + / (tfcoil_variables.n_tf_coils * dr_wp**2) + * ( + r_out_wp**2 * np.log(r_out_wp / r_in_wp) + + r_in_outwp**2 * np.log((r_in_outwp + dr_wp) / r_in_outwp) + + dr_wp**2 * np.log((r_in_outwp + dr_wp) / r_in_wp) + - dr_wp * (r_out_wp + r_in_outwp) + + 2.0e0 + * dr_wp + * ( + r_out_wp * np.log(r_in_wp / r_out_wp) + + r_in_outwp * np.log((r_in_outwp + dr_wp) / r_in_outwp) + ) + ) + ) + + # Case of a centrepost (physics_variables.itart == 1) with sliding joints (the CP vertical are separated from the leg ones) + # Rem SK : casing/insulation thickness not subtracted as part of the CP is genuinely connected to the legs.. + if physics_variables.itart == 1 and tfcoil_variables.i_cp_joints == 1: + # CP vertical tension [N] + tfcoil_variables.vforce = ( + 0.25e0 + * ( + physics_variables.bt + * physics_variables.rmajor + * tfcoil_variables.c_tf_total + ) + / (tfcoil_variables.n_tf_coils * dr_wp**2) + * ( + 2.0e0 * r_out_wp**2 * np.log(r_out_wp / r_in_wp) + + 2.0e0 * dr_wp**2 * np.log(build_variables.r_cp_top / r_in_wp) + + 3.0e0 * dr_wp**2 + - 2.0e0 * dr_wp * r_out_wp + + 4.0e0 * dr_wp * r_out_wp * np.log(r_in_wp / r_out_wp) + ) + ) + + # Vertical tension applied on the outer leg [N] + tfcoil_variables.vforce_outboard = vforce_tot - tfcoil_variables.vforce + + # Inboard vertical tension fraction + tfcoil_variables.f_vforce_inboard = tfcoil_variables.vforce / vforce_tot + + # Case of TF without joints or with clamped joints vertical tension + else: + # Inboard vertical tension [N] + tfcoil_variables.vforce = tfcoil_variables.f_vforce_inboard * vforce_tot + + # Ouboard vertical tension [N] + tfcoil_variables.vforce_outboard = tfcoil_variables.vforce * ( + (1.0e0 / tfcoil_variables.f_vforce_inboard) - 1.0e0 + ) + + # *** + + # Total vertical force + sctfcoil_module.vforce_inboard_tot = ( + tfcoil_variables.vforce * tfcoil_variables.n_tf_coils + ) + + @staticmethod + def he_density(temp: float) -> float: + """Author : S. Kahn + Subroutine calculating temperature dependent helium density at 100 bar + from fit using the following data, valid in [4-50] K + Ref : R.D. McCarty, Adv. Cryo. Eng., 1990, 35, 1465-1475. + + :param temp: Helium temperature [K] + :type temp: float + + :returns density: Heliyn density [kg/m3] + :type density: float + """ + + # Fit range validation + if temp < 4.0e0 or temp > 50.0e0: + eh.fdiags[0] = temp + eh.report_error(257) + + # Oder 3 polynomial fit + if temp < 29.5e0: + density = ( + 217.753831e0 + - 1.66564525e0 * temp + - 0.160654724e0 * temp**2 + + 0.00339003258e0 * temp**3 + ) + + # Linear interpolation between the fits to avoid discontinuity + elif temp < 30.5e0: + density = 231.40661479377616e0 - 3.917589985552496e0 * temp + + # Oder 2 polynomial fit + else: + density = 212.485251e0 - 4.18059786e0 * temp + 0.0289632937e0 * temp**2 + + return density + + @staticmethod + def he_cp(temp: float) -> float: + """Author : S. Kahn + Subroutine calculating temperature dependent thermal capacity at + constant pressures at 100 Bar from fit using the following data + valid in [4-50] K + Ref : R.D. McCarty, Adv. Cryo. Eng., 1990, 35, 1465-1475. + + :param temp: Helium temperature [K] + :type temp: float + + :return cp: Themal capacity at constant pressure [K/(kg.K)] + :type cp: float + """ + + # Fit range validation + if temp < 4.0e0 or temp > 50.0e0: + eh.fdiags[0] = temp + eh.report_error(257) + + # Order 3 polynomial fit in [4-30] K on the dimenion [K/(g.K)] + if temp < 29.5e0: + cp = ( + -0.834218557e0 + + 0.637079569e0 * temp + - 0.0208839696e0 * temp**2 + + 0.000233433748e0 * temp**3 + ) + + # Linear interpolation between the fits to avoid discontinuity + elif ( + temp < 30.5e0 + ): # Linear interpolation between the fits to avoid discontinuity + cp = 4.924018467550791e0 + 0.028953709588498633e0 * temp + + # Linear fit in [30-60] K on the dimenion [K/(g.K)] + else: + cp = 6.11883125e0 - 0.01022048e0 * temp + + # conversion to [K/(kg.K)] and return + return cp * 1.0e3 + + @staticmethod + def he_visco(temp: float) -> float: + """Author : S. Kahn + Subroutine calculating temperature dependent He viscosity at 100 Bar + from fit using the following data, valid in [4-50] K + Ref : V.D. Arp,; R.D. McCarty ; Friend, D.G., Technical Note 1334, National + Institute of Standards and Technology, Boulder, CO, 1998, 0. + + :param temp: Helium temperature [K] + :type temp: float + + :return visco: Themal capacity at constant pressure [Pa.s] + :type visco: float + """ + + if temp < 4.0e0 or temp > 50.0e0: + eh.fdiags[0] = temp + eh.report_error(257) + + # Order 4 polynomial exponential fit in [4-25] K + if temp < 22.5e0: + visco = np.exp( + -9.19688182e0 + - 4.83007225e-1 * temp + + 3.47720002e-2 * temp**2 + - 1.17501538e-3 * temp**3 + + 1.54218249e-5 * temp**4 + ) + + # Linear interpolation between the fits to avoid discontinuity + elif temp < 27.5e0: + visco = 6.708587487790973e-6 + 5.776427353055518e-9 * temp + + # Linear fit in [25-60] K + else: + visco = 5.41565319e-6 + 5.279222e-8 * temp + + return visco + + @staticmethod + def he_th_cond(temp: float) -> float: + """Author : S. Kahn + Subroutine calculating temperature dependent He thermal conductivity + at 100 Bar from fit using the following data, valid in [4-50] K + Ref : B.A. Hands B.A., Cryogenics, 1981, 21, 12, 697-703. + + :param temp: Helium temperature [K] + :type temp: float + + :return th_cond: Themal conductivity [W/(m.K)] + :type th_cond: float + """ + + # Fit range validation + if temp < 4.0e0 or temp > 50.0e0: + eh.fdiags[0] = temp + eh.report_error(257) + + # Order 4 polynomial fit + if temp < 24.0e0: + th_cond = ( + -7.56066334e-3 + + 1.62626819e-2 * temp + - 1.3633619e-3 * temp**2 + + 4.84227752e-5 * temp**3 + - 6.31264281e-7 * temp**4 + ) + + # Linear interpolation between the fits to avoid discontinuity + elif temp < 25.0e0: + th_cond = 0.05858194642349288e0 - 5.706361831471496e-5 * temp + + # Order 2 polynomial fit + elif temp < 50.0e0: + th_cond = ( + 0.0731268577e0 + - 0.0013826223e0 * temp + + 3.55551245e-5 * temp**2 + - 2.32185411e-7 * temp**3 + ) + + # Linear interpolation between the fits to avoid discontinuity + elif temp < 51.0e0: + th_cond = 4.450475632499988e-2 + 3.871124250000024e-4 * temp + + # Linear fit + else: + th_cond = 0.04235676e0 + 0.00042923e0 * temp + + return th_cond + + @staticmethod + def al_th_cond(temp: float) -> float: + """Author : S. Kahn + Subroutine calculating temperature dependent Al thermal conductivity + + :param temp: Helium temperature [K] + :type temp: float + + :return th_cond: Themal conductivity [W/(m.K)] + :type th_cond: float + """ + + # Fiting range verification + if temp < 15.0e0 or temp > 150.0e0: + eh.fdiags[0] = temp + eh.report_error(258) + + # fit 15 < T < 60 K (order 3 poly) + if temp < 60.0e0: + th_cond = ( + 16332.2073e0 + - 776.91775e0 * temp + + 13.405688e0 * temp**2 + - 8.01e-02 * temp**3 + ) + + # Linear interpolation between the fits to avoid discontinuity + elif temp < 70.0e0: + th_cond = 1587.9108966527328e0 - 15.19819661087886e0 * temp + + # fit 70 < T < 150 K (order 2 poly) + elif temp < 150.0e0: + th_cond = 1782.77406e0 - 24.7778504e0 * temp + 9.70842050e-2 * temp**2 + + # constant value after that set with the fit upper limit to avoid discontinuities + else: + th_cond = 250.4911087866094e0 + + return th_cond + + @staticmethod + @numba.njit(cache=True) + def tfcind(tfthk, xarc, yarc): + """Calculates the self inductance of a TF coil + This routine calculates the self inductance of a TF coil + approximated by a straight inboard section and two elliptical arcs. + The inductance of the TFC (considered as a single axisymmetric turn) + is calculated by numerical integration over the cross-sectional area. + The contribution from the cross-sectional area of the + coil itself is calculated by taking the field as B(r)/2. + The field in the dr_bore is calculated for unit current. + Top/bottom symmetry is assumed. + + :param tfthk: TF coil thickness (m) + :type tfthk: float + """ + NINTERVALS = 100 + + # Integrate over the whole TF area, including the coil thickness. + x0 = xarc[1] + y0 = yarc[1] + + # Minor and major radii of the inside and outside perimeters of the the + # Inboard leg and arc. + # Average the upper and lower halves, which are different in the + # single null case + ai = xarc[1] - xarc[0] + bi = (yarc[1] - yarc[3]) / 2.0e0 - yarc[0] + ao = ai + tfthk + bo = bi + tfthk + # Interval used for integration + dr = ao / NINTERVALS + # Start both integrals from the centre-point where the arcs join. + # Initialise major radius + r = x0 - dr / 2.0e0 + + tfind = 0 + + for _ in range(NINTERVALS): + # Field in the dr_bore for unit current + b = RMU0 / (2.0e0 * np.pi * r) + # Find out if there is a dr_bore + if x0 - r < ai: + h_bore = y0 + bi * np.sqrt(1 - ((r - x0) / ai) ** 2) + h_thick = bo * np.sqrt(1 - ((r - x0) / ao) ** 2) - h_bore + else: + h_bore = 0.0e0 + # Include the contribution from the straight section + h_thick = bo * np.sqrt(1 - ((r - x0) / ao) ** 2) + yarc[0] + + # Assume B in TF coil = 1/2 B in dr_bore + # Multiply by 2 for upper and lower halves of coil + tfind += b * dr * (2.0e0 * h_bore + h_thick) + r = r - dr + + # Outboard arc + ai = xarc[2] - xarc[1] + bi = (yarc[1] - yarc[3]) / 2.0e0 + ao = ai + tfthk + bo = bi + tfthk + dr = ao / NINTERVALS + # Initialise major radius + r = x0 + dr / 2.0e0 + + for _ in range(NINTERVALS): + # Field in the dr_bore for unit current + b = RMU0 / (2.0e0 * np.pi * r) + # Find out if there is a dr_bore + if r - x0 < ai: + h_bore = y0 + bi * np.sqrt(1 - ((r - x0) / ai) ** 2) + h_thick = bo * np.sqrt(1 - ((r - x0) / ao) ** 2) - h_bore + else: + h_bore = 0.0e0 + h_thick = bo * np.sqrt(1 - ((r - x0) / ao) ** 2) + + # Assume B in TF coil = 1/2 B in dr_bore + # Multiply by 2 for upper and lower halves of coil + tfind += b * dr * (2.0e0 * h_bore + h_thick) + r = r + dr + + return tfind + + def tf_coil_area_and_masses(self): + """Subroutine to calculate the TF coil areas and masses""" + vol_case = 0.0e0 # Total TF case volume [m3] + vol_ins = 0.0e0 # Total leg turn insulation volume [m3] + vol_gr_ins = 0.0e0 # Total leg turn insulation volume [m3] + vol_cond = 0.0e0 # Total conductor insulator volume [m3] + vol_ins_leg = 0.0e0 # Outboard leg turn isulation volume [m3] + vol_gr_ins_leg = 0.0e0 # Outboard leg turn insulation volume [m3] + vol_cond_leg = 0.0e0 # Outboard leg conductor insulator volume [m3] + # --- + + # Surface areas (for cryo system) [m2] + wbtf = ( + build_variables.r_tf_inboard_out * np.sin(sctfcoil_module.theta_coil) + - build_variables.r_tf_inboard_in * sctfcoil_module.tan_theta_coil + ) + tfcoil_variables.tfocrn = ( + build_variables.r_tf_inboard_in * sctfcoil_module.tan_theta_coil + ) + tfcoil_variables.tficrn = tfcoil_variables.tfocrn + wbtf + + # Total surface area of two toroidal shells covering the TF coils [m2] + # (inside and outside surfaces) + # = 2 * centroid coil length * 2 pi R, where R is average of i/b and o/b centres + tfcoil_variables.tfcryoarea = ( + 2.0e0 + * tfcoil_variables.len_tf_coil + * constants.twopi + * 0.5e0 + * (build_variables.r_tf_inboard_mid + build_variables.r_tf_outboard_mid) + ) + + # Superconductor coil design specific calculation + # --- + if tfcoil_variables.i_tf_sup == 1: + # Mass of case [kg] + # *** + + # Mass of ground-wall insulation [kg] + # (assumed to be same density/material as turn insulation) + tfcoil_variables.whtgw = ( + tfcoil_variables.len_tf_coil + * (sctfcoil_module.awpc - sctfcoil_module.awptf) + * tfcoil_variables.dcondins + ) + + # The length of the vertical section is that of the first (inboard) segment + # = height of TF coil inner edge + (2 * coil thickness) + tfcoil_variables.cplen = (2.0e0 * build_variables.hmax) + ( + 2.0e0 * build_variables.dr_tf_inboard + ) + + # The 2.2 factor is used as a scaling factor to fit + # to the ITER-FDR value of 450 tonnes; see CCFE note T&M/PKNIGHT/PROCESS/026 + if physics_variables.itart == 1: + # tfcoil_variables.len_tf_coil does not include inboard leg ('centrepost') length in TART + tfcoil_variables.whtcas = ( + 2.2e0 + * tfcoil_variables.dcase + * ( + tfcoil_variables.cplen * tfcoil_variables.acasetf + + tfcoil_variables.len_tf_coil * tfcoil_variables.acasetfo + ) + ) + else: + tfcoil_variables.whtcas = ( + 2.2e0 + * tfcoil_variables.dcase + * ( + tfcoil_variables.cplen * tfcoil_variables.acasetf + + (tfcoil_variables.len_tf_coil - tfcoil_variables.cplen) + * tfcoil_variables.acasetfo + ) + ) + + # *** + + # Masses of conductor constituents + # --------------------------------- + # Superconductor mass [kg] + # Includes space allowance for central helium channel, area tfcoil_variables.awphec + tfcoil_variables.whtconsc = ( + tfcoil_variables.len_tf_coil + * tfcoil_variables.n_tf_turn + * tfcoil_variables.acstf + * (1.0e0 - tfcoil_variables.vftf) + * (1.0e0 - tfcoil_variables.fcutfsu) + - tfcoil_variables.len_tf_coil * tfcoil_variables.awphec + ) * tfcoil_variables.dcond[tfcoil_variables.i_tf_sc_mat - 1] + + # Copper mass [kg] + tfcoil_variables.whtconcu = ( + tfcoil_variables.len_tf_coil + * tfcoil_variables.n_tf_turn + * tfcoil_variables.acstf + * (1.0e0 - tfcoil_variables.vftf) + * tfcoil_variables.fcutfsu + - tfcoil_variables.len_tf_coil * tfcoil_variables.awphec + ) * constants.dcopper + if tfcoil_variables.whtconcu <= 0.0e0: + tfcoil_variables.whtconcu = 0.0e0 + + # Steel conduit (sheath) mass [kg] + tfcoil_variables.whtconsh = ( + tfcoil_variables.len_tf_coil + * tfcoil_variables.n_tf_turn + * tfcoil_variables.acndttf + * fwbs_variables.denstl + ) + + # Conduit insulation mass [kg] + # (tfcoil_variables.aiwp already contains tfcoil_variables.n_tf_turn) + tfcoil_variables.whtconin = ( + tfcoil_variables.len_tf_coil + * tfcoil_variables.aiwp + * tfcoil_variables.dcondins + ) + + # Total conductor mass [kg] + tfcoil_variables.whtcon = ( + tfcoil_variables.whtconsc + + tfcoil_variables.whtconcu + + tfcoil_variables.whtconsh + + tfcoil_variables.whtconin + ) + # --------------------------------- + + # Total TF coil mass [kg] (all coils) + tfcoil_variables.whttf = ( + tfcoil_variables.whtcas + + tfcoil_variables.whtcon + + tfcoil_variables.whtgw + ) * tfcoil_variables.n_tf_coils + + # If spherical tokamak, distribute between centrepost and outboard legs + # (in this case, total TF coil length = inboard `cplen` + outboard `len_tf_coil`) + if physics_variables.itart == 1: + tfleng_sph = tfcoil_variables.cplen + tfcoil_variables.len_tf_coil + tfcoil_variables.whtcp = tfcoil_variables.whttf * ( + tfcoil_variables.cplen / tfleng_sph + ) + tfcoil_variables.whttflgs = tfcoil_variables.whttf * ( + tfcoil_variables.len_tf_coil / tfleng_sph + ) + + # Resitivive magnets weights + # --- + # Rem SK : No casing for the outboard leg is considered for now # + else: + # Volumes + # ------- + # CP with joints + # --- + if physics_variables.itart == 1: + # Total volume of one outerleg [m3] + tfcoil_variables.voltfleg = ( + tfcoil_variables.len_tf_coil * tfcoil_variables.a_tf_leg_outboard + ) + + # Outboard leg TF conductor volume [m3] + vol_cond_leg = tfcoil_variables.len_tf_coil * sctfcoil_module.a_leg_cond + + # Total TF conductor volume [m3] + vol_cond = ( + tfcoil_variables.vol_cond_cp + + tfcoil_variables.n_tf_coils * vol_cond_leg + ) + + # Outboard leg TF turn insulation layer volume (per leg) [m3] + vol_ins_leg = tfcoil_variables.len_tf_coil * sctfcoil_module.a_leg_ins + + # Total turn insulation layer volume [m3] + vol_ins = ( + sctfcoil_module.vol_ins_cp + + tfcoil_variables.n_tf_coils * vol_ins_leg + ) + + # Ouboard leg TF ground insulation layer volume (per leg) [m3] + vol_gr_ins_leg = ( + tfcoil_variables.len_tf_coil * sctfcoil_module.a_leg_gr_ins + ) + + # Total ground insulation layer volume [m3] + vol_gr_ins = ( + sctfcoil_module.vol_gr_ins_cp + + tfcoil_variables.n_tf_coils * vol_gr_ins_leg + ) + + # Total volume of the CP casing [m3] + # Rem : no outer leg case + vol_case = sctfcoil_module.vol_case_cp + + # No joints + # --- + else: + # Total TF outer leg conductor volume [m3] + vol_cond = ( + tfcoil_variables.len_tf_coil + * sctfcoil_module.a_leg_cond + * tfcoil_variables.n_tf_coils + ) + + # Total turn insulation layer volume [m3] + vol_ins = ( + tfcoil_variables.len_tf_coil + * sctfcoil_module.a_leg_ins + * tfcoil_variables.n_tf_coils + ) + + # Total ground insulation volume [m3] + vol_gr_ins = ( + tfcoil_variables.len_tf_coil + * sctfcoil_module.a_leg_gr_ins + * tfcoil_variables.n_tf_coils + ) + + # Total case volume [m3] + vol_case = ( + tfcoil_variables.len_tf_coil + * tfcoil_variables.acasetf + * tfcoil_variables.n_tf_coils + ) + + # --- + # ------- + + # Copper magnets casing/conductor weights per coil [kg] + if tfcoil_variables.i_tf_sup == 0: + tfcoil_variables.whtcas = ( + fwbs_variables.denstl * vol_case / tfcoil_variables.n_tf_coils + ) # Per TF leg, no casing for outer leg + tfcoil_variables.whtconcu = ( + constants.dcopper * vol_cond / tfcoil_variables.n_tf_coils + ) + tfcoil_variables.whtconal = 0.0e0 + + # Outer legs/CP weights + if physics_variables.itart == 1: + # Weight of all the TF legs + tfcoil_variables.whttflgs = tfcoil_variables.n_tf_coils * ( + constants.dcopper * vol_cond_leg + + tfcoil_variables.dcondins * (vol_ins_leg + vol_gr_ins_leg) + ) + + # CP weight + tfcoil_variables.whtcp = ( + constants.dcopper * tfcoil_variables.vol_cond_cp + + tfcoil_variables.dcondins + * (sctfcoil_module.vol_ins_cp + sctfcoil_module.vol_gr_ins_cp) + + sctfcoil_module.vol_case_cp * fwbs_variables.denstl + ) + + # Cryo-aluminium conductor weights + # Casing made of re-inforced aluminium alloy + elif tfcoil_variables.i_tf_sup == 2: + # Casing weight (CP only if physics_variables.itart = 1)bper leg/coil + tfcoil_variables.whtcas = ( + constants.dalu * vol_case / tfcoil_variables.n_tf_coils + ) + tfcoil_variables.whtconcu = 0.0e0 + tfcoil_variables.whtconal = ( + constants.dalu * vol_cond / tfcoil_variables.n_tf_coils + ) + + # Outer legs/CP weights + if physics_variables.itart == 1: + # Weight of all the TF legs + tfcoil_variables.whttflgs = tfcoil_variables.n_tf_coils * ( + constants.dalu * vol_cond_leg + + tfcoil_variables.dcondins * (vol_ins_leg + vol_gr_ins_leg) + ) + + # CP weight + tfcoil_variables.whtcp = ( + constants.dalu * tfcoil_variables.vol_cond_cp + + tfcoil_variables.dcondins + * (sctfcoil_module.vol_ins_cp + sctfcoil_module.vol_gr_ins_cp) + + sctfcoil_module.vol_case_cp * fwbs_variables.denstl + ) + + # Turn insulation mass [kg] + tfcoil_variables.whtconin = ( + tfcoil_variables.dcondins * vol_ins / tfcoil_variables.n_tf_coils + ) + + # Ground wall insulation layer weight + tfcoil_variables.whtgw = ( + tfcoil_variables.dcondins * vol_gr_ins / tfcoil_variables.n_tf_coils + ) + + # Total weight + tfcoil_variables.whttf = ( + tfcoil_variables.whtcas + + tfcoil_variables.whtconcu + + tfcoil_variables.whtconal + + tfcoil_variables.whtconin + + tfcoil_variables.whtgw + ) * tfcoil_variables.n_tf_coils + + @staticmethod + @numba.njit(cache=True) + def stresscl( + n_tf_layer, + n_radial_array, + n_tf_wp_layers, + i_tf_bucking, + r_tf_inboard_in, + dr_bore, + hmax, + f_z_cs_tf_internal, + dr_cs, + i_tf_inside_cs, + dr_tf_inboard, + dr_cs_tf_gap, + i_pf_conductor, + j_cs_flat_top_end, + j_cs_pulse_start, + c_pf_coil_turn_peak_input, + n_pf_coils_in_group, + ld_ratio_cst, + r_out_cst, + f_a_cs_steel, + eyoung_steel, + poisson_steel, + eyoung_cond_axial, + poisson_cond_axial, + eyoung_cond_trans, + poisson_cond_trans, + eyoung_ins, + poisson_ins, + thicndut, + eyoung_copper, + poisson_copper, + i_tf_sup, + eyoung_res_tf_buck, + r_wp_inner, + tan_theta_coil, + theta_coil, + r_wp_outer, + a_tf_steel, + a_case_front, + a_case_nose, + tfinsgap, + tinstf, + n_tf_turn, + i_tf_turns_integer, + t_cable, + t_cable_radial, + dhecoil, + fcutfsu, + thwcndut, + t_lat_case_av, + t_wp_toroidal_av, + a_tf_ins, + aswp, + acond, + awpc, + eyoung_al, + poisson_al, + fcoolcp, + n_tf_graded_layers, + c_tf_total, + casthi, + i_tf_stress_model, + vforce_inboard_tot, + i_tf_tresca, + acasetf, + vforce, + acndttf, + ): + """TF coil stress routine + + + author: P J Knight, CCFE, Culham Science Centre + author: J Morris, CCFE, Culham Science Centre + author: S Kahn, CCFE, Culham Science Centre + author: J Galambos, FEDC/ORNL + + This subroutine sets up the stress calculations for the + TF coil set. + PROCESS Superconducting TF Coil Model, J. Morris, CCFE, 1st May 2014 + """ + jeff = np.zeros((n_tf_layer,)) + # Effective current density [A/m2] + + radtf = np.zeros((n_tf_layer + 1,)) + # Radii used to define the layers used in the stress models [m] + # Layers are labelled from inboard to outbard + + eyoung_trans = np.zeros((n_tf_layer,)) + # Young's moduli (one per layer) of the TF coil in the + # transverse (radial/toroidal) direction. Used in the stress + # models [Pa] + + poisson_trans = np.zeros( + (n_tf_layer,), + ) + # Poisson's ratios (one per layer) of the TF coil between the + # two transverse directions (radial and toroidal). Used in the + # stress models. + + eyoung_member_array = np.zeros((n_tf_wp_layers,)) + # Array to store the Young's moduli of the members to composite into smeared + # properties [Pa] + + poisson_member_array = np.zeros((n_tf_wp_layers,)) + # Array to store the Poisson's ratios of the members to composite into smeared + # properti + + l_member_array = np.zeros((n_tf_wp_layers,)) + # Array to store the linear dimension (thickness) of the members to composite into smeared + # properties [m] + + eyoung_axial = np.zeros((n_tf_layer,)) + # Young's moduli (one per layer) of the TF coil in the vertical + # direction used in the stress models [Pa] + + poisson_axial = np.zeros((n_tf_layer,)) + # Poisson's ratios (one per layer) of the TF coil between the + # vertical and transverse directions (in that order). Used in the + # stress models. d(transverse strain)/d(vertical strain) with + # only vertical stress. + + sig_tf_wp_av_z = np.zeros(((n_tf_layer - i_tf_bucking) * n_radial_array,)) + # TF Inboard leg WP smeared vertical stress r distribution at mid-plane [Pa] + + sig_tf_r_max = np.zeros((n_tf_layer,)) + # Radial stress of the point of maximum shear stress of each layer [Pa] + + sig_tf_t_max = np.zeros((n_tf_layer,)) + # Toroidal stress of the point of maximum shear stress of each layer [Pa] + + sig_tf_z_max = np.zeros((n_tf_layer,)) + # Vertical stress of the point of maximum shear stress of each layer [Pa] + # Rem : Currently constant but will be r dependent in the future + + sig_tf_vmises_max = np.zeros((n_tf_layer,)) + # Von-Mises stress of the point of maximum shear stress of each layer [Pa] + + s_shear_tf_peak = np.zeros((n_tf_layer,)) + # Maximum shear stress, for the Tresca yield criterion of each layer [Pa] + # If the CEA correction is addopted, the CEA corrected value is used + + sig_tf_z = np.zeros((n_tf_layer * n_radial_array,)) + # TF Inboard leg vertical tensile stress at mid-plane [Pa] + + sig_tf_smeared_r = np.zeros((n_tf_layer * n_radial_array,)) + # TF Inboard leg radial smeared stress r distribution at mid-plane [Pa] + + sig_tf_smeared_t = np.zeros((n_tf_layer * n_radial_array,)) + # TF Inboard leg tangential smeared stress r distribution at mid-plane [Pa] + + sig_tf_smeared_z = np.zeros((n_tf_layer * n_radial_array,)) + # TF Inboard leg vertical smeared stress r distribution at mid-plane [Pa] + + fac_sig_z_wp_av = 0.0 + # WP averaged vertical stress unsmearing factor + + str_tf_r = np.zeros((n_radial_array * n_tf_layer,)) + str_tf_t = np.zeros((n_radial_array * n_tf_layer,)) + str_tf_z = np.zeros((n_radial_array * n_tf_layer,)) + + sig_tf_case = None + sig_tf_cs_bucked = None + str_wp = None + casestr = None + insstrain = None + + # New extended plane strain model can handle it + if ( + abs(r_tf_inboard_in) < np.finfo(float(r_tf_inboard_in)).eps + and i_tf_stress_model != 2 + ): + raise ValueError("r_tf_inboard_in is ~= 0", 245) + + # TODO: following is no longer used/needed? + # if tfcoil_variables.acstf >= 0.0e0: + # tcbs = numpy.sqrt(tfcoil_variables.acstf) + # else: + # tcbs = 0.0e0 + + # LAYER ELASTIC PROPERTIES + # ------------------------ + # Number of bucking layers + n_tf_bucking = i_tf_bucking + + # CS properties (bucked and wedged) + # --- + if i_tf_bucking >= 2: + # Calculation performed at CS flux swing (no current on CS) + jeff[0] = 0.0e0 + + # Inner radius of the CS + if i_tf_inside_cs == 1: + # CS not used as wedge support i_tf_inside_cs = 1 + radtf[0] = 0.001 + else: + radtf[0] = dr_bore + + # Superconducting CS + if i_pf_conductor == 0: + # Getting the turn dimention from scratch + # as the TF is called before CS in caller.f90 + # -# + + # CS vertical cross-section area [m2] + if i_tf_inside_cs == 1: + a_oh = 2.0e0 * hmax * f_z_cs_tf_internal * (dr_bore - dr_tf_inboard) + else: + a_oh = 2.0e0 * hmax * f_z_cs_tf_internal * dr_cs + + # Maximum current in Central Solenoid, at either BOP or EOF [MA-turns] + # Absolute value + curr_oh_max = ( + 1.0e-6 * np.maximum(j_cs_flat_top_end, j_cs_pulse_start) * a_oh + ) + + # Number of turns + n_oh_turns = ( + 1.0e6 + * curr_oh_max + / c_pf_coil_turn_peak_input[sum(n_pf_coils_in_group)] + ) + + # CS Turn vertical cross-sectionnal area + a_cs_turn = a_oh / n_oh_turns + + # CS coil turn geometry calculation - stadium shape + # Literature: https://doi.org/10.1016/j.fusengdes.2017.04.052 + d_cond_cst = ( + a_cs_turn / ld_ratio_cst + ) ** 0.5 # width of cs turn conduit + l_cond_cst = ld_ratio_cst * d_cond_cst # length of cs turn conduit + # Radius of turn space = r_in_cst + # Radius of curved outer corrner r_out_cst = 3mm from literature + # ld_ratio_cst = 70 / 22 from literature + p1 = ((l_cond_cst - d_cond_cst) / np.pi) ** 2 + p2 = ( + (l_cond_cst * d_cond_cst) + - (4 - np.pi) * (r_out_cst**2) + - (a_cs_turn * f_a_cs_steel) + ) / np.pi + r_in_cst = -((l_cond_cst - d_cond_cst) / np.pi) + np.sqrt(p1 + p2) + t_cond_oh = ( + d_cond_cst / 2 + ) - r_in_cst # thickness of steel conduit in cs turn + + # OH/CS conduit thickness calculated assuming square conduit [m] + # The CS insulation layer is assumed to the same as the TF one + + # CS turn cable space thickness + t_cable_oh = r_in_cst * 2 + # -# + + # Smeared elastic properties of the CS + # These smearing functions were written assuming transverse- + # isotropic materials; that is not true of the CS, where the + # stiffest dimension is toroidal and the radial and vertical + # dimension are less stiff. Nevertheless this attempts to + # hit the mark. + # [EDIT: eyoung_cond is for the TF coil, not the CS coil] + + # Get transverse properties + (eyoung_trans[0], a_working, poisson_trans[0]) = eyoung_parallel( + eyoung_steel, + f_a_cs_steel, + poisson_steel, + eyoung_cond_axial, + 1e0 - f_a_cs_steel, + poisson_cond_axial, + ) + + # Get vertical properties + # Split up into "members", concentric squares in cross section + # (described in Figure 10 of the TF coil documentation) + # Conductor + eyoung_member_array[0] = eyoung_cond_trans + poisson_member_array[0] = poisson_cond_trans + l_member_array[0] = t_cable_oh + # Steel conduit + eyoung_member_array[1] = eyoung_steel + poisson_member_array[1] = poisson_steel + l_member_array[1] = 2 * t_cond_oh + # Insulation + eyoung_member_array[2] = eyoung_ins + poisson_member_array[2] = poisson_ins + l_member_array[2] = 2 * thicndut + # [EDIT: Add central cooling channel? Would be new member #1] + + # Compute the composited (smeared) properties + ( + eyoung_axial[0], + a_working, + poisson_axial[0], + eyoung_cs_stiffest_leg, + ) = eyoung_t_nested_squares( + 3, eyoung_member_array, l_member_array, poisson_member_array + ) + + # resistive CS (copper) + else: + # Here is a rough approximation + eyoung_trans[0] = eyoung_copper + eyoung_axial[0] = eyoung_copper + poisson_trans[0] = poisson_copper + poisson_axial[0] = poisson_copper + + # --- + + # CS-TF interlayer properties + # --- + if i_tf_bucking == 3: + # No current in this layer + jeff[1] = 0.0e0 + + # Outer radius of the CS + if i_tf_inside_cs == 1: + radtf[1] = dr_bore - dr_tf_inboard - dr_cs_tf_gap + else: + radtf[1] = dr_bore + dr_cs + + # Assumed to be Kapton for the moment + # Ref : https://www.dupont.com/content/dam/dupont/products-and-services/membranes-and-films/polyimde-films/documents/DEC-Kapton-summary-of-properties.pdf + eyoung_trans[1] = 2.5e9 + eyoung_axial[1] = 2.5e9 + poisson_trans[1] = 0.34e0 # Default value for young modulus + poisson_axial[1] = 0.34e0 # Default value for young modulus + + # --- + + # bucking cylinder/casing properties + # --- + if i_tf_bucking >= 1: + # No current in bucking cylinder/casing + jeff[n_tf_bucking - 1] = 0.0e0 + + if i_tf_sup == 1: + eyoung_trans[n_tf_bucking - 1] = eyoung_steel + eyoung_axial[n_tf_bucking - 1] = eyoung_steel + poisson_trans[n_tf_bucking - 1] = poisson_steel + poisson_axial[n_tf_bucking - 1] = poisson_steel + + # Bucking cylinder properties + else: + eyoung_trans[n_tf_bucking - 1] = eyoung_res_tf_buck + eyoung_axial[n_tf_bucking - 1] = eyoung_res_tf_buck + poisson_trans[n_tf_bucking - 1] = poisson_steel # Seek better value # + poisson_axial[n_tf_bucking - 1] = poisson_steel # Seek better value # + + # Innernost TF casing radius + radtf[n_tf_bucking - 1] = r_tf_inboard_in + + # --- + + # (Super)conductor layer properties + # --- + # SC coil + if i_tf_sup == 1: + # Inner/outer radii of the layer representing the WP in stress calculations [m] + # These radii are chosen to preserve the true WP area; see Issue #1048 + r_wp_inner_eff = r_wp_inner * np.sqrt(tan_theta_coil / theta_coil) + r_wp_outer_eff = r_wp_outer * np.sqrt(tan_theta_coil / theta_coil) + + # Area of the cylinder representing the WP in stress calculations [m2] + a_wp_eff = (r_wp_outer_eff**2 - r_wp_inner_eff**2) * theta_coil + + # Steel cross-section under the area representing the WP in stress calculations [m2] + a_wp_steel_eff = a_tf_steel - a_case_front - a_case_nose + + # WP effective insulation thickness (SC only) [m] + # include groundwall insulation + insertion gap in tfcoil_variables.thicndut + # inertion gap is tfcoil_variables.tfinsgap on 4 sides + t_ins_eff = thicndut + (tfinsgap + tinstf) / n_tf_turn + + # Effective WP young modulus in the toroidal direction [Pa] + # The toroidal property drives the stress calculation (J. Last report no 4) + # Hence, the radial direction is relevant for the property smearing + # Rem : This assumption might be re-defined for bucked and wedged design + + # Non-integer or interger number of turns + t_cable_eyng = t_cable if i_tf_turns_integer == 0 else t_cable_radial + + # Average WP Young's modulus in the transverse + # (radial and toroidal) direction + # Split up into "members", concentric squares in cross section + # (described in Figure 10 of the TF coil documentation) + # Helium + eyoung_member_array[0] = 0e0 + poisson_member_array[0] = poisson_steel + l_member_array[0] = dhecoil + # Conductor and co-wound copper + ( + eyoung_member_array[1], + l_member_array[1], + poisson_member_array[1], + ) = eyoung_series( + np.double(eyoung_cond_trans), + (t_cable_eyng - dhecoil) * (1.0e0 - fcutfsu), + np.double(poisson_cond_trans), + np.double(eyoung_copper), + (t_cable_eyng - dhecoil) * fcutfsu, + np.double(poisson_copper), + ) + # Steel conduit + eyoung_member_array[2] = eyoung_steel + poisson_member_array[2] = poisson_steel + l_member_array[2] = 2 * thwcndut + # Insulation + eyoung_member_array[3] = eyoung_ins + poisson_member_array[3] = poisson_ins + l_member_array[3] = 2 * t_ins_eff + + # Compute the composited (smeared) properties + ( + eyoung_wp_trans, + a_working, + poisson_wp_trans, + eyoung_wp_stiffest_leg, + ) = eyoung_t_nested_squares( + 4, + eyoung_member_array, + l_member_array, + poisson_member_array, + ) + + # Lateral casing correction (series-composition) + (eyoung_wp_trans_eff, a_working, poisson_wp_trans_eff) = eyoung_series( + eyoung_wp_trans, + np.double(t_wp_toroidal_av), + poisson_wp_trans, + np.double(eyoung_steel), + 2.0e0 * t_lat_case_av, + np.double(poisson_steel), + ) + + poisson_wp_trans_eff = np.double(poisson_wp_trans_eff) + + # Average WP Young's modulus in the vertical direction + # Split up into "members", concentric squares in cross section + # (described in Figure 10 of the TF coil documentation) + # Steel conduit + eyoung_member_array[0] = eyoung_steel + poisson_member_array[0] = poisson_steel + l_member_array[0] = aswp + # Insulation + eyoung_member_array[1] = eyoung_ins + poisson_member_array[1] = poisson_ins + l_member_array[1] = a_tf_ins + # Copper + eyoung_member_array[2] = eyoung_copper + poisson_member_array[2] = poisson_copper + l_member_array[2] = acond * fcutfsu + # Conductor + eyoung_member_array[3] = eyoung_cond_axial + poisson_member_array[3] = poisson_cond_axial + l_member_array[3] = acond * (1.0e0 - fcutfsu) + # Helium and void + eyoung_member_array[4] = 0e0 + poisson_member_array[4] = poisson_steel + l_member_array[4] = awpc - acond - a_tf_ins - aswp + # Compute the composite / smeared properties: + (eyoung_wp_axial, a_working, poisson_wp_axial) = eyoung_parallel_array( + 5, + eyoung_member_array, + l_member_array, + poisson_member_array, + ) + + # Average WP Young's modulus in the vertical direction, now including the lateral case + # Parallel-composite the steel and insulation, now including the lateral case (sidewalls) + (eyoung_wp_axial_eff, a_working, poisson_wp_axial_eff) = eyoung_parallel( + eyoung_steel, + a_wp_steel_eff - aswp, + poisson_steel, + eyoung_wp_axial, + a_working, + poisson_wp_axial, + ) + + # Resistive coil + else: + # Picking the conductor material Young's modulus + if i_tf_sup == 0: + eyoung_cond = eyoung_copper + poisson_cond = poisson_copper + elif i_tf_sup == 2: + eyoung_cond = eyoung_al + poisson_cond = poisson_al + + # Effective WP young modulus in the toroidal direction [Pa] + # Rem : effect of cooling pipes and insulation not taken into account + # for now as it needs a radially dependent Young modulus + eyoung_wp_trans_eff = eyoung_cond + eyoung_wp_trans = np.double(eyoung_cond) + poisson_wp_trans_eff = np.double(poisson_cond) + poisson_wp_trans = np.double(poisson_cond) + + # WP area using the stress model circular geometry (per coil) [m2] + a_wp_eff = (r_wp_outer**2 - r_wp_inner**2) * theta_coil + + # Effective conductor region young modulus in the vertical direction [Pa] + # Parallel-composite conductor and insulator + (eyoung_wp_axial, a_working, poisson_wp_axial) = eyoung_parallel( + eyoung_cond, + (a_wp_eff - a_tf_ins) * (1.0e0 - fcoolcp), + poisson_cond, + eyoung_ins, + a_tf_ins, + poisson_ins, + ) + # Parallel-composite cooling pipes into that + (eyoung_wp_axial, a_working, poisson_wp_axial) = eyoung_parallel( + 0e0, + (a_wp_eff - a_tf_ins) * fcoolcp, + poisson_cond, + eyoung_wp_axial, + a_working, + poisson_wp_axial, + ) + + # Effective young modulus used in stress calculations + eyoung_wp_axial_eff = eyoung_wp_axial + poisson_wp_axial_eff = poisson_wp_axial + + # Effect conductor layer inner/outer radius + r_wp_inner_eff = np.double(r_wp_inner) + r_wp_outer_eff = np.double(r_wp_outer) + + # Thickness of the layer representing the WP in stress calcualtions [m] + dr_tf_wp_eff = r_wp_outer_eff - r_wp_outer_eff + + # Thickness of WP with homogeneous stress property [m] + dr_wp_layer = dr_tf_wp_eff / n_tf_graded_layers + + for ii in range(np.intc(n_tf_graded_layers)): + # Homogeneous current in (super)conductor + jeff[n_tf_bucking + ii] = c_tf_total / ( + np.pi * (r_wp_outer_eff**2 - r_wp_inner_eff**2) + ) + + # Same thickness for all WP layers in stress calculation + radtf[n_tf_bucking + ii] = r_wp_inner_eff + ii * dr_wp_layer + + # Young modulus + eyoung_trans[n_tf_bucking + ii] = eyoung_wp_trans_eff + eyoung_axial[n_tf_bucking + ii] = eyoung_wp_axial_eff + + # Poisson's ratio + poisson_trans[n_tf_bucking + ii] = poisson_wp_trans_eff + poisson_axial[n_tf_bucking + ii] = poisson_wp_axial_eff + + # Steel case on the plasma side of the inboard TF coil + # As per Issue #1509 + jeff[n_tf_layer - 1] = 0.0e0 + radtf[n_tf_layer - 1] = r_wp_outer_eff + eyoung_trans[n_tf_layer - 1] = eyoung_steel + eyoung_axial[n_tf_layer - 1] = eyoung_steel + poisson_trans[n_tf_layer - 1] = poisson_steel + poisson_axial[n_tf_layer - 1] = poisson_steel + + # last layer radius + radtf[n_tf_layer] = r_wp_outer_eff + casthi + + # The ratio between the true cross sectional area of the + # front case, and that considered by the plane strain solver + f_tf_stress_front_case = ( + a_case_front + / theta_coil + / (radtf[n_tf_layer] ** 2 - radtf[n_tf_layer - 1] ** 2) + ) + + # Correct for the missing axial stiffness from the missing + # outer case steel as per the updated description of + # Issue #1509 + eyoung_axial[n_tf_layer - 1] = ( + eyoung_axial[n_tf_layer - 1] * f_tf_stress_front_case + ) + + # --- + # ------------------------ + + # RADIAL STRESS SUBROUTINES CALL + # ------------------------------ + # Stress model not valid the TF does not contain any hole + # (Except if i_tf_plane_stress == 2; see Issue 1414) + # Current action : trigger and error and add a little hole + # to allow stress calculations + # Rem SK : Can be easily ameneded playing around the boundary conditions + # New extended plane strain model can handle it + if abs(radtf[0]) < np.finfo(float(radtf[0])).eps and i_tf_stress_model != 2: + radtf[0] = 1.0e-9 + # --- + + # Old generalized plane stress model + # --- + if i_tf_stress_model == 1: + # Plane stress calculation (SC) [Pa] + + (sig_tf_r, sig_tf_t, deflect, radial_array) = plane_stress( + nu=poisson_trans, + rad=radtf, + ey=eyoung_trans, + j=jeff, + nlayers=int(n_tf_layer), + n_radial_array=int(n_radial_array), + ) + + # Vertical stress [Pa] + sig_tf_z[:] = vforce / ( + acasetf + acndttf * n_tf_turn + ) # Array equation [EDIT: Are you sure? It doesn't look like one to me] + + # Strain in vertical direction on WP + str_wp = sig_tf_z[n_tf_bucking] / eyoung_wp_axial_eff + + # Case strain + casestr = sig_tf_z[n_tf_bucking - 1] / eyoung_steel + + # Radial strain in insulator + insstrain = ( + sig_tf_r[n_radial_array - 1] + * eyoung_wp_stiffest_leg + / eyoung_wp_trans_eff + / eyoung_ins + ) + # --- + + # New generalized plane strain formulation + # --- + # See #1670 for why i_tf_stress_model=0 was removed + elif i_tf_stress_model in (0, 2): + # Extended plane strain calculation [Pa] + # Issues #1414 and #998 + # Permits build_variables.dr_bore >= 0, O(n) in layers + # If build_variables.dr_bore > 0, same result as generalized plane strain calculation + + ( + radial_array, + sig_tf_r, + sig_tf_t, + sig_tf_z, + str_tf_r, + str_tf_t, + str_tf_z, + deflect, + ) = extended_plane_strain( + poisson_trans, + poisson_axial, + eyoung_trans, + eyoung_axial, + radtf, + jeff, + vforce_inboard_tot, + int(n_tf_layer), + int(n_radial_array), + int(n_tf_bucking), + ) + + # Strain in TF conductor material + str_wp = str_tf_z[n_tf_bucking * n_radial_array] + + # --- + + # Storing the smeared properties for output + + sig_tf_smeared_r[:] = sig_tf_r # Array equation + sig_tf_smeared_t[:] = sig_tf_t # Array equation + sig_tf_smeared_z[:] = sig_tf_z # Array equation + + # ------------------------------ + + # STRESS DISTRIBUTIONS CORRECTIONS + # -------------------------------- + # SC central solenoid coil stress unsmearing (bucked and wedged only) + # --- + if i_tf_bucking >= 2 and i_pf_conductor == 0: + # Central Solenoid (OH) steel conduit stress unsmearing factors + for ii in range(n_radial_array): + sig_tf_r[ii] = sig_tf_r[ii] * eyoung_cs_stiffest_leg / eyoung_axial[0] + sig_tf_t[ii] = sig_tf_t[ii] * eyoung_steel / eyoung_trans[0] + sig_tf_z[ii] = sig_tf_z[ii] * eyoung_cs_stiffest_leg / eyoung_axial[0] + + # --- + + # No TF vertical forces on CS and CS-TF layer (bucked and wedged only) + # --- + # This correction is only applied if the plane stress model is used + # as the generalized plane strain calculates the vertical stress properly + if i_tf_bucking >= 2 and i_tf_stress_model == 1: + for ii in range((n_tf_bucking - 1) * n_radial_array): + sig_tf_z[ii] = 0.0e0 + + # --- + + # Toroidal coil unsmearing + # --- + # Copper magnets + if i_tf_sup == 0: + # Vertical force unsmearing factor + fac_sig_z = eyoung_copper / eyoung_wp_axial_eff + + # Toroidal WP steel stress unsmearing factor + fac_sig_t = 1.0e0 + fac_sig_r = 1.0e0 + + elif i_tf_sup == 1: + # Vertical WP steel stress unsmearing factor + if i_tf_stress_model != 1: + fac_sig_z = eyoung_steel / eyoung_wp_axial_eff + fac_sig_z_wp_av = eyoung_wp_axial / eyoung_wp_axial_eff + else: + fac_sig_z = 1.0e0 + + # Toroidal WP steel conduit stress unsmearing factor + fac_sig_t = eyoung_wp_stiffest_leg / eyoung_wp_trans_eff + + # Radial WP steel conduit stress unsmearing factor + fac_sig_r = eyoung_wp_stiffest_leg / eyoung_wp_trans_eff + + elif i_tf_sup == 2: + # Vertical WP steel stress unsmearing factor + fac_sig_z = eyoung_al / eyoung_wp_axial_eff + + # Toroidal WP steel stress unsmearing factor + # NO CALCULTED FOR THE MOMENT (to be done later) + fac_sig_t = 1.0e0 + fac_sig_r = 1.0e0 + + # Application of the unsmearing to the WP layers + # For each point within the winding pack / conductor, unsmear the + # stress. This is n_radial_array test points within tfcoil_variables.n_tf_graded_layers + # layers starting at n_tf_bucking + 1 + # GRADED MODIF : add another do loop to allow the graded properties + # to be taken into account + for ii in range( + n_tf_bucking * n_radial_array, + ((n_tf_bucking + n_tf_graded_layers) * n_radial_array), + ): + sig_tf_wp_av_z[ii - n_tf_bucking * n_radial_array] = ( + sig_tf_z[ii] * fac_sig_z_wp_av + ) + sig_tf_r[ii] = sig_tf_r[ii] * fac_sig_r + sig_tf_t[ii] = sig_tf_t[ii] * fac_sig_t + sig_tf_z[ii] = sig_tf_z[ii] * fac_sig_z + + # For each point within the front case, + # remove the correction for the missing axial + # stiffness as per the updated description of + # Issue #1509 + for ii in range( + (n_tf_bucking + n_tf_graded_layers) * n_radial_array, + (n_tf_layer * n_radial_array), + ): + sig_tf_z[ii] = sig_tf_z[ii] / f_tf_stress_front_case + # --- + + # Tresca / Von Mises yield criteria calculations + # ----------------------------- + # Array equation + s_shear_tf = np.maximum( + np.absolute(sig_tf_r - sig_tf_z), np.absolute(sig_tf_z - sig_tf_t) + ) + + # Array equation + + sig_tf_vmises = np.sqrt( + 0.5e0 + * ( + (sig_tf_r - sig_tf_t) ** 2 + + (sig_tf_r - sig_tf_z) ** 2 + + (sig_tf_z - sig_tf_t) ** 2 + ) + ) + + # Array equation + s_shear_cea_tf_cond = s_shear_tf.copy() + + # SC conducting layer stress distribution corrections + # --- + if i_tf_sup == 1: + # GRADED MODIF : add another do loop to allow the graded properties + # to be taken into account + for ii in range( + (n_tf_bucking * n_radial_array), n_tf_layer * n_radial_array + ): + # Addaped Von-mises stress calculation to WP strucure [Pa] + + svmxz = sigvm(0.0e0, sig_tf_t[ii], sig_tf_z[ii], 0.0e0, 0.0e0, 0.0e0) + + svmyz = sigvm(sig_tf_r[ii], 0.0e0, sig_tf_z[ii], 0.0e0, 0.0e0, 0.0e0) + sig_tf_vmises[ii] = max(svmxz, svmyz) + + # Maximum shear stress for the Tresca yield criterion using CEA calculation [Pa] + s_shear_cea_tf_cond[ii] = ( + 1.02e0 * abs(sig_tf_r[ii]) + 1.6e0 * sig_tf_z[ii] + ) + + # --- + # ----------------------------- + + # Output formating : Maximum shear stress of each layer for the Tresca yield criterion + # ---------------- + for ii in range(n_tf_layer): + sig_max = 0.0e0 + ii_max = 0 + + for jj in range(ii * n_radial_array, (ii + 1) * n_radial_array): + # CEA out of plane approximation + if i_tf_tresca == 1 and i_tf_sup == 1 and ii >= i_tf_bucking + 1: + if sig_max < s_shear_cea_tf_cond[jj]: + sig_max = s_shear_cea_tf_cond[jj] + ii_max = jj + + # Conventional Tresca + else: + if sig_max < s_shear_tf[jj]: + sig_max = s_shear_tf[jj] + ii_max = jj + + # OUT.DAT output + + sig_tf_r_max[ii] = sig_tf_r[ii_max] + sig_tf_t_max[ii] = sig_tf_t[ii_max] + sig_tf_z_max[ii] = sig_tf_z[ii_max] + sig_tf_vmises_max[ii] = sig_tf_vmises[ii_max] + + # Maximum shear stress for the Tresca yield criterion (or CEA OOP correction) + + if i_tf_tresca == 1 and i_tf_sup == 1 and ii >= i_tf_bucking + 1: + s_shear_tf_peak[ii] = s_shear_cea_tf_cond[ii_max] + else: + s_shear_tf_peak[ii] = s_shear_tf[ii_max] + + # Constraint equation for the Tresca yield criterion + + sig_tf_wp = s_shear_tf_peak[n_tf_bucking] + # Maximum assumed in the first graded layer + + if i_tf_bucking >= 1: + sig_tf_case = s_shear_tf_peak[n_tf_bucking - 1] + if i_tf_bucking >= 2: + sig_tf_cs_bucked = s_shear_tf_peak[0] + # ---------------- + + return ( + sig_tf_r_max, + sig_tf_t_max, + sig_tf_z_max, + sig_tf_vmises_max, + s_shear_tf_peak, + deflect, + eyoung_axial, + eyoung_trans, + eyoung_wp_axial, + eyoung_wp_trans, + poisson_wp_trans, + radial_array, + s_shear_cea_tf_cond, + poisson_wp_axial, + sig_tf_r, + sig_tf_smeared_r, + sig_tf_smeared_t, + sig_tf_smeared_z, + sig_tf_t, + s_shear_tf, + sig_tf_vmises, + sig_tf_z, + str_tf_r, + str_tf_t, + str_tf_z, + n_radial_array, + n_tf_bucking, + sig_tf_wp, + sig_tf_case, + sig_tf_cs_bucked, + str_wp, + casestr, + insstrain, + sig_tf_wp_av_z, + ) + + def out_stress( + self, + sig_tf_r_max, + sig_tf_t_max, + sig_tf_z_max, + sig_tf_vmises_max, + s_shear_tf_peak, + deflect, + eyoung_axial, + eyoung_trans, + eyoung_wp_axial, + eyoung_wp_trans, + poisson_wp_trans, + radial_array, + s_shear_cea_tf_cond, + poisson_wp_axial, + sig_tf_r, + sig_tf_smeared_r, + sig_tf_smeared_t, + sig_tf_smeared_z, + sig_tf_t, + s_shear_tf, + sig_tf_vmises, + sig_tf_z, + str_tf_r, + str_tf_t, + str_tf_z, + n_radial_array, + n_tf_bucking, + sig_tf_wp_av_z, + ): + """Subroutine showing the writing the TF midplane stress analysis + in the output file and the stress distribution in the SIG_TF.json + file used to plot stress distributions + Author : S. Kahn + """ + + def table_format_arrays(a, mult=1, delim="\t\t"): + return delim.join([str(i * mult) for i in a]) + + # Stress output section + po.oheadr(self.outfile, "TF coils ") + po.osubhd(self.outfile, "TF Coil Stresses (CCFE model) :") + + if tfcoil_variables.i_tf_stress_model == 1: + po.ocmmnt(self.outfile, "Plane stress model with smeared properties") + else: + po.ocmmnt(self.outfile, "Generalized plane strain model") + + po.ovarre( + self.outfile, + "Allowable maximum shear stress in TF coil case (Tresca criterion) (Pa)", + "(sig_tf_case_max)", + tfcoil_variables.sig_tf_case_max, + ) + + po.ovarre( + self.outfile, + "Allowable maximum shear stress in TF coil conduit (Tresca criterion) (Pa)", + "(sig_tf_wp_max)", + tfcoil_variables.sig_tf_wp_max, + ) + if tfcoil_variables.i_tf_tresca == 1 and tfcoil_variables.i_tf_sup == 1: + po.ocmmnt( + self.outfile, + "WP conduit Tresca criterion corrected using CEA formula (i_tf_tresca = 1)", + ) + + if tfcoil_variables.i_tf_bucking >= 3: + po.ocmmnt( + self.outfile, "No stress limit imposed on the TF-CS interface layer" + ) + po.ocmmnt( + self.outfile, " -> Too much unknow on it material choice/properties" + ) + + # OUT.DAT data on maximum shear stress values for the Tresca criterion + po.ocmmnt( + self.outfile, + "Materal stress of the point of maximum shear stress (Tresca criterion) for each layer", + ) + po.ocmmnt( + self.outfile, + "Please use utilities/plot_stress_tf.py for radial plots plots summary", + ) + + if tfcoil_variables.i_tf_bucking == 0: + if tfcoil_variables.i_tf_sup == 1: + po.write(self.outfile, " Layers \t\t\t\t WP \t\t Outer case") + else: + po.write(self.outfile, " Layers \t\t\t\t conductor \t\t Outer case") + + elif tfcoil_variables.i_tf_bucking == 1: + if tfcoil_variables.i_tf_sup == 1: + po.write( + self.outfile, " Layers \t\t\t\t Steel case \t\t WP \t\t Outer case" + ) + else: + po.write( + self.outfile, + " Layers \t\t\t\t bucking \t\t conductor \t\t Outer case", + ) + + elif tfcoil_variables.i_tf_bucking == 2: + if tfcoil_variables.i_tf_sup == 1: + po.write( + self.outfile, + " Layers \t\t\t\t CS \t\t Steel case \t\t WP \t\t Outer case", + ) + else: + po.write( + self.outfile, + " Layers \t\t\t\t CS \t\t bucking \t\t conductor \t\t Outer case", + ) + + elif tfcoil_variables.i_tf_bucking == 3: + if tfcoil_variables.i_tf_sup == 1: + po.write( + self.outfile, + " Layers \t\t\t\t CS \t\t interface \t\t Steel case \t\t WP \t\t Outer case", + ) + else: + po.write( + self.outfile, + " Layers \t\t\t\t CS \t\t interface \t\t bucking \t\t conductor \t\t Outer case", + ) + + po.write( + self.outfile, + f" Radial stress \t\t\t (MPa) \t\t {table_format_arrays(sig_tf_r_max, 1e-6)}", + ) + po.write( + self.outfile, + f" Toroidal stress \t\t\t (MPa) \t\t {table_format_arrays(sig_tf_t_max, 1e-6)}", + ) + po.write( + self.outfile, + f" Vertical stress \t\t\t (MPa) \t\t {table_format_arrays(sig_tf_z_max, 1e-6)}", + ) + po.write( + self.outfile, + f" Von-Mises stress \t\t\t (MPa) \t\t {table_format_arrays(sig_tf_vmises_max, 1e-6)}", + ) + + if tfcoil_variables.i_tf_tresca == 1 and tfcoil_variables.i_tf_sup == 1: + po.write( + self.outfile, + f" Shear (CEA Tresca) \t\t\t (MPa) \t\t {table_format_arrays(s_shear_tf_peak, 1e-6)}", + ) + else: + po.write( + self.outfile, + f" Shear (Tresca) \t\t\t (MPa) \t\t {table_format_arrays(s_shear_tf_peak, 1e-6)}", + ) + + po.write(self.outfile, "") + po.write( + self.outfile, + f" Toroidal modulus \t\t\t (GPa) \t\t {table_format_arrays(eyoung_trans, 1e-9)}", + ) + po.write( + self.outfile, + f" Vertical modulus \t\t\t (GPa) \t\t {table_format_arrays(eyoung_axial, 1e-9)}", + ) + po.write(self.outfile, "") + po.ovarre( + self.outfile, + "WP transverse modulus (GPa)", + "(eyoung_wp_trans*1.0d-9)", + eyoung_wp_trans * 1.0e-9, + "OP ", + ) + po.ovarre( + self.outfile, + "WP vertical modulus (GPa)", + "(eyoung_wp_axial*1.0d-9)", + eyoung_wp_axial * 1.0e-9, + "OP ", + ) + po.ovarre( + self.outfile, + "WP transverse Poissons ratio", + "(poisson_wp_trans)", + poisson_wp_trans, + "OP ", + ) + po.ovarre( + self.outfile, + "WP vertical-transverse Pois. rat.", + "(poisson_wp_axial)", + poisson_wp_axial, + "OP ", + ) + + # MFILE.DAT data + for ii in range(n_tf_bucking + 2): + po.ovarre( + constants.mfile, + f"Radial stress at maximum shear of layer {ii + 1} (Pa)", + f"(sig_tf_r_max({ii + 1}))", + sig_tf_r_max[ii], + ) + po.ovarre( + constants.mfile, + f"toroidal stress at maximum shear of layer {ii + 1} (Pa)", + f"(sig_tf_t_max({ii + 1}))", + sig_tf_t_max[ii], + ) + po.ovarre( + constants.mfile, + f"Vertical stress at maximum shear of layer {ii + 1} (Pa)", + f"(sig_tf_z_max({ii + 1}))", + sig_tf_z_max[ii], + ) + po.ovarre( + constants.mfile, + f"Von-Mises stress at maximum shear of layer {ii + 1} (Pa)", + f"(sig_tf_vmises_max({ii + 1}))", + sig_tf_vmises_max[ii], + ) + if tfcoil_variables.i_tf_tresca == 1 and tfcoil_variables.i_tf_sup == 1: + po.ovarre( + constants.mfile, + f"Maximum shear stress for CEA Tresca yield criterion {ii + 1} (Pa)", + f"(s_shear_tf_peak({ii + 1}))", + s_shear_tf_peak[ii], + ) + else: + po.ovarre( + constants.mfile, + f"Maximum shear stress for the Tresca yield criterion {ii + 1} (Pa)", + f"(s_shear_tf_peak({ii + 1}))", + s_shear_tf_peak[ii], + ) + + # SIG_TF.json storage + sig_file_data = { + "Points per layers": n_radial_array, + "Radius (m)": radial_array, + "Radial stress (MPa)": sig_tf_r * 1e-6, + "Toroidal stress (MPa)": sig_tf_t * 1e-6, + "Vertical stress (MPa)": sig_tf_z * 1e-6, + "Radial smear stress (MPa)": sig_tf_smeared_r * 1e-6, + "Toroidal smear stress (MPa)": sig_tf_smeared_t * 1e-6, + "Vertical smear stress (MPa)": sig_tf_smeared_z * 1e-6, + "Von-Mises stress (MPa)": sig_tf_vmises * 1e-6, + "CEA Tresca stress (MPa)": ( + s_shear_cea_tf_cond * 1e-6 + if tfcoil_variables.i_tf_sup == 1 + else s_shear_tf * 1e-6 + ), + "rad. displacement (mm)": deflect * 1e3, + } + if tfcoil_variables.i_tf_stress_model != 1: + sig_file_data = { + **sig_file_data, + "Radial strain": str_tf_r, + "Toroidal strain": str_tf_t, + "Vertical strain": str_tf_z, + } + + if tfcoil_variables.i_tf_sup == 1: + sig_file_data = { + **sig_file_data, + "WP smeared stress (MPa)": sig_tf_wp_av_z * 1.0e-6, + } + + sig_file_data = { + k: list(v) if isinstance(v, np.ndarray) else v + for k, v in sig_file_data.items() + } + + sig_filename = ( + f2py_compatible_to_string(global_variables.output_prefix) + "SIG_TF.json" + ) + with open(sig_filename, "w") as f: + json.dump(sig_file_data, f) + + # TODO sig_tf_wp_av_z is always undefined here. This needs correcting or removing + # if ( tfcoil_variables.i_tf_sup == 1 ) : + # write(constants.sig_file,'(t2, "WP" ," smeared stress", t20, "(MPa)",t26, *(F11.3,3x))') sig_tf_wp_av_z*1.0e-6 + # + + # Quantities from the plane stress stress formulation (no resitive coil output) + if tfcoil_variables.i_tf_stress_model == 1 and tfcoil_variables.i_tf_sup == 1: + # Other quantities (displacement strain, etc..) + po.ovarre( + self.outfile, + "Maximum radial deflection at midplane (m)", + "(deflect)", + deflect[n_radial_array - 1], + "OP ", + ) + po.ovarre( + self.outfile, + "Vertical strain on casing", + "(casestr)", + tfcoil_variables.casestr, + "OP ", + ) + po.ovarre( + self.outfile, + "Radial strain on insulator", + "(insstrain)", + tfcoil_variables.insstrain, + "OP ", + ) + + +@numba.njit(cache=True) +def eyoung_parallel( + eyoung_j_1, a_1, poisson_j_perp_1, eyoung_j_2, a_2, poisson_j_perp_2 +): + """ + Author : C. Swanson, PPPL + January 2022 + See Issue #1205 for derivation PDF + This subroutine gives the smeared elastic properties of two + members that are carrying a force in parallel with each other. + The force goes in direction j. + Members 1 and 2 are the individual members to be smeared. + Member 3 is the effective smeared member (output). + This is pretty easy because the smeared properties are simply + the average weighted by the cross-sectional areas perpendicular + to j. + The assumption is that the strains in j are equal. + If you're dealing with anisotropy, please pay attention to the + fact that the specific Young's Modulus used here is that in + the j direction, and the specific Poisson's ratio used here is + that between the j and transverse directions in that order. + (transverse strain / j strain, under j stress) + The smeared Poisson's ratio is computed assuming the transverse + dynamics are isotropic, and that the two members are free to + shrink/expand under Poisson effects without interference from + each other. This may not be true of your case. + + To build up a composite smeared member of any number of + individual members, you can pass the same properties for + members 2 and 3, and call it successively, using the properties + of each member as the first triplet of arguments. This way, the + last triplet acts as a "working sum": + call eyoung_parallel(triplet1, triplet2, tripletOUT) + call eyoung_parallel(triplet3, tripletOUT, tripletOUT) + call eyoung_parallel(triplet4, tripletOUT, tripletOUT) + ... etc. + So that tripletOUT would eventually have the smeared properties + of the total composite member. + """ + poisson_j_perp_3 = (poisson_j_perp_1 * a_1 + poisson_j_perp_2 * a_2) / (a_1 + a_2) + eyoung_j_3 = (eyoung_j_1 * a_1 + eyoung_j_2 * a_2) / (a_1 + a_2) + a_3 = a_1 + a_2 + + return eyoung_j_3, a_3, poisson_j_perp_3 + + +@numba.njit(cache=True) +def sigvm(sx: float, sy: float, sz: float, txy: float, txz: float, tyz: float) -> float: + """Calculates Von Mises stress in a TF coil + author: P J Knight, CCFE, Culham Science Centre + author: B Reimer, FEDC + This routine calculates the Von Mises combination of + stresses (Pa) in a TF coil. + + :param sx: In-plane stress in X direction [Pa] + :param sy: In-plane stress in Y direction [Pa] + :param sz: In-plane stress in Z direction [Pa] + :param txy: Out-of-plane stress in X-Y plane [Pa] + :param txz: Out-of-plane stress in X-Z plane [Pa] + :param tyz: Out-of-plane stress in Y-Z plane [Pa] + + :returns: Von Mises combination of stresses (Pa) in a TF coil. + """ + + return np.sqrt( + 0.5 + * ( + (sx - sy) ** 2 + + (sx - sz) ** 2 + + (sz - sy) ** 2 + + 6 * (txy**2 + txz**2 + tyz**2) + ) + ) + + +@numba.njit(cache=True, error_model="numpy") +def extended_plane_strain( + nu_t, + nu_zt, + ey_t, + ey_z, + rad, + d_curr, + v_force, + nlayers, + n_radial_array, + i_tf_bucking, +): + """Author : C. Swanson, PPPL and S. Kahn, CCFE + September 2021 + There is a writeup of the derivation of this model on the gitlab server. + https://git.ccfe.ac.uk/process/process/-/issues/1414 + This surboutine estimates the radial displacement, stresses, and strains of + the inboard midplane of the TF. It assumes that structures are axisymmetric + and long in the axial (z) direction, the "axisymmetric extended plane strain" + problem. The TF is assumed to be constructed from some number of layers, + within which materials properties and current densities are uniform. + The 1D radially-resolved solution is reduced to a 0D matrix inversion problem + using analytic solutions to Lame's thick cylinder problem. Materials may be + transverse-isotropic in Young's modulus and Poisson's ratio. The consraints + are: Either zero radial stress or zero radial displacement at the inner + surface (depending on whether the inner radius is zero), zero radial stress + at the outer surface, total axial force (tension) is equal to the input, + and optionally the axial force of an inner subset of cylinders is zero (slip + conditions between the inner and outer subset). The matrix inversion / linear + solve is always 4x4, no matter how many layers there are. + The problem is formulated around a solution vector (A,B,eps_z,1.0,eps_z_slip) + where A and B are the parameters in Lame's solution where u = A*r + B/r, u + is the radial displacement. eps_z is the axial strain on the outer, force- + carrying layers. eps_z_slip is the axial strain on the inner, non-force- + carrying layers (optionally). The solution vector is A,B at the outermost + radius, and is transformed via matrix multiplication into those A,B + values at other radii. The constraints are inner products with this vector, + and so when stacked form a matrix to invert. + """ + # outputs + sigr = np.zeros((n_radial_array * nlayers,)) + # Stress distribution in the radial direction (r) [Pa] + + sigt = np.zeros((n_radial_array * nlayers,)) + # Stress distribution in the toroidal direction (t) [Pa] + + sigz = np.zeros((n_radial_array * nlayers,)) + # Stress distribution in the vertical direction (z) + + str_r = np.zeros((n_radial_array * nlayers,)) + # Strain distribution in the radial direction (r) + + str_t = np.zeros((n_radial_array * nlayers,)) + # Strain distribution in the toroidal direction (t) + + str_z = np.zeros((n_radial_array * nlayers,)) + # Uniform strain in the vertical direction (z) + + r_deflect = np.zeros((n_radial_array * nlayers,)) + # Radial displacement radial distribution [m] + + rradius = np.zeros((n_radial_array * nlayers,)) + # Radius array [m] + + # local arrays + # Stiffness form of compliance tensor + nu_tz = np.zeros((nlayers,)) + # Transverse-axial Poisson's ratio + # (ratio of axial strain to transverse strain upon transverse stress) + ey_bar_z = np.zeros((nlayers,)) + # Axial effective Young's modulus (zero cross-strains, not stresses) [Pa] + ey_bar_t = np.zeros((nlayers,)) + # Transverse effective Young's modulus [Pa] + nu_bar_t = np.zeros((nlayers,)) + # Transverse effective Poisson's ratio + nu_bar_tz = np.zeros((nlayers,)) + # Transverse-axial effective Poisson's ratio + nu_bar_zt = np.zeros((nlayers,)) + # Axial-transverse effective Poisson's ratio + + # Lorentz force parameters + currents = np.zeros((nlayers,)) + # Currents in each layer [A] + currents_enclosed = np.zeros((nlayers,)) + # Currents enclosed by inner radius of each layer [A] + f_lin_fac = np.zeros((nlayers,)) + # Factor that multiplies r linearly in the force density + f_rec_fac = np.zeros((nlayers,)) + # Factor that multiplies r reciprocally in the force density + f_int_a = np.zeros((nlayers,)) + # Force density integral that adds to Lame parameter A + f_int_b = np.zeros((nlayers,)) + # Force density integral that adds to Lame parameter B + + # Layer transfer matrices + m_int = np.zeros( + ( + 5, + 5, + nlayers, + ), + ) + # Matrix that transforms the Lame parmeter vector from the + # outer radius to the inner radius of each layer + m_ext = np.zeros( + ( + 5, + 5, + nlayers, + ), + ) + # Matrix that transforms the Lame parmeter vector from the + # inner radius of one layer to the outer radius of the + # next inner. + m_tot = np.zeros( + ( + 5, + 5, + nlayers, + ), + ) + # Matrix that transforms the Lame parmeter vector from the + # outer radius of the outer layer to the inner radius of + # each. + + # Axial force inner product + v_force_row = np.zeros( + ( + 1, + 5, + ), + ) + # Row vector (matrix multiplication is inner product) to + # obtain the axial force from the force-carrying layers + v_force_row_slip = np.zeros( + ( + 1, + 5, + ), + ) + # Row vector (matrix multiplication is inner product) to + # obtain the axial force inner slip layers (no net force) + rad_row_helper = np.zeros( + ( + 1, + 5, + ), + ) + # A helper variable to store [radius, 1, 0, 0, 0] in row + + # Boundary condition matrix + m_bc = np.zeros( + ( + 4, + 5, + ), + ) + # Boundary condition matrix. Multiply this with the + # outermost solution vector, (A,B,eps_z,1.0,eps_z_slip), + # to obtain a zero vector. + m_toinv = np.zeros( + ( + 4, + 4, + ), + ) + # Matrix to invert to get the solution + RHS_vec = np.zeros((4,)) + # Right-hand-side vector to divide m_toinv + a_vec_solution = np.zeros((5,)) + # Solution vector, Lame parameters at outer radius, strain + # of force-carrying layers, and strain of slip layers + # (A,B,eps_z,1,eps_z_slip) + + # Constructing the solution everywhere + a_vec_layer = np.zeros((5,)) + # Lame parameters and strains vector at outer radius + # of each layer + + # The stress calcualtion differential equations is analytically sloved + # The final solution is given by the layer boundary conditions on + # radial stress and displacement between layers solved + # The problem is set as aa.cc = bb, cc being the constant we search + + # Inner slip layers parameters + # Section 15 in the writeup + # Innermost layer that takes axial force. Layers inner of this + # have zero net axial force, to include CS decoupling. + # This configuration JUST HAPPENS to work out because of + # the specific i_tf_bucking options; if those are changed, + # will need a switch here. + nonslip_layer = i_tf_bucking + + if nonslip_layer < 1: + nonslip_layer = 1 + + # Stiffness tensor factors + # Section 3 in the writeup + # With Section 12 anisotropic materials properties + # *** + # Dependent Poisson's ratio: nu-transverse-axial + # from nu-axial-transverse and the Young's moduli + nu_tz[:] = nu_zt * ey_t / ey_z + + # Effective Young's Moduli and Poisson's ratios + # holding strain, not stress, cross-terms constant + ey_bar_z[:] = ey_z * (1 - nu_t) / (1 - nu_t - 2 * nu_tz * nu_zt) + ey_bar_t[:] = ( + ey_t * (1 - nu_tz * nu_zt) / (1 - nu_t - 2 * nu_tz * nu_zt) / (1 + nu_t) + ) + + nu_bar_t[:] = (nu_t + nu_tz * nu_zt) / (1 - nu_tz * nu_zt) + nu_bar_tz[:] = nu_tz / (1 - nu_t) + nu_bar_zt[:] = nu_zt * (1 + nu_t) / (1 - nu_tz * nu_zt) + + # Lorentz force parameters + # Section 13 in the writeup + # *** + # Currents in each layer [A] + currents[:] = np.pi * d_curr * (rad[1 : nlayers + 1] ** 2 - rad[:nlayers] ** 2) + # Currents enclosed by inner radius of each layer [A] + currents_enclosed[0] = 0.0e0 + + for ii in range(1, nlayers): + currents_enclosed[ii] = currents_enclosed[ii - 1] + currents[ii - 1] + # Factor that multiplies r linearly in the force density + f_lin_fac[:] = RMU0 / 2.0e0 * d_curr**2 + # Factor that multiplies r reciprocally in the force density + f_rec_fac[:] = ( + RMU0 + / 2.0e0 + * (d_curr * currents_enclosed / np.pi - d_curr**2 * rad[:nlayers] ** 2) + ) + # Force density integral that adds to Lame parameter A + f_int_a[:] = 0.5e0 * f_lin_fac * ( + rad[1 : nlayers + 1] ** 2 - rad[:nlayers] ** 2 + ) + f_rec_fac * np.log(rad[1 : nlayers + 1] / rad[:nlayers]) + if f_rec_fac[0] == 0e0: + f_int_a[0] = 0.5e0 * f_lin_fac[0] * (rad[1] ** 2 - rad[0] ** 2) + + # Force density integral that adds to Lame parameter B + f_int_b[:] = 0.25e0 * f_lin_fac * ( + rad[1 : nlayers + 1] ** 4 - rad[:nlayers] ** 4 + ) + 0.5e0 * f_rec_fac * (rad[1 : nlayers + 1] ** 2 - rad[:nlayers] ** 2) + + # Transformation matrix from outer to inner Lame parameters + # Section 5 in the writeup + # With Section 12 anisotropic materials properties + # *** + # m_int[kk] multiplies Lame parameter vector of layer kk (A,B,eps_z,1.0,eps_z_slip) + # and transforms the values at the outer radius to the values at the inner radius + for kk in range(nlayers): + m_int[0, 0, kk] = 1.0e0 + m_int[1, 1, kk] = 1.0e0 + m_int[2, 2, kk] = 1.0e0 + m_int[3, 3, kk] = 1.0e0 + m_int[4, 4, kk] = 1.0e0 + + m_int[0, 3, kk] = -0.5e0 / ey_bar_t[kk] * f_int_a[kk] + m_int[1, 3, kk] = 0.5e0 / ey_bar_t[kk] * f_int_b[kk] + + # Transformation matrix between layers + # Section 6 in the writeup + # With Section 12 anisotropic materials properties + # With Section 15 inner slip-decoupled layers + # *** + # m_ext[kk] multiplies Lame parameter vector of layer kk (A,B,eps_z,1.0,eps_z_slip) + # and transforms the values at the inner radius to the values at the outer radius + # of layer kk-1 + for kk in range(1, nonslip_layer - 1): + ey_fac = ey_bar_t[kk] / ey_bar_t[kk - 1] + m_ext[0, 2, kk] = 0.0e0 + m_ext[0, 4, kk] = 0.5e0 * (ey_fac * nu_bar_zt[kk] - nu_bar_zt[kk - 1]) + + if nonslip_layer > 1: + ey_fac = ey_bar_t[nonslip_layer - 1] / ey_bar_t[nonslip_layer - 2] + m_ext[0, 2, nonslip_layer - 1] = 0.5e0 * ey_fac * nu_bar_zt[nonslip_layer - 1] + m_ext[0, 4, nonslip_layer - 1] = 0.5e0 * (-nu_bar_zt[nonslip_layer - 2]) + + for kk in range(nonslip_layer, nlayers): + ey_fac = ey_bar_t[kk] / ey_bar_t[kk - 1] + m_ext[0, 2, kk] = 0.5e0 * (ey_fac * nu_bar_zt[kk] - nu_bar_zt[kk - 1]) + m_ext[0, 4, kk] = 0.0e0 + + for kk in range(1, nlayers): + ey_fac = ey_bar_t[kk] / ey_bar_t[kk - 1] + m_ext[0, 0, kk] = 0.5e0 * (ey_fac * (1 + nu_bar_t[kk]) + 1 - nu_bar_t[kk - 1]) + if rad[kk] > 0e0: + m_ext[0, 1, kk] = ( + 0.5e0 + / rad[kk] ** 2 + * (1 - nu_bar_t[kk - 1] - ey_fac * (1 - nu_bar_t[kk])) + ) + + m_ext[1, 0, kk] = rad[kk] ** 2 * (1 - m_ext[0, 0, kk]) + m_ext[1, 1, kk] = 1 - rad[kk] ** 2 * m_ext[0, 1, kk] + m_ext[1, 2, kk] = -(rad[kk] ** 2) * m_ext[0, 2, kk] + m_ext[1, 4, kk] = -(rad[kk] ** 2) * m_ext[0, 4, kk] + m_ext[2, 2, kk] = 1.0e0 + m_ext[3, 3, kk] = 1.0e0 + m_ext[4, 4, kk] = 1.0e0 + + # Total transformation matrix, from Lame parmeters at outside to + # Lame parameters at inside of each layer + # Section 7 in the writeup + # *** + m_tot[:, :, nlayers - 1] = m_int[:, :, nlayers - 1] + + for kk in range(nlayers - 2, -1, -1): + m_tot[:, :, kk] = np.ascontiguousarray(m_int[:, :, kk]) @ ( + np.ascontiguousarray(m_ext[:, :, kk + 1]) + @ np.ascontiguousarray(m_tot[:, :, kk + 1]) + ) + + # Axial force inner product. Dot-product this with the + # outermost solution vector, (A,B,eps_z,1.0,eps_z_slip), + # to obtain the axial force. + # Section 8 in the writeup + # *** + # Axial stiffness products + ey_bar_z_area = np.pi * sum( + ey_bar_z[nonslip_layer - 1 : nlayers] + * ( + rad[nonslip_layer : nlayers + 1] ** 2 + - rad[nonslip_layer - 1 : nlayers] ** 2 + ) + ) + ey_bar_z_area_slip = np.pi * sum( + ey_bar_z[: nonslip_layer - 1] + * (rad[1:nonslip_layer] ** 2 - rad[: nonslip_layer - 1] ** 2) + ) + + # Axial stiffness inner product, for layers which carry axial force + rad_row_helper[0, :] = [rad[nlayers] ** 2, 1e0, 0e0, 0e0, 0e0] + v_force_row[:, :] = ( + 2e0 * np.pi * ey_bar_z[nlayers - 1] * nu_bar_tz[nlayers - 1] * rad_row_helper + ) + rad_row_helper[0, :] = [rad[nonslip_layer - 1] ** 2, 1e0, 0e0, 0e0, 0e0] + v_force_row[:, :] = v_force_row - 2e0 * np.pi * ey_bar_z[ + nonslip_layer - 1 + ] * nu_bar_tz[nonslip_layer - 1] * ( + rad_row_helper @ np.ascontiguousarray(m_tot[:, :, nonslip_layer - 1]) + ) + for kk in range(nonslip_layer, nlayers): + rad_row_helper[0, :] = [rad[kk] ** 2, 1e0, 0e0, 0e0, 0e0] + v_force_row[:, :] = v_force_row + 2e0 * np.pi * ( + ey_bar_z[kk - 1] * nu_bar_tz[kk - 1] - ey_bar_z[kk] * nu_bar_tz[kk] + ) * (rad_row_helper @ np.ascontiguousarray(m_tot[:, :, kk])) + + # Include the effect of axial stiffness + v_force_row[0, 2] += ey_bar_z_area + + # Axial stiffness inner product, for layers which DON'T carry force + if nonslip_layer > 1: + rad_row_helper[0, :] = [rad[nonslip_layer - 1] ** 2, 1e0, 0e0, 0e0, 0e0] + v_force_row_slip[:, :] = ( + 2e0 + * np.pi + * ey_bar_z[nonslip_layer - 2] + * nu_bar_tz[nonslip_layer - 2] + * (rad_row_helper @ np.ascontiguousarray(m_tot[:, :, nonslip_layer - 1])) + ) + rad_row_helper[0, :] = [rad[0] ** 2, 1e0, 0e0, 0e0, 0e0] + v_force_row_slip[:, :] -= ( + 2e0 + * np.pi + * ey_bar_z[0] + * nu_bar_tz[0] + * (rad_row_helper @ np.ascontiguousarray(m_tot[:, :, 0])) + ) + for kk in range(1, nonslip_layer - 1): + rad_row_helper[0, :] = [rad[kk] ** 2, 1e0, 0e0, 0e0, 0e0] + v_force_row_slip[:, :] += ( + 2e0 + * np.pi + * (ey_bar_z[kk - 1] * nu_bar_tz[kk - 1] - ey_bar_z[kk] * nu_bar_tz[kk]) + * (rad_row_helper @ np.ascontiguousarray(m_tot[:, :, kk])) + ) + # Include the effect of axial stiffness + v_force_row_slip[0, 4] += ey_bar_z_area_slip + else: + # If there's no inner slip layer, still need a finite 5th + # element to ensure no singular matrix + v_force_row_slip[0, :] = [0e0, 0e0, 0e0, 0e0, 1e0] + + # Boundary condition matrix. Multiply this with the + # outermost solution vector, (A,B,eps_z,1.0,eps_z_slip), + # to obtain a zero vector. + # Solved to get the Lame parameters. + # Section 9 in the writeup + # *** + # Outer boundary condition row, zero radial stress + m_bc[0, :] = [ + (1e0 + nu_bar_t[nlayers - 1]) * rad[nlayers] ** 2, + -1e0 + nu_bar_t[nlayers - 1], + nu_bar_zt[nlayers - 1] * rad[nlayers] ** 2, + 0e0, + 0e0, + ] + # Inner boundary condition row, zero radial stress + # or zero displacement if rad(1)=0 + if nonslip_layer > 1: + m_bc[1, :] = [ + (1e0 + nu_bar_t[0]) * rad[0] ** 2, + -1e0 + nu_bar_t[0], + 0e0, + 0e0, + nu_bar_zt[0] * rad[0] ** 2, + ] + else: + m_bc[1, :] = [ + (1e0 + nu_bar_t[0]) * rad[0] ** 2, + -1e0 + nu_bar_t[0], + nu_bar_zt[0] * rad[0] ** 2, + 0e0, + 0e0, + ] + + m_bc[1, :] = np.ascontiguousarray(m_bc[1, :]) @ np.ascontiguousarray(m_tot[:, :, 0]) + # Axial force boundary condition + m_bc[2, :] = v_force_row[0, :] + m_bc[2, 3] = m_bc[2, 3] - v_force + # Axial force boundary condition of slip layers + m_bc[3, :] = v_force_row_slip[0, :] + + # The solution, the outermost Lame parameters A,B + # and the axial strains of the force-carrying and + # slip layers eps_z and eps_z_slip. + # Section 10 in the writeup + # *** + m_toinv[:, :3] = m_bc[ + :, + :3, + ] + m_toinv[:, 3] = m_bc[:, 4] + RHS_vec[:] = -m_bc[:, 3] + + a_vec_solution[:4] = np.linalg.solve(m_toinv, RHS_vec) + + a_vec_solution[4] = a_vec_solution[3] + a_vec_solution[3] = 1 + + # Radial/toroidal/vertical stress radial distribution + # ------ + # Radial displacement, stress and strain distributions + + a_vec_layer[:] = a_vec_solution[:] + for ii in range(nlayers - 1, -1, -1): + a_layer = a_vec_layer[0] + b_layer = a_vec_layer[1] + + dradius = (rad[ii + 1] - rad[ii]) / (n_radial_array - 1) + + for jj in range(ii * n_radial_array, (ii + 1) * n_radial_array): + rradius[jj] = rad[ii] + dradius * (jj - (n_radial_array * ii)) + + f_int_a_plot = 0.5e0 * f_lin_fac[ii] * ( + rad[ii + 1] ** 2 - rradius[jj] ** 2 + ) + f_rec_fac[ii] * np.log(rad[ii + 1] / (rradius[jj])) + f_int_b_plot = 0.25e0 * f_lin_fac[ii] * ( + rad[ii + 1] ** 4 - rradius[jj] ** 4 + ) + 0.5e0 * f_rec_fac[ii] * (rad[ii + 1] ** 2 - rradius[jj] ** 2) + a_plot = a_layer - 0.5e0 / ey_bar_t[ii] * f_int_a_plot + b_plot = b_layer + 0.5e0 / ey_bar_t[ii] * f_int_b_plot + + # Radial displacement + r_deflect[jj] = a_plot * rradius[jj] + b_plot / rradius[jj] + + # Radial strain + str_r[jj] = a_plot - b_plot / rradius[jj] ** 2 + # Azimuthal strain + str_t[jj] = a_plot + b_plot / rradius[jj] ** 2 + # Axial strain + if ii < nonslip_layer - 1: + str_z[jj] = a_vec_solution[4] + else: + str_z[jj] = a_vec_solution[2] + + # Radial stress + sigr[jj] = ey_bar_t[ii] * ( + str_r[jj] + (nu_bar_t[ii] * str_t[jj]) + (nu_bar_zt[ii] * str_z[jj]) + ) + # Aximuthal stress + sigt[jj] = ey_bar_t[ii] * ( + str_t[jj] + (nu_bar_t[ii] * str_r[jj]) + (nu_bar_zt[ii] * str_z[jj]) + ) + # Axial stress + sigz[jj] = ey_bar_z[ii] * ( + str_z[jj] + (nu_bar_tz[ii] * (str_r[jj] + str_t[jj])) + ) + + a_vec_layer = np.ascontiguousarray(m_tot[:, :, ii]) @ a_vec_solution + a_vec_layer = np.ascontiguousarray(m_ext[:, :, ii]) @ a_vec_layer + # ------ + + return rradius, sigr, sigt, sigz, str_r, str_t, str_z, r_deflect + + +@numba.njit(cache=True) +def plane_stress(nu, rad, ey, j, nlayers, n_radial_array): + """Calculates the stresses in a superconductor TF coil + inboard leg at the midplane using the plain stress approximation + author: P J Knight, CCFE, Culham Science Centre + author: J Morris, CCFE, Culham Science Centre + author: S Kahn, CCFE, Culham Science Centre + This routine calculates the stresses in a superconductor TF coil + inboard leg at midplane. +

A 2 layer plane stress model developed by CCFE is used. The first layer + is the steel case inboard of the winding pack, and the second + layer is the winding pack itself. + PROCESS Superconducting TF Coil Model, J. Morris, CCFE, 1st May 2014 + """ + alpha = np.zeros((nlayers,)) + beta = np.zeros((nlayers,)) + # Lorentz body force parametres + + area = np.zeros((nlayers,)) + # Layer area + + aa = np.zeros(( + 2 * nlayers, + 2 * nlayers, + )) + # Matrix encoding the integration constant cc coeficients + + bb = np.zeros((2 * nlayers,)) + # Vector encoding the alpha/beta (lorentz forces) contribution + + cc = np.zeros((2 * nlayers,)) + c1 = np.zeros((nlayers,)) + c2 = np.zeros((nlayers,)) + # Integration constants vector (solution) + + rradius = np.zeros((nlayers * n_radial_array,)) + # Radius array [m] + + sigr = np.zeros((nlayers * n_radial_array,)) + # Radial stress radial distribution [Pa] + + sigt = np.zeros((nlayers * n_radial_array,)) + # Toroidal stress radial distribution [Pa] + + r_deflect = np.zeros((nlayers * n_radial_array,)) + # Radial deflection (displacement) radial distribution [m] + + kk = ey / (1 - nu**2) + + # Lorentz forces parametrisation coeficients (array equation) + alpha = 0.5e0 * RMU0 * j**2 / kk + + inner_layer_curr = 0.0e0 + for ii in range(nlayers): + beta[ii] = ( + 0.5e0 + * RMU0 + * j[ii] + * (inner_layer_curr - np.pi * j[ii] * rad[ii] ** 2) + / (np.pi * kk[ii]) + ) + + # Layer area + area[ii] = np.pi * (rad[ii + 1] ** 2 - rad[ii] ** 2) + + # Total current carried by the inners layers + inner_layer_curr = inner_layer_curr + area[ii] * j[ii] + # *** + + # Null radial stress at R(1) + aa[0, 0] = kk[0] * (1.0e0 + nu[0]) + aa[0, 1] = -kk[0] * (1.0e0 - nu[0]) / (rad[0] ** 2) + + # Inter-layer boundary conditions + if nlayers != 1: + for ii in range(nlayers - 1): + # Continuous radial normal stress at R(ii+1) + aa[2 * ii + 1, 2 * ii] = kk[ii] * (1.0e0 + nu[ii]) + aa[2 * ii + 1, 2 * ii + 1] = -kk[ii] * (1.0e0 - nu[ii]) / rad[ii + 1] ** 2 + aa[2 * ii + 1, 2 * ii + 2] = -kk[ii + 1] * (1.0e0 + nu[ii + 1]) + aa[2 * ii + 1, 2 * ii + 3] = ( + kk[ii + 1] * (1.0e0 - nu[ii + 1]) / rad[ii + 1] ** 2 + ) + + # Continuous displacement at R(ii+1) + aa[2 * ii + 2, 2 * ii] = rad[ii + 1] + aa[2 * ii + 2, 2 * ii + 1] = 1.0e0 / rad[ii + 1] + aa[2 * ii + 2, 2 * ii + 2] = -rad[ii + 1] + aa[2 * ii + 2, 2 * ii + 3] = -1.0e0 / rad[ii + 1] + + # Radial stress = 0 + aa[2 * (nlayers - 1) + 1, 2 * (nlayers - 1)] = kk[nlayers - 1] * ( + 1.0e0 + nu[nlayers - 1] + ) + aa[2 * (nlayers - 1) + 1, 2 * (nlayers - 1) + 1] = ( + -kk[nlayers - 1] * (1.0e0 - nu[nlayers - 1]) / rad[nlayers] ** 2 + ) + # *** + + # Right hand side vector bb + # *** + # Null radial stress at R(1) + bb[0] = -kk[0] * ( + 0.125e0 * alpha[0] * (3.0e0 + nu[0]) * rad[0] ** 2 + + 0.5e0 * beta[0] * (1.0e0 + (1.0e0 + nu[0]) * np.log(rad[0])) + ) + + # Inter-layer boundary conditions + if nlayers != 1: + for ii in range(nlayers - 1): + # Continuous radial normal stress at R[ii+1] + bb[2 * ii + 1] = -kk[ii] * ( + 0.125e0 * alpha[ii] * (3.0e0 + nu[ii]) * rad[ii + 1] ** 2 + + 0.5e0 * beta[ii] * (1.0e0 + (1.0e0 + nu[ii]) * np.log(rad[ii + 1])) + ) + kk[ii + 1] * ( + 0.125e0 * alpha[ii + 1] * (3.0e0 + nu[ii + 1]) * rad[ii + 1] ** 2 + + 0.5e0 + * beta[ii + 1] + * (1.0e0 + (1.0e0 + nu[ii + 1]) * np.log(rad[ii + 1])) + ) + + # Continuous displacement at R(ii+1) + bb[2 * ii + 2] = ( + -0.125e0 * alpha[ii] * rad[ii + 1] ** 3 + - 0.5e0 * beta[ii] * rad[ii + 1] * np.log(rad[ii + 1]) + + 0.125e0 * alpha[ii + 1] * rad[ii + 1] ** 3 + + 0.5e0 * beta[ii + 1] * rad[ii + 1] * np.log(rad[ii + 1]) + ) + + # Null radial stress at R(nlayers+1) + bb[2 * (nlayers - 1) + 1] = -kk[nlayers - 1] * ( + 0.125e0 * alpha[nlayers - 1] * (3.0e0 + nu[nlayers - 1]) * rad[nlayers] ** 2 + + 0.5e0 + * beta[nlayers - 1] + * (1.0e0 + (1.0e0 + nu[nlayers - 1]) * np.log(rad[nlayers])) + ) + # *** + + # Find solution vector cc + # *** + aa = np.asfortranarray(aa) + cc = np.linalg.solve(aa, bb) + + # Multiply c by (-1) (John Last, internal CCFE memorandum, 21/05/2013) + for ii in range(nlayers): + c1[ii] = cc[2 * ii] + c2[ii] = cc[2 * ii + 1] + # *** + # ------ + + # Radial/toroidal/vertical stress radial distribution + # ------ + + for ii in range(nlayers): + dradius = (rad[ii + 1] - rad[ii]) / n_radial_array + for jj in range(ii * n_radial_array, (ii + 1) * n_radial_array): + rad_c = rad[ii] + dradius * (jj - n_radial_array * ii) + rradius[jj] = rad_c + + # Radial stress radial distribution [Pa] + sigr[jj] = kk[ii] * ( + (1.0e0 + nu[ii]) * c1[ii] + - ((1.0e0 - nu[ii]) * c2[ii]) / rad_c**2 + + 0.125e0 * (3.0e0 + nu[ii]) * alpha[ii] * rad_c**2 + + 0.5e0 * beta[ii] * (1.0e0 + (1.0e0 + nu[ii]) * np.log(rad_c)) + ) + + # Radial stress radial distribution [Pa] + sigt[jj] = kk[ii] * ( + (1.0e0 + nu[ii]) * c1[ii] + + (1.0e0 - nu[ii]) * c2[ii] / rad_c**2 + + 0.125e0 * (1.0e0 + 3.0e0 * nu[ii]) * alpha[ii] * rad_c**2 + + 0.5e0 * beta[ii] * (nu[ii] + (1.0e0 + nu[ii]) * np.log(rad_c)) + ) + + # Deflection [m] + r_deflect[jj] = ( + c1[ii] * rad_c + + c2[ii] / rad_c + + 0.125e0 * alpha[ii] * rad_c**3 + + 0.5e0 * beta[ii] * rad_c * np.log(rad_c) + ) + + return sigr, sigt, r_deflect, rradius + + +@numba.njit(cache=True) +def eyoung_parallel_array(n, eyoung_j_in, a_in, poisson_j_perp_in): + """ + Author : C. Swanson, PPPL + January 2022 + See Issue #1205 for derivation PDF + This subroutine gives the smeared elastic properties of two + members that are carrying a force in parallel with each other. + The force goes in direction j. + Members 1 and 2 are the individual members to be smeared. + Member 3 is the effective smeared member (output). + This is pretty easy because the smeared properties are simply + the average weighted by the cross-sectional areas perpendicular + to j. + The assumption is that the strains in j are equal. + If you're dealing with anisotropy, please pay attention to the + fact that the specific Young's Modulus used here is that in + the j direction, and the specific Poisson's ratio used here is + that between the j and transverse directions in that order. + (transverse strain / j strain, under j stress) + The smeared Poisson's ratio is computed assuming the transverse + dynamics are isotropic, and that the two members are free to + shrink/expand under Poisson effects without interference from + each other. This may not be true of your case. + + To build up a composite smeared member of any number of + individual members, you can pass the same properties for + members 2 and 3, and call it successively, using the properties + of each member as the first triplet of arguments. This way, the + last triplet acts as a "working sum": + call eyoung_parallel(triplet1, triplet2, tripletOUT) + call eyoung_parallel(triplet3, tripletOUT, tripletOUT) + call eyoung_parallel(triplet4, tripletOUT, tripletOUT) + ... etc. + So that tripletOUT would eventually have the smeared properties + of the total composite member. + """ + eyoung_j_out = 0 + a_out = 0 + poisson_j_perp_out = 0 + + # Parallel-composite them all together + for ii in range(n): + eyoung_j_out, a_out, poisson_j_perp_out = eyoung_parallel( + eyoung_j_in[ii], + a_in[ii], + poisson_j_perp_in[ii], + eyoung_j_out, + a_out, + poisson_j_perp_out, + ) + + return eyoung_j_out, a_out, poisson_j_perp_out + + +@numba.njit(cache=True) +def eyoung_t_nested_squares(n, eyoung_j_in, l_in, poisson_j_perp_in): + """ + Author : C. Swanson, PPPL + January 2022 + This subroutine gives the smeared transverse elastic + properties of n members whose cross sectional areas are + nested squares. It uses the subroutines eyoung_series and + eyoung_parallel, above, so please be aware of the assumptions + inherent in those subroutines. + + It assumes that each "leg" of the square cross section + (vertical slice, as described in Figure 10 of the TF coil + documentation) is composed of several layers under stress in + series, and each leg is under stress in parallel with every + other leg. + """ + eyoung_j_working = np.zeros((n,)) + l_working = np.zeros((n,)) + poisson_j_perp_working = np.zeros((n,)) + + # First member + eyoung_j_working[0] = eyoung_j_in[0] + l_working[0] = l_in[0] + poisson_j_perp_working[0] = poisson_j_perp_in[0] + + for ii in range(1, n): + # Initialize the leg of which this is the new member + eyoung_j_working[ii] = eyoung_j_in[ii] + l_working[ii] = l_working[ii - 1] + l_in[ii] + poisson_j_perp_working[ii] = poisson_j_perp_in[ii] + + # Serial-composite the new layer of this member into the previous legs + # changed from range(ii-1) because range(0) == [] + for jj in range(ii): + ( + eyoung_j_working[jj], + l_working[jj], + poisson_j_perp_working[jj], + ) = eyoung_series( + eyoung_j_working[ii], + l_in[ii], + poisson_j_perp_working[ii], + eyoung_j_working[jj], + l_working[jj], + poisson_j_perp_working[jj], + ) + + # Find stiffest leg + eyoung_stiffest = max(eyoung_j_working) + + eyoung_j_out = 0 + l_out = 0 + poisson_j_perp_out = 0 + + # Parallel-composite them all together + for ii in range(n): + eyoung_j_out, l_out, poisson_j_perp_out = eyoung_parallel( + eyoung_j_working[ii], + l_in[ii], + poisson_j_perp_working[ii], + eyoung_j_out, + l_out, + poisson_j_perp_out, + ) + + return eyoung_j_out, l_out, poisson_j_perp_out, eyoung_stiffest + + +@numba.njit(cache=True) +def eyoung_series(eyoung_j_1, l_1, poisson_j_perp_1, eyoung_j_2, l_2, poisson_j_perp_2): + """ + Author : C. Swanson, PPPL + January 2022 + See Issue #1205 for derivation PDF + This subroutine gives the smeared elastic properties of two + members that are carrying a force in series with each other. + The force goes in direction j. + The assumption is that the stresses in j are equal. + The smeared Young's modulus is the inverse of the average of + the inverse of the Young's moduli, weighted by the length + of the members in j. + Members 1 and 2 are the individual members to be smeared. + Member 3 is the effective smeared member (output). + The smeared Poisson's ratio is the averaged of the Poisson's + ratios, weighted by the quantity (Young's modulus / length of + the members in j). + + If you're dealing with anisotropy, please pay attention to the + fact that the specific Young's Modulus used here is that in + the j direction, and the specific Poisson's ratio used here is + that between the j and transverse directions in that order. + (transverse strain / j strain, under j stress) + The smeared Poisson's ratio is computed assuming the transverse + dynamics are isotropic, and that the two members are free to + shrink/expand under Poisson effects without interference from + each other. This may not be true of your case. + + To build up a composite smeared member of any number of + individual members, you can pass the same properties for + members 2 and 3, and call it successively, using the properties + of each member as the first triplet of arguments. This way, the + last triplet acts as a "working sum": + call eyoung_series(triplet1, triplet2, tripletOUT) + call eyoung_series(triplet3, tripletOUT, tripletOUT) + call eyoung_series(triplet4, tripletOUT, tripletOUT) + ... etc. + So that tripletOUT would eventually have the smeared properties + of the total composite member. + """ + + if eyoung_j_1 * eyoung_j_2 == 0: + # poisson_j_perp_3 = 0 + poisson_j_perp_3 = poisson_j_perp_1 if eyoung_j_1 == 0 else poisson_j_perp_2 + + eyoung_j_3 = 0.0 + l_3 = l_1 + l_2 + else: + poisson_j_perp_3 = ( + poisson_j_perp_1 * l_1 / eyoung_j_1 + poisson_j_perp_2 * l_2 / eyoung_j_2 + ) / (l_1 / eyoung_j_1 + l_2 / eyoung_j_2) + eyoung_j_3 = (l_1 + l_2) / (l_1 / eyoung_j_1 + l_2 / eyoung_j_2) + l_3 = l_1 + l_2 + + eyoung_j_3 = np.array(eyoung_j_3) + poisson_j_perp_3 = np.array(poisson_j_perp_3) + + return eyoung_j_3, l_3, poisson_j_perp_3 diff --git a/process/tfcoil.py b/process/tfcoil.py deleted file mode 100644 index 76b006ccac..0000000000 --- a/process/tfcoil.py +++ /dev/null @@ -1,575 +0,0 @@ -import copy - -import numpy as np - -from process import fortran as ft -from process import process_output as po -from process.build import Build -from process.fortran import build_variables as bv -from process.fortran import constants -from process.fortran import error_handling as eh -from process.fortran import fwbs_variables as fwbsv -from process.fortran import tfcoil_variables as tfv -from process.sctfcoil import Sctfcoil - -TF_TYPES = { - 1: "Nb3Sn ITER", - 2: "Bi-2212", - 3: "NbTi", - 4: "Nb3Sn user", - 5: "Nb3Sn WST", - 6: "REBCO Croco", - 7: "NbTi Ginzburg-Landau", - 8: "REBCO Ginzburg-Landau", - 9: "REBCO Hazelton-Zhai", -} - - -class TFcoil: - """Calculates the parameters of a resistive TF coil system for a fusion power plant""" - - def __init__(self, build: Build, sctfcoil: Sctfcoil): - """Initialise Fortran module variables.""" - self.outfile = ft.constants.nout # output file unit - self.iprint = 0 # switch for writing to output file (1=yes) - self.build = build - self.sctfcoil = sctfcoil - - def run(self): - """Run main tfcoil subroutine without outputting.""" - self.iprint = 0 - self.tfcoil() - - def output(self): - """Run main tfcoil subroutine and write output.""" - self.iprint = 1 - self.tfcoil() - - def tfcoil(self): - """TF coil module - author: P J Knight, CCFE, Culham Science Centre - outfile : input integer : output file unit - iprint : input integer : switch for writing to output file (1=yes) - This subroutine calculates various parameters for the TF coil set. - If the TF coils are superconducting the calculations are performed - in routine sctfcoil instead. - """ - - # TF coil calculations - self.sctfcoil.sctfcoil(output=bool(self.iprint)) - - # Port size calculation - self.build.portsz() - - def cntrpst(self): - """ - Evaluates the properties of a TART centrepost - author: P J Knight, CCFE, Culham Science Centre - outfile : input integer : output file unit - iprint : input integer : switch for writing to output file (1=yes) - This subroutine evaluates the parameters of the centrepost for a - tight aspect ratio tokamak. The centrepost is assumed to be tapered, - i.e. narrowest on the midplane (z=0). - """ - - # Temperature margin used in calculations (K) - tmarg = 10.0e0 - - # Number of integral step used for the coolant temperature rise - n_tcool_it = 20 - - # Coolant channels: - acool = tfv.a_cp_cool * tfv.n_tf_coils # Cooling cross-sectional area - dcool = 2.0e0 * tfv.rcool # Diameter - lcool = 2.0e0 * (bv.hmax + bv.dr_tf_outboard) # Length - tfv.ncool = acool / (constants.pi * tfv.rcool**2) # Number - - # Average conductor cross-sectional area to cool (with cooling area) - acpav = 0.5e0 * tfv.vol_cond_cp / (bv.hmax + bv.dr_tf_outboard) + acool - ro = (acpav / (constants.pi * tfv.ncool)) ** 0.5 - - # Inner legs total heating power (to be removed by coolant) - ptot = tfv.p_cp_resistive + fwbsv.pnuc_cp_tf * 1.0e6 - - # Temperature calculations - # ------------------------- - # Temperature rise in coolant (inlet to outlet) - # ********************************************** - # Water coollant - # -------------- - if tfv.i_tf_sup == 0: - # Water coolant physical properties - coolant_density = constants.denh2o - coolant_cp = constants.cph2o - coolant_visco = constants.muh2o - coolant_th_cond = constants.kh2o - - # Mass flow rate [kg/s] - cool_mass_flow = acool * coolant_density * tfv.vcool - - # Water temperature rise - tfv.dtiocool = ptot / (cool_mass_flow * coolant_cp) - - # Constant coolant velocity - vcool_max = tfv.vcool - # -------------- - - # Helium coolant - # -------------- - elif tfv.i_tf_sup == 2: - # Inlet coolant density [kg/m3] - coolant_density = self.he_density(tfv.tcoolin) - - # Mass flow rate [kg/s] - cool_mass_flow = acool * coolant_density * tfv.vcool - - # Infinitesimal power deposition used in the integral - dptot = ptot / n_tcool_it - - tcool_calc = copy.copy(tfv.tcoolin) # K - for _i in range(n_tcool_it): - # Thermal capacity Cp - coolant_cp = self.he_cp(tcool_calc) - - # Temperature infinitesimal increase - tcool_calc += dptot / (cool_mass_flow * coolant_cp) - - # Outlet coolant density (minimal coolant density value) - coolant_density = self.he_density(tcool_calc) - - # Maxium coolant velocity - vcool_max = cool_mass_flow / (acool * coolant_density) - - # Getting the global in-outlet temperature increase - tfv.dtiocool = tcool_calc - tfv.tcoolin - # -------------- - - # Average coolant temperature - tcool_av = tfv.tcoolin + 0.5e0 * tfv.dtiocool - # ********************************************** - - # Film temperature rise - # ********************* - # Rem : The helium cooling properties are calculated using the outlet ones - # this is not an exact approximation for average temperature rise - - # Helium viscosity - if tfv.i_tf_sup == 2: - coolant_visco = self.he_visco(tcool_av) - - # Reynolds number - reyn = coolant_density * tfv.vcool * dcool / coolant_visco - - # Helium thermal conductivity [W/(m.K)] - if tfv.i_tf_sup == 2: - coolant_th_cond = self.he_th_cond(tcool_av) - - # Prandlt number - prndtl = coolant_cp * coolant_visco / coolant_th_cond - - # Film temperature difference calculations - # Originally prandtl was prndtl**0.3e0 but this is incorrect as from - # Dittus-Boelter correlation where the fluid is being heated it should be as below - nuselt = 0.023e0 * reyn**0.8e0 * prndtl**0.4e0 - h = nuselt * coolant_th_cond / dcool - dtfilmav = ptot / (h * 2.0e0 * constants.pi * tfv.rcool * tfv.ncool * lcool) - - # Average film temperature (in contact with te conductor) - tcool_film = tcool_av + dtfilmav - # ********************* - - # Temperature rise in conductor - # ------------------------------ - # Conductor thermal conductivity - # ****** - # Copper conductor - if tfv.i_tf_sup == 0: - conductor_th_cond = constants.k_copper - - # Aluminium - elif tfv.i_tf_sup == 2: - conductor_th_cond = self.al_th_cond(tcool_film) - # ****** - - # Average temperature rise : To be changed with Garry Voss' better documented formula ? - dtcncpav = ( - (ptot / tfv.vol_cond_cp) - / (2.0e0 * conductor_th_cond * (ro**2 - tfv.rcool**2)) - * ( - ro**2 * tfv.rcool**2 - - 0.25e0 * tfv.rcool**4 - - 0.75e0 * ro**4 - + ro**4 * np.log(ro / tfv.rcool) - ) - ) - - # Peak temperature rise : To be changed with Garry Voss' better documented formula ? - dtconcpmx = ( - (ptot / tfv.vol_cond_cp) - / (2.0e0 * conductor_th_cond) - * ((tfv.rcool**2 - ro**2) / 2.0e0 + ro**2 * np.log(ro / tfv.rcool)) - ) - - # If the average conductor temperature difference is negative, set it to 0 - if dtcncpav < 0.0e0: - eh.report_error(249) - dtcncpav = 0.0e0 - - # If the average conductor temperature difference is negative, set it to 0 - if dtconcpmx < 0.0e0: - eh.report_error(250) - dtconcpmx = 0.0e0 - - # Average conductor temperature - tfv.tcpav2 = tfv.tcoolin + dtcncpav + dtfilmav + 0.5e0 * tfv.dtiocool - - # Peak wall temperature - tfv.tcpmax = tfv.tcoolin + tfv.dtiocool + dtfilmav + dtconcpmx - tcoolmx = tfv.tcoolin + tfv.dtiocool + dtfilmav - # ------------------------- - - # Thermal hydraulics: friction factor from Z. Olujic, Chemical - # Engineering, Dec. 1981, p. 91 - roughrat = 4.6e-5 / dcool - fricfac = ( - 1.0e0 - / ( - -2.0e0 - * np.log10( - roughrat / 3.7e0 - - 5.02e0 / reyn * np.log10(roughrat / 3.7e0 + 14.5e0 / reyn) - ) - ) - ** 2 - ) - - # Pumping efficiency - if tfv.i_tf_sup == 0: # Water cooled - tfv.etapump = 0.8e0 - elif tfv.i_tf_sup == 2: # Cryogenic helium - tfv.etapump = 0.6e0 - - # Pressure drop calculation - dpres = fricfac * (lcool / dcool) * coolant_density * 0.5e0 * tfv.vcool**2 - tfv.ppump = dpres * acool * tfv.vcool / tfv.etapump - - # Critical pressure in saturation pressure calculations (Pa) - pcrt = 2.24e7 - - # Saturation pressure - # Ref : Keenan, Keyes, Hill, Moore, steam tables, Wiley & Sons, 1969 - # Rem 1 : ONLY VALID FOR WATER ! - # Rem 2 : Not used anywhere else in the code ... - tclmx = tcoolmx + tmarg - tclmxs = min(tclmx, 374.0e0) - fc = 0.65e0 - 0.01e0 * tclmxs - sum_ = ( - -741.9242e0 - - 29.721e0 * fc - - 11.55286e0 * fc**2 - - 0.8685635e0 * fc**3 - + 0.1094098e0 * fc**4 - + 0.439993e0 * fc**5 - + 0.2520658e0 * fc**6 - + 0.0518684e0 * fc**7 - ) - psat = pcrt * np.exp(0.01e0 / (tclmxs + 273.0e0) * (374.0e0 - tclmxs) * sum_) - presin = psat + dpres - - # Output section - if self.iprint == 1: - po.oheadr(self.outfile, "Centrepost Coolant Parameters") - po.ovarre( - self.outfile, "Centrepost coolant fraction", "(fcoolcp)", tfv.fcoolcp - ) - po.ovarre( - self.outfile, "Average coolant channel diameter (m)", "(dcool)", dcool - ) - po.ovarre(self.outfile, "Coolant channel length (m)", "(lcool)", lcool) - po.ovarre( - self.outfile, "Inlet coolant flow speed (m/s)", "(vcool)", tfv.vcool - ) - po.ovarre( - self.outfile, - "Outlet coolant flow speed (m/s)", - "(vcool_max)", - vcool_max, - ) - po.ovarre( - self.outfile, - "Coolant mass flow rate (kg/s)", - "(cool_mass_flow)", - cool_mass_flow, - ) - po.ovarre(self.outfile, "Number of coolant tubes", "(ncool)", tfv.ncool) - po.ovarre(self.outfile, "Reynolds number", "(reyn)", reyn) - po.ovarre(self.outfile, "Prandtl number", "(prndtl)", prndtl) - po.ovarre(self.outfile, "Nusselt number", "(nuselt)", nuselt) - - po.osubhd(self.outfile, "Resistive Heating :") - po.ovarre( - self.outfile, - "Average conductor resistivity (ohm.m)", - "(rho_cp)", - tfv.rho_cp, - ) - po.ovarre( - self.outfile, - "Resistive heating (MW)", - "(p_cp_resistive/1.0e6)", - tfv.p_cp_resistive / 1.0e6, - ) - po.ovarre( - self.outfile, "Nuclear heating (MW)", "(pnuc_cp_tf)", fwbsv.pnuc_cp_tf - ) - po.ovarre(self.outfile, "Total heating (MW)", "(ptot/1.0e6)", ptot / 1.0e6) - - po.osubhd(self.outfile, "Temperatures :") - po.ovarre( - self.outfile, - "Input coolant temperature (K)", - "(tfv.tcoolin)", - tfv.tcoolin, - ) - po.ovarre( - self.outfile, - "Input-output coolant temperature rise (K)", - "(dtiocool)", - tfv.dtiocool, - ) - po.ovarre(self.outfile, "Film temperature rise (K)", "(dtfilmav)", dtfilmav) - po.ovarre( - self.outfile, - "Average temp gradient in conductor (K/m)", - "(dtcncpav)", - dtcncpav, - ) - po.ovarre( - self.outfile, - "Average centrepost temperature (K)", - "(tcpav2)", - tfv.tcpav2, - ) - po.ovarre( - self.outfile, "Peak centrepost temperature (K)", "(tcpmax)", tfv.tcpmax - ) - - po.osubhd(self.outfile, "Pump Power :") - po.ovarre(self.outfile, "Coolant pressure drop (Pa)", "(dpres)", dpres) - if tfv.i_tf_sup == 0: # Saturation pressure calculated with Water data ... - po.ovarre( - self.outfile, "Coolant inlet pressure (Pa)", "(presin)", presin - ) - - po.ovarre(self.outfile, "Pump power (W)", "(ppump)", tfv.ppump) - - @staticmethod - def he_density(temp: float) -> float: - """Author : S. Kahn - Subroutine calculating temperature dependent helium density at 100 bar - from fit using the following data, valid in [4-50] K - Ref : R.D. McCarty, Adv. Cryo. Eng., 1990, 35, 1465-1475. - - :param temp: Helium temperature [K] - :type temp: float - - :returns density: Heliyn density [kg/m3] - :type density: float - """ - - # Fit range validation - if temp < 4.0e0 or temp > 50.0e0: - eh.fdiags[0] = temp - eh.report_error(257) - - # Oder 3 polynomial fit - if temp < 29.5e0: - density = ( - 217.753831e0 - - 1.66564525e0 * temp - - 0.160654724e0 * temp**2 - + 0.00339003258e0 * temp**3 - ) - - # Linear interpolation between the fits to avoid discontinuity - elif temp < 30.5e0: - density = 231.40661479377616e0 - 3.917589985552496e0 * temp - - # Oder 2 polynomial fit - else: - density = 212.485251e0 - 4.18059786e0 * temp + 0.0289632937e0 * temp**2 - - return density - - @staticmethod - def he_cp(temp: float) -> float: - """Author : S. Kahn - Subroutine calculating temperature dependent thermal capacity at - constant pressures at 100 Bar from fit using the following data - valid in [4-50] K - Ref : R.D. McCarty, Adv. Cryo. Eng., 1990, 35, 1465-1475. - - :param temp: Helium temperature [K] - :type temp: float - - :return cp: Themal capacity at constant pressure [K/(kg.K)] - :type cp: float - """ - - # Fit range validation - if temp < 4.0e0 or temp > 50.0e0: - eh.fdiags[0] = temp - eh.report_error(257) - - # Order 3 polynomial fit in [4-30] K on the dimenion [K/(g.K)] - if temp < 29.5e0: - cp = ( - -0.834218557e0 - + 0.637079569e0 * temp - - 0.0208839696e0 * temp**2 - + 0.000233433748e0 * temp**3 - ) - - # Linear interpolation between the fits to avoid discontinuity - elif ( - temp < 30.5e0 - ): # Linear interpolation between the fits to avoid discontinuity - cp = 4.924018467550791e0 + 0.028953709588498633e0 * temp - - # Linear fit in [30-60] K on the dimenion [K/(g.K)] - else: - cp = 6.11883125e0 - 0.01022048e0 * temp - - # conversion to [K/(kg.K)] and return - return cp * 1.0e3 - - @staticmethod - def he_visco(temp: float) -> float: - """Author : S. Kahn - Subroutine calculating temperature dependent He viscosity at 100 Bar - from fit using the following data, valid in [4-50] K - Ref : V.D. Arp,; R.D. McCarty ; Friend, D.G., Technical Note 1334, National - Institute of Standards and Technology, Boulder, CO, 1998, 0. - - :param temp: Helium temperature [K] - :type temp: float - - :return visco: Themal capacity at constant pressure [Pa.s] - :type visco: float - """ - - if temp < 4.0e0 or temp > 50.0e0: - eh.fdiags[0] = temp - eh.report_error(257) - - # Order 4 polynomial exponential fit in [4-25] K - if temp < 22.5e0: - visco = np.exp( - -9.19688182e0 - - 4.83007225e-1 * temp - + 3.47720002e-2 * temp**2 - - 1.17501538e-3 * temp**3 - + 1.54218249e-5 * temp**4 - ) - - # Linear interpolation between the fits to avoid discontinuity - elif temp < 27.5e0: - visco = 6.708587487790973e-6 + 5.776427353055518e-9 * temp - - # Linear fit in [25-60] K - else: - visco = 5.41565319e-6 + 5.279222e-8 * temp - - return visco - - @staticmethod - def he_th_cond(temp: float) -> float: - """Author : S. Kahn - Subroutine calculating temperature dependent He thermal conductivity - at 100 Bar from fit using the following data, valid in [4-50] K - Ref : B.A. Hands B.A., Cryogenics, 1981, 21, 12, 697-703. - - :param temp: Helium temperature [K] - :type temp: float - - :return th_cond: Themal conductivity [W/(m.K)] - :type th_cond: float - """ - - # Fit range validation - if temp < 4.0e0 or temp > 50.0e0: - eh.fdiags[0] = temp - eh.report_error(257) - - # Order 4 polynomial fit - if temp < 24.0e0: - th_cond = ( - -7.56066334e-3 - + 1.62626819e-2 * temp - - 1.3633619e-3 * temp**2 - + 4.84227752e-5 * temp**3 - - 6.31264281e-7 * temp**4 - ) - - # Linear interpolation between the fits to avoid discontinuity - elif temp < 25.0e0: - th_cond = 0.05858194642349288e0 - 5.706361831471496e-5 * temp - - # Order 2 polynomial fit - elif temp < 50.0e0: - th_cond = ( - 0.0731268577e0 - - 0.0013826223e0 * temp - + 3.55551245e-5 * temp**2 - - 2.32185411e-7 * temp**3 - ) - - # Linear interpolation between the fits to avoid discontinuity - elif temp < 51.0e0: - th_cond = 4.450475632499988e-2 + 3.871124250000024e-4 * temp - - # Linear fit - else: - th_cond = 0.04235676e0 + 0.00042923e0 * temp - - return th_cond - - @staticmethod - def al_th_cond(temp: float) -> float: - """Author : S. Kahn - Subroutine calculating temperature dependent Al thermal conductivity - - :param temp: Helium temperature [K] - :type temp: float - - :return th_cond: Themal conductivity [W/(m.K)] - :type th_cond: float - """ - - # Fiting range verification - if temp < 15.0e0 or temp > 150.0e0: - eh.fdiags[0] = temp - eh.report_error(258) - - # fit 15 < T < 60 K (order 3 poly) - if temp < 60.0e0: - th_cond = ( - 16332.2073e0 - - 776.91775e0 * temp - + 13.405688e0 * temp**2 - - 8.01e-02 * temp**3 - ) - - # Linear interpolation between the fits to avoid discontinuity - elif temp < 70.0e0: - th_cond = 1587.9108966527328e0 - 15.19819661087886e0 * temp - - # fit 70 < T < 150 K (order 2 poly) - elif temp < 150.0e0: - th_cond = 1782.77406e0 - 24.7778504e0 * temp + 9.70842050e-2 * temp**2 - - # constant value after that set with the fit upper limit to avoid discontinuities - else: - th_cond = 250.4911087866094e0 - - return th_cond diff --git a/tests/unit/test_resistive_tf_coil.py b/tests/unit/test_resistive_tf_coil.py new file mode 100644 index 0000000000..ea396d7496 --- /dev/null +++ b/tests/unit/test_resistive_tf_coil.py @@ -0,0 +1,698 @@ +from typing import Any, NamedTuple + +import pytest + +from process.fortran import ( + build_variables, + physics_variables, + sctfcoil_module, + tfcoil_variables, +) +from process.resistive_tf_coil import ResistiveTFCoil + + +@pytest.fixture +def resistive_tf_coil(): + """Provides SuperconductingTFCoil object for testing. + + :returns: initialised SuperconductingTFCoil object + :rtype: process.sctfcoil.SuperconductingTFCoil + """ + return ResistiveTFCoil() + + +class ResTfInternalGeomParam(NamedTuple): + n_tf_turn: Any = None + + thicndut: Any = None + + thkcas: Any = None + + dr_tf_wp: Any = None + + tftort: Any = None + + tfareain: Any = None + + c_tf_total: Any = None + + fcoolcp: Any = None + + cpttf: Any = None + + cdtfleg: Any = None + + casthi: Any = None + + aiwp: Any = None + + acasetf: Any = None + + tinstf: Any = None + + n_tf_coils: Any = None + + dr_tf_outboard: Any = None + + r_tf_inboard_in: Any = None + + r_tf_inboard_out: Any = None + + r_cp_top: Any = None + + itart: Any = None + + expected_n_tf_turn: Any = None + + expected_cpttf: Any = None + + expected_cdtfleg: Any = None + + expected_aiwp: Any = None + + expected_acasetf: Any = None + + +@pytest.mark.parametrize( + "restfinternalgeomparam", + ( + ResTfInternalGeomParam( + n_tf_turn=0, + thicndut=0.00080000000000000004, + thkcas=0, + dr_tf_wp=0.15483000000000002, + tftort=0.45367650933034859, + tfareain=0.0753112923616783, + c_tf_total=25500000, + fcoolcp=0.12725, + cpttf=70000, + cdtfleg=0, + casthi=0.0077415000000000019, + aiwp=0, + acasetf=0, + tinstf=0, + n_tf_coils=12, + dr_tf_outboard=0.15483000000000002, + r_tf_inboard_in=0, + r_tf_inboard_out=0.15483000000000002, + r_cp_top=0.87643571428571443, + itart=1, + expected_n_tf_turn=1, + expected_cpttf=2125000, + expected_cdtfleg=421788350.27812088, + expected_aiwp=0.00030678028680367151, + expected_acasetf=0.00061190425043863676, + ), + ResTfInternalGeomParam( + n_tf_turn=1, + thicndut=0.00080000000000000004, + thkcas=0, + dr_tf_wp=0.14708850000000001, + tftort=0.44435902370665786, + tfareain=0.0753112923616783, + c_tf_total=25500000, + fcoolcp=0.12725, + cpttf=2125000, + cdtfleg=421788350.27812088, + casthi=0.0077415000000000019, + aiwp=0.00030678028680367151, + acasetf=0.00061190425043863676, + tinstf=0, + n_tf_coils=12, + dr_tf_outboard=0.15483000000000002, + r_tf_inboard_in=0, + r_tf_inboard_out=0.15483000000000002, + r_cp_top=0.85843571428571441, + itart=1, + expected_n_tf_turn=1, + expected_cpttf=2125000, + expected_cdtfleg=430664525.98439038, + expected_aiwp=0.00029439388680367086, + expected_acasetf=0.00061190425043863676, + ), + ), +) +def test_res_tf_internal_geom(restfinternalgeomparam, monkeypatch, resistive_tf_coil): + """ + Automatically generated Regression Unit Test for res_tf_internal_geom. + + This test was generated using data from tests/regression/scenarios/FNSF/IN.DAT. + + :param restfinternalgeomparam: the data used to mock and assert in this test. + :type restfinternalgeomparam: restfinternalgeomparam + + :param monkeypatch: pytest fixture used to mock module/class variables + :type monkeypatch: _pytest.monkeypatch.monkeypatch + + :param sctfcoil: initialised SuperconductingTFCoil object + :type sctfcoil: process.sctfcoil.SuperconductingTFCoil + """ + + monkeypatch.setattr(tfcoil_variables, "n_tf_turn", restfinternalgeomparam.n_tf_turn) + + monkeypatch.setattr(tfcoil_variables, "thicndut", restfinternalgeomparam.thicndut) + + monkeypatch.setattr(tfcoil_variables, "thkcas", restfinternalgeomparam.thkcas) + + monkeypatch.setattr(tfcoil_variables, "dr_tf_wp", restfinternalgeomparam.dr_tf_wp) + + monkeypatch.setattr(tfcoil_variables, "tftort", restfinternalgeomparam.tftort) + + monkeypatch.setattr(tfcoil_variables, "tfareain", restfinternalgeomparam.tfareain) + + monkeypatch.setattr( + tfcoil_variables, "c_tf_total", restfinternalgeomparam.c_tf_total + ) + + monkeypatch.setattr(tfcoil_variables, "fcoolcp", restfinternalgeomparam.fcoolcp) + + monkeypatch.setattr(tfcoil_variables, "cpttf", restfinternalgeomparam.cpttf) + + monkeypatch.setattr(tfcoil_variables, "cdtfleg", restfinternalgeomparam.cdtfleg) + + monkeypatch.setattr(tfcoil_variables, "casthi", restfinternalgeomparam.casthi) + + monkeypatch.setattr(tfcoil_variables, "aiwp", restfinternalgeomparam.aiwp) + + monkeypatch.setattr(tfcoil_variables, "acasetf", restfinternalgeomparam.acasetf) + + monkeypatch.setattr(tfcoil_variables, "tinstf", restfinternalgeomparam.tinstf) + + monkeypatch.setattr( + tfcoil_variables, "n_tf_coils", restfinternalgeomparam.n_tf_coils + ) + + monkeypatch.setattr( + build_variables, "dr_tf_outboard", restfinternalgeomparam.dr_tf_outboard + ) + + monkeypatch.setattr( + build_variables, "r_tf_inboard_in", restfinternalgeomparam.r_tf_inboard_in + ) + + monkeypatch.setattr( + build_variables, "r_tf_inboard_out", restfinternalgeomparam.r_tf_inboard_out + ) + + monkeypatch.setattr(build_variables, "r_cp_top", restfinternalgeomparam.r_cp_top) + + monkeypatch.setattr(physics_variables, "itart", restfinternalgeomparam.itart) + + resistive_tf_coil.res_tf_internal_geom() + + assert tfcoil_variables.n_tf_turn == pytest.approx( + restfinternalgeomparam.expected_n_tf_turn + ) + + assert tfcoil_variables.cpttf == pytest.approx( + restfinternalgeomparam.expected_cpttf + ) + + assert tfcoil_variables.cdtfleg == pytest.approx( + restfinternalgeomparam.expected_cdtfleg + ) + + assert tfcoil_variables.aiwp == pytest.approx(restfinternalgeomparam.expected_aiwp) + + assert tfcoil_variables.acasetf == pytest.approx( + restfinternalgeomparam.expected_acasetf + ) + + assert tfcoil_variables.n_tf_turn == pytest.approx( + restfinternalgeomparam.expected_n_tf_turn + ) + + assert tfcoil_variables.cpttf == pytest.approx( + restfinternalgeomparam.expected_cpttf + ) + + assert tfcoil_variables.cdtfleg == pytest.approx( + restfinternalgeomparam.expected_cdtfleg + ) + + assert tfcoil_variables.aiwp == pytest.approx(restfinternalgeomparam.expected_aiwp) + + assert tfcoil_variables.acasetf == pytest.approx( + restfinternalgeomparam.expected_acasetf + ) + + +class TfResHeatingParam(NamedTuple): + rho_cp: Any = None + temp_tf_legs_outboard: Any = None + thicndut: Any = None + th_joint_contact: Any = None + rho_tf_leg: Any = None + vol_cond_cp: Any = None + n_tf_turn: Any = None + thkcas: Any = None + tftort: Any = None + len_tf_coil: Any = None + res_tf_leg: Any = None + temp_cp_average: Any = None + a_tf_leg_outboard: Any = None + c_tf_total: Any = None + rho_tf_joints: Any = None + p_tf_leg_resistive: Any = None + p_cp_resistive: Any = None + pres_joints: Any = None + n_tf_joints_contact: Any = None + n_tf_joints: Any = None + n_tf_coils: Any = None + i_tf_sup: Any = None + frholeg: Any = None + frhocp: Any = None + fcoolcp: Any = None + casthi: Any = None + a_cp_cool: Any = None + f_a_tf_cool_outboard: Any = None + i_cp_joints: Any = None + tinstf: Any = None + dr_tf_outboard: Any = None + dr_tf_inboard: Any = None + r_cp_top: Any = None + hmax: Any = None + r_tf_inboard_in: Any = None + r_tf_inboard_out: Any = None + itart: Any = None + h_cp_top: Any = None + is_leg_cp_temp_same: Any = None + expected_rho_cp: Any = None + expected_rho_tf_leg: Any = None + expected_vol_cond_cp: Any = None + expected_res_tf_leg: Any = None + expected_p_tf_leg_resistive: Any = None + expected_p_cp_resistive: Any = None + expected_pres_joints: Any = None + expected_a_cp_cool: Any = None + expected_is_leg_cp_temp_same: Any = None + + +@pytest.mark.parametrize( + "tfresheatingparam", + ( + TfResHeatingParam( + rho_cp=0, + temp_tf_legs_outboard=-1, + thicndut=0.00080000000000000004, + th_joint_contact=0.029999999999999999, + rho_tf_leg=0, + vol_cond_cp=0, + n_tf_turn=1, + thkcas=0, + tftort=0.45367650933034859, + len_tf_coil=15.582502857142856, + res_tf_leg=0, + temp_cp_average=347.13, + a_tf_leg_outboard=0.070242733939617885, + c_tf_total=25500000, + rho_tf_joints=2.5000000000000002e-10, + p_tf_leg_resistive=0, + p_cp_resistive=0, + pres_joints=0, + n_tf_joints_contact=6, + n_tf_joints=4, + n_tf_coils=12, + i_tf_sup=0, + frholeg=1, + frhocp=1, + fcoolcp=0.12725, + casthi=0.0077415000000000019, + a_cp_cool=0, + f_a_tf_cool_outboard=0.20000000000000001, + i_cp_joints=1, + tinstf=0, + dr_tf_outboard=0.15483000000000002, + dr_tf_inboard=0.15483000000000002, + r_cp_top=0.87643571428571443, + hmax=4.4214285714285717, + r_tf_inboard_in=0, + r_tf_inboard_out=0.15483000000000002, + itart=1, + h_cp_top=2.6714285714285717, + is_leg_cp_temp_same=0, + expected_rho_cp=2.0721414e-08, + expected_rho_tf_leg=2.0721414e-08, + expected_vol_cond_cp=12.020160732580297, + expected_res_tf_leg=5.826541893267926e-06, + expected_p_tf_leg_resistive=315725738.84145576, + expected_p_cp_resistive=446175692.00121117, + expected_pres_joints=1944336.7995005273, + expected_a_cp_cool=0.00068328705812121333, + expected_is_leg_cp_temp_same=1, + ), + TfResHeatingParam( + rho_cp=2.0721414e-08, + temp_tf_legs_outboard=-1, + thicndut=0.00080000000000000004, + th_joint_contact=0.029999999999999999, + rho_tf_leg=2.0721414e-08, + vol_cond_cp=12.020160732580297, + n_tf_turn=1, + thkcas=0, + tftort=0.44435902370665786, + len_tf_coil=15.654502857142857, + res_tf_leg=5.647653956699231e-06, + temp_cp_average=347.13, + a_tf_leg_outboard=0.068800107640501845, + c_tf_total=25500000, + rho_tf_joints=2.5000000000000002e-10, + p_tf_leg_resistive=332643748.67243439, + p_cp_resistive=432477095.0716282, + pres_joints=1944336.7995005273, + n_tf_joints_contact=6, + n_tf_joints=4, + n_tf_coils=12, + i_tf_sup=0, + frholeg=1, + frhocp=1, + fcoolcp=0.12725, + casthi=0.0077415000000000019, + a_cp_cool=0.00068328705812121333, + f_a_tf_cool_outboard=0.20000000000000001, + i_cp_joints=1, + tinstf=0, + dr_tf_outboard=0.15483000000000002, + dr_tf_inboard=0.15483000000000002, + r_cp_top=0.85843571428571441, + hmax=4.4214285714285717, + r_tf_inboard_in=0, + r_tf_inboard_out=0.15483000000000002, + itart=1, + h_cp_top=2.6714285714285717, + is_leg_cp_temp_same=1, + expected_rho_cp=2.0721414e-08, + expected_rho_tf_leg=2.0721414e-08, + expected_vol_cond_cp=11.545770024935592, + expected_res_tf_leg=5.9766449694251195e-06, + expected_p_tf_leg_resistive=323859449.2807237, + expected_p_cp_resistive=451516213.33864105, + expected_pres_joints=1944336.7995005273, + expected_a_cp_cool=0.00068328705812121333, + expected_is_leg_cp_temp_same=1, + ), + ), +) +def test_tf_res_heating(tfresheatingparam, monkeypatch, resistive_tf_coil): + """ + Automatically generated Regression Unit Test for tf_res_heating. + + This test was generated using data from tests/regression/scenarios/FNSF/IN.DAT. + + :param tfresheatingparam: the data used to mock and assert in this test. + :type tfresheatingparam: tfresheatingparam + + :param monkeypatch: pytest fixture used to mock module/class variables + :type monkeypatch: _pytest.monkeypatch.monkeypatch + """ + + monkeypatch.setattr(tfcoil_variables, "rho_cp", tfresheatingparam.rho_cp) + + monkeypatch.setattr( + tfcoil_variables, + "temp_tf_legs_outboard", + tfresheatingparam.temp_tf_legs_outboard, + ) + + monkeypatch.setattr(tfcoil_variables, "thicndut", tfresheatingparam.thicndut) + + monkeypatch.setattr( + tfcoil_variables, "th_joint_contact", tfresheatingparam.th_joint_contact + ) + + monkeypatch.setattr(tfcoil_variables, "rho_tf_leg", tfresheatingparam.rho_tf_leg) + + monkeypatch.setattr(tfcoil_variables, "vol_cond_cp", tfresheatingparam.vol_cond_cp) + + monkeypatch.setattr(tfcoil_variables, "n_tf_turn", tfresheatingparam.n_tf_turn) + + monkeypatch.setattr(tfcoil_variables, "thkcas", tfresheatingparam.thkcas) + + monkeypatch.setattr(tfcoil_variables, "tftort", tfresheatingparam.tftort) + + monkeypatch.setattr(tfcoil_variables, "len_tf_coil", tfresheatingparam.len_tf_coil) + + monkeypatch.setattr(tfcoil_variables, "res_tf_leg", tfresheatingparam.res_tf_leg) + + monkeypatch.setattr( + tfcoil_variables, "temp_cp_average", tfresheatingparam.temp_cp_average + ) + + monkeypatch.setattr( + tfcoil_variables, "a_tf_leg_outboard", tfresheatingparam.a_tf_leg_outboard + ) + + monkeypatch.setattr(tfcoil_variables, "c_tf_total", tfresheatingparam.c_tf_total) + + monkeypatch.setattr( + tfcoil_variables, "rho_tf_joints", tfresheatingparam.rho_tf_joints + ) + + monkeypatch.setattr( + tfcoil_variables, "p_tf_leg_resistive", tfresheatingparam.p_tf_leg_resistive + ) + + monkeypatch.setattr( + tfcoil_variables, "p_cp_resistive", tfresheatingparam.p_cp_resistive + ) + + monkeypatch.setattr(tfcoil_variables, "pres_joints", tfresheatingparam.pres_joints) + + monkeypatch.setattr( + tfcoil_variables, "n_tf_joints_contact", tfresheatingparam.n_tf_joints_contact + ) + + monkeypatch.setattr(tfcoil_variables, "n_tf_joints", tfresheatingparam.n_tf_joints) + + monkeypatch.setattr(tfcoil_variables, "n_tf_coils", tfresheatingparam.n_tf_coils) + + monkeypatch.setattr(tfcoil_variables, "i_tf_sup", tfresheatingparam.i_tf_sup) + + monkeypatch.setattr(tfcoil_variables, "frholeg", tfresheatingparam.frholeg) + + monkeypatch.setattr(tfcoil_variables, "frhocp", tfresheatingparam.frhocp) + + monkeypatch.setattr(tfcoil_variables, "fcoolcp", tfresheatingparam.fcoolcp) + + monkeypatch.setattr(tfcoil_variables, "casthi", tfresheatingparam.casthi) + + monkeypatch.setattr(tfcoil_variables, "a_cp_cool", tfresheatingparam.a_cp_cool) + + monkeypatch.setattr( + tfcoil_variables, "f_a_tf_cool_outboard", tfresheatingparam.f_a_tf_cool_outboard + ) + + monkeypatch.setattr(tfcoil_variables, "i_cp_joints", tfresheatingparam.i_cp_joints) + + monkeypatch.setattr(tfcoil_variables, "tinstf", tfresheatingparam.tinstf) + + monkeypatch.setattr( + build_variables, "dr_tf_outboard", tfresheatingparam.dr_tf_outboard + ) + + monkeypatch.setattr( + build_variables, "dr_tf_inboard", tfresheatingparam.dr_tf_inboard + ) + + monkeypatch.setattr(build_variables, "r_cp_top", tfresheatingparam.r_cp_top) + + monkeypatch.setattr(build_variables, "hmax", tfresheatingparam.hmax) + + monkeypatch.setattr( + build_variables, "r_tf_inboard_in", tfresheatingparam.r_tf_inboard_in + ) + + monkeypatch.setattr( + build_variables, "r_tf_inboard_out", tfresheatingparam.r_tf_inboard_out + ) + + monkeypatch.setattr(physics_variables, "itart", tfresheatingparam.itart) + + monkeypatch.setattr(sctfcoil_module, "h_cp_top", tfresheatingparam.h_cp_top) + + monkeypatch.setattr( + sctfcoil_module, "is_leg_cp_temp_same", tfresheatingparam.is_leg_cp_temp_same + ) + + resistive_tf_coil.tf_res_heating() + + assert tfcoil_variables.rho_cp == pytest.approx(tfresheatingparam.expected_rho_cp) + + assert tfcoil_variables.rho_tf_leg == pytest.approx( + tfresheatingparam.expected_rho_tf_leg + ) + + assert tfcoil_variables.vol_cond_cp == pytest.approx( + tfresheatingparam.expected_vol_cond_cp + ) + + assert tfcoil_variables.res_tf_leg == pytest.approx( + tfresheatingparam.expected_res_tf_leg + ) + + assert tfcoil_variables.p_tf_leg_resistive == pytest.approx( + tfresheatingparam.expected_p_tf_leg_resistive + ) + + assert tfcoil_variables.p_cp_resistive == pytest.approx( + tfresheatingparam.expected_p_cp_resistive + ) + + assert tfcoil_variables.pres_joints == pytest.approx( + tfresheatingparam.expected_pres_joints + ) + + assert tfcoil_variables.a_cp_cool == pytest.approx( + tfresheatingparam.expected_a_cp_cool + ) + + assert sctfcoil_module.is_leg_cp_temp_same == pytest.approx( + tfresheatingparam.expected_is_leg_cp_temp_same + ) + + +class CpostParam(NamedTuple): + n_tf_coils: Any = None + + hmax: Any = None + + r_tf_inboard_in: Any = None + + r_tf_inboard_out: Any = None + + r_cp_top: Any = None + + ztop: Any = None + + hmaxi: Any = None + + cas_in_th: Any = None + + cas_out_th: Any = None + + gr_ins_th: Any = None + + ins_th: Any = None + + n_tf_turn: Any = None + + curr: Any = None + + rho: Any = None + + fcool: Any = None + + expected_vol_ins_cp: Any = None + + expected_vol_gr_ins_cp: Any = None + + expected_vol_case_cp: Any = None + + expected_respow: Any = None + + expected_vol_cond_cp: Any = None + + expected_a_cp_cool: Any = None + + +@pytest.mark.parametrize( + "cpostparam", + ( + CpostParam( + n_tf_coils=12, + hmax=4.4214285714285717, + r_tf_inboard_in=0, + r_tf_inboard_out=0.15483000000000002, + r_cp_top=0.87643571428571443, + ztop=2.6714285714285717, + hmaxi=4.5762585714285713, + cas_in_th=0, + cas_out_th=0.0077415000000000019, + gr_ins_th=0, + ins_th=0.00080000000000000004, + n_tf_turn=1, + curr=25500000, + rho=2.1831760869565221e-08, + fcool=0.12725, + expected_vol_ins_cp=0.12917075053120922, + expected_vol_gr_ins_cp=0, + expected_vol_case_cp=0.12791418544773489, + expected_respow=470083798.99090022, + expected_vol_cond_cp=12.020160732580297, + expected_a_cp_cool=0.00068328705812121333, + ), + CpostParam( + n_tf_coils=12, + hmax=4.4214285714285717, + r_tf_inboard_in=0, + r_tf_inboard_out=0.15483000000000002, + r_cp_top=0.85843571428571441, + ztop=2.6714285714285717, + hmaxi=4.5762585714285713, + cas_in_th=0, + cas_out_th=0.0077415000000000019, + gr_ins_th=0, + ins_th=0.00080000000000000004, + n_tf_turn=1, + curr=25500000, + rho=2.1831760869565221e-08, + fcool=0.12725, + expected_vol_ins_cp=0.12679799009998483, + expected_vol_gr_ins_cp=0, + expected_vol_case_cp=0.12648575512245444, + expected_respow=475710489.56122422, + expected_vol_cond_cp=11.545770024935592, + expected_a_cp_cool=0.00068328705812121333, + ), + ), +) +def test_cpost(cpostparam, monkeypatch, resistive_tf_coil): + """ + Automatically generated Regression Unit Test for cpost. + + This test was generated using data from tests/regression/scenarios/FNSF/IN.DAT. + + :param cpostparam: the data used to mock and assert in this test. + :type cpostparam: cpostparam + + :param monkeypatch: pytest fixture used to mock module/class variables + :type monkeypatch: _pytest.monkeypatch.monkeypatch + """ + + monkeypatch.setattr(build_variables, "hmax", cpostparam.hmax) + + ( + a_cp_cool, + vol_cond_cp, + respow, + vol_ins_cp, + vol_case_cp, + vol_gr_ins_cp, + ) = resistive_tf_coil.cpost( + r_tf_inboard_in=cpostparam.r_tf_inboard_in, + r_tf_inboard_out=cpostparam.r_tf_inboard_out, + r_cp_top=cpostparam.r_cp_top, + ztop=cpostparam.ztop, + hmaxi=cpostparam.hmaxi, + cas_in_th=cpostparam.cas_in_th, + cas_out_th=cpostparam.cas_out_th, + gr_ins_th=cpostparam.gr_ins_th, + ins_th=cpostparam.ins_th, + n_tf_turn=cpostparam.n_tf_turn, + curr=cpostparam.curr, + rho=cpostparam.rho, + fcool=cpostparam.fcool, + n_tf_coils=cpostparam.n_tf_coils, + ) + + assert vol_ins_cp == pytest.approx(cpostparam.expected_vol_ins_cp) + + assert vol_gr_ins_cp == pytest.approx(cpostparam.expected_vol_gr_ins_cp) + + assert vol_case_cp == pytest.approx(cpostparam.expected_vol_case_cp) + + assert respow == pytest.approx(cpostparam.expected_respow) + + assert vol_cond_cp == pytest.approx(cpostparam.expected_vol_cond_cp) + + assert a_cp_cool == pytest.approx(cpostparam.expected_a_cp_cool) diff --git a/tests/unit/test_sctfcoil.py b/tests/unit/test_sctfcoil.py index 92c1ebd80b..5db5ffe0ae 100644 --- a/tests/unit/test_sctfcoil.py +++ b/tests/unit/test_sctfcoil.py @@ -1,29 +1,26 @@ from typing import Any, NamedTuple -import numpy as np import pytest -from process import sctfcoil as sctf +from process import superconducting_tf_coil as sctf from process.fortran import ( build_variables, divertor_variables, - fwbs_variables, global_variables, - physics_variables, sctfcoil_module, tfcoil_variables, ) -from process.sctfcoil import Sctfcoil +from process.superconducting_tf_coil import SuperconductingTFCoil @pytest.fixture def sctfcoil(): - """Provides Sctfcoil object for testing. + """Provides SuperconductingTFCoil object for testing. - :returns: initialised Sctfcoil object - :rtype: process.sctfcoil.Sctfcoil + :returns: initialised SuperconductingTFCoil object + :rtype: process.sctfcoil.SuperconductingTFCoil """ - return Sctfcoil() + return SuperconductingTFCoil() class ProtectParam(NamedTuple): @@ -77,8 +74,8 @@ def test_protect(protectparam, sctfcoil): :param protectparam: the data used to mock and assert in this test. :type protectparam: protectparam - :param sctfcoil: initialised Sctfcoil object - :type sctfcoil: process.sctfcoil.Sctfcoil + :param sctfcoil: initialised SuperconductingTFCoil object + :type sctfcoil: process.sctfcoil.SuperconductingTFCoil """ ajwpro, vd = sctfcoil.protect( @@ -313,8 +310,8 @@ def test_supercon(superconparam, monkeypatch, sctfcoil): :param monkeypatch: pytest fixture used to mock module/class variables :type monkeypatch: _pytest.monkeypatch.monkeypatch - :param sctfcoil: initialised Sctfcoil object - :type sctfcoil: process.sctfcoil.Sctfcoil + :param sctfcoil: initialised SuperconductingTFCoil object + :type sctfcoil: process.sctfcoil.SuperconductingTFCoil """ monkeypatch.setattr(tfcoil_variables, "tmargmin_tf", superconparam.tmargmin_tf) @@ -385,13664 +382,1101 @@ def test_supercon(superconparam, monkeypatch, sctfcoil): assert tmarg == pytest.approx(superconparam.expected_tmarg) -class TfCurrentParam(NamedTuple): - casthi: Any = None - - c_tf_total: Any = None - - rbmax: Any = None - - i_tf_sup: Any = None - - casths_fraction: Any = None - - tinstf: Any = None - - tftort: Any = None - - bmaxtf: Any = None - - tfinsgap: Any = None - - tfc_sidewall_is_fraction: Any = None - - casths: Any = None +class PeakTfWithRippleParam(NamedTuple): + tf_fit_t: Any = None - casthi_is_fraction: Any = None + tf_fit_z: Any = None - casthi_fraction: Any = None + tf_fit_y: Any = None n_tf_coils: Any = None - thicndut: Any = None - - thkcas: Any = None - - oacdcp: Any = None - - tfareain: Any = None - - r_tf_inboard_out: Any = None - - r_tf_inboard_in: Any = None - - dr_tf_inboard: Any = None - - bt: Any = None + wwp1: Any = None - rmajor: Any = None + dr_tf_wp: Any = None - tfc_current: Any = None + tfin: Any = None - theta_coil: Any = None + bmaxtf: Any = None - expected_c_tf_total: Any = None + expected_tf_fit_t: Any = None - expected_rbmax: Any = None + expected_tf_fit_z: Any = None - expected_bmaxtf: Any = None + expected_tf_fit_y: Any = None - expected_oacdcp: Any = None + expected_bmaxtfrp: Any = None - expected_tfc_current: Any = None + expected_flag: Any = None @pytest.mark.parametrize( - "tfcurrentparam", + "peaktfwithrippleparam", ( - TfCurrentParam( - casthi=0.060000000000000012, - c_tf_total=0, - rbmax=0, - i_tf_sup=1, - casths_fraction=0.059999999999999998, - tinstf=0.0080000000000000019, - tftort=1.6395161177915356, - bmaxtf=0, - tfinsgap=0.01, - tfc_sidewall_is_fraction=False, - casths=0.05000000000000001, - casthi_is_fraction=False, - casthi_fraction=0.050000000000000003, + PeakTfWithRippleParam( + tf_fit_t=0, + tf_fit_z=0, + tf_fit_y=0, n_tf_coils=16, - thicndut=0.002, - thkcas=0.52465000000000006, - oacdcp=8673900, - tfareain=27.308689677971632, - r_tf_inboard_out=4.20194118510911, - r_tf_inboard_in=2.9939411851091102, - dr_tf_inboard=1.208, - bt=5.3292000000000002, - rmajor=8.8901000000000003, - tfc_current=0, - theta_coil=0.19634954084936207, - expected_c_tf_total=236885604.60000002, - expected_rbmax=4.0432020634751211, - expected_bmaxtf=11.717722779177526, - expected_oacdcp=8674367.2945641987, - expected_tfc_current=14805350.287500001, + wwp1=1.299782604942499, + dr_tf_wp=0.50661087836601015, + tfin=3.789896624292115, + bmaxtf=11.717722779177526, + expected_tf_fit_t=0.80807838916035957, + expected_tf_fit_z=0.3149613642807837, + expected_tf_fit_y=1.0658869305062604, + expected_bmaxtfrp=12.48976756562082, + expected_flag=0, + ), + PeakTfWithRippleParam( + tf_fit_t=0.80807838916035957, + tf_fit_z=0.3149613642807837, + tf_fit_y=1.0658869305062604, + n_tf_coils=16, + wwp1=1.299782604942499, + dr_tf_wp=0.50661087836601015, + tfin=3.789896624292115, + bmaxtf=11.717722779177526, + expected_tf_fit_t=0.80807838916035957, + expected_tf_fit_z=0.3149613642807837, + expected_tf_fit_y=1.0658869305062604, + expected_bmaxtfrp=12.48976756562082, + expected_flag=0, ), ), ) -def test_tf_current(tfcurrentparam, monkeypatch, sctfcoil): +def test_peak_tf_with_ripple(peaktfwithrippleparam, monkeypatch, sctfcoil): """ - Automatically generated Regression Unit Test for tf_current. + Automatically generated Regression Unit Test for peak_tf_with_ripple. This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. - :param tfcurrentparam: the data used to mock and assert in this test. - :type tfcurrentparam: tfcurrentparam + :param peaktfwithrippleparam: the data used to mock and assert in this test. + :type peaktfwithrippleparam: peaktfwithrippleparam :param monkeypatch: pytest fixture used to mock module/class variables :type monkeypatch: _pytest.monkeypatch.monkeypatch - - :param sctfcoil: initialised Sctfcoil object - :type sctfcoil: process.sctfcoil.Sctfcoil """ - monkeypatch.setattr(tfcoil_variables, "casthi", tfcurrentparam.casthi) - - monkeypatch.setattr(tfcoil_variables, "c_tf_total", tfcurrentparam.c_tf_total) - - monkeypatch.setattr(tfcoil_variables, "rbmax", tfcurrentparam.rbmax) - - monkeypatch.setattr(tfcoil_variables, "i_tf_sup", tfcurrentparam.i_tf_sup) - - monkeypatch.setattr( - tfcoil_variables, "casths_fraction", tfcurrentparam.casths_fraction - ) - - monkeypatch.setattr(tfcoil_variables, "tinstf", tfcurrentparam.tinstf) - - monkeypatch.setattr(tfcoil_variables, "tftort", tfcurrentparam.tftort) - - monkeypatch.setattr(tfcoil_variables, "bmaxtf", tfcurrentparam.bmaxtf) - - monkeypatch.setattr(tfcoil_variables, "tfinsgap", tfcurrentparam.tfinsgap) - - monkeypatch.setattr( - tfcoil_variables, - "tfc_sidewall_is_fraction", - tfcurrentparam.tfc_sidewall_is_fraction, - ) + monkeypatch.setattr(sctfcoil_module, "tf_fit_t", peaktfwithrippleparam.tf_fit_t) - monkeypatch.setattr(tfcoil_variables, "casths", tfcurrentparam.casths) + monkeypatch.setattr(sctfcoil_module, "tf_fit_z", peaktfwithrippleparam.tf_fit_z) - monkeypatch.setattr( - tfcoil_variables, "casthi_is_fraction", tfcurrentparam.casthi_is_fraction - ) + monkeypatch.setattr(sctfcoil_module, "tf_fit_y", peaktfwithrippleparam.tf_fit_y) - monkeypatch.setattr( - tfcoil_variables, "casthi_fraction", tfcurrentparam.casthi_fraction + bmaxtfrp, flag = sctfcoil.peak_tf_with_ripple( + n_tf_coils=peaktfwithrippleparam.n_tf_coils, + wwp1=peaktfwithrippleparam.wwp1, + dr_tf_wp=peaktfwithrippleparam.dr_tf_wp, + tfin=peaktfwithrippleparam.tfin, + bmaxtf=peaktfwithrippleparam.bmaxtf, ) - monkeypatch.setattr(tfcoil_variables, "n_tf_coils", tfcurrentparam.n_tf_coils) - - monkeypatch.setattr(tfcoil_variables, "thicndut", tfcurrentparam.thicndut) - - monkeypatch.setattr(tfcoil_variables, "thkcas", tfcurrentparam.thkcas) - - monkeypatch.setattr(tfcoil_variables, "oacdcp", tfcurrentparam.oacdcp) - - monkeypatch.setattr(tfcoil_variables, "tfareain", tfcurrentparam.tfareain) - - monkeypatch.setattr( - build_variables, "r_tf_inboard_out", tfcurrentparam.r_tf_inboard_out + assert sctfcoil_module.tf_fit_t == pytest.approx( + peaktfwithrippleparam.expected_tf_fit_t ) - monkeypatch.setattr( - build_variables, "r_tf_inboard_in", tfcurrentparam.r_tf_inboard_in + assert sctfcoil_module.tf_fit_z == pytest.approx( + peaktfwithrippleparam.expected_tf_fit_z ) - monkeypatch.setattr(build_variables, "dr_tf_inboard", tfcurrentparam.dr_tf_inboard) - - monkeypatch.setattr(physics_variables, "bt", tfcurrentparam.bt) - - monkeypatch.setattr(physics_variables, "rmajor", tfcurrentparam.rmajor) - - monkeypatch.setattr(sctfcoil_module, "tfc_current", tfcurrentparam.tfc_current) - - monkeypatch.setattr(sctfcoil_module, "theta_coil", tfcurrentparam.theta_coil) - - sctfcoil.tf_current() - - assert tfcoil_variables.c_tf_total == pytest.approx( - tfcurrentparam.expected_c_tf_total + assert sctfcoil_module.tf_fit_y == pytest.approx( + peaktfwithrippleparam.expected_tf_fit_y ) - assert tfcoil_variables.rbmax == pytest.approx(tfcurrentparam.expected_rbmax) - - assert tfcoil_variables.bmaxtf == pytest.approx(tfcurrentparam.expected_bmaxtf) - - assert tfcoil_variables.oacdcp == pytest.approx(tfcurrentparam.expected_oacdcp) - - assert sctfcoil_module.tfc_current == pytest.approx( - tfcurrentparam.expected_tfc_current - ) + assert bmaxtfrp == pytest.approx(peaktfwithrippleparam.expected_bmaxtfrp) + assert flag == pytest.approx(peaktfwithrippleparam.expected_flag) -class TfGlobalGeometryParam(NamedTuple): - r_tf_outboard_mid: Any = None - r_cp_top: Any = None +class TfWpGeomParam(NamedTuple): + dr_tf_inboard: Any = None r_tf_inboard_in: Any = None r_tf_inboard_out: Any = None - dr_tf_outboard: Any = None + dr_tf_wp: Any = None - tfareain: Any = None + casthi: Any = None - c_tf_total: Any = None + thkcas: Any = None - tftort: Any = None + casths: Any = None - n_tf_coils: Any = None + wwp1: Any = None - a_tf_leg_outboard: Any = None + wwp2: Any = None - i_tf_sup: Any = None + tinstf: Any = None - dztop: Any = None + tfinsgap: Any = None - i_tf_case_geom: Any = None + awpc: Any = None - itart: Any = None + awptf: Any = None + + r_wp_inner: Any = None - kappa: Any = None + r_wp_outer: Any = None - rminor: Any = None + r_wp_centre: Any = None - h_cp_top: Any = None + t_wp_toroidal: Any = None - r_tf_outboard_in: Any = None + t_wp_toroidal_av: Any = None - r_tf_outboard_out: Any = None + a_ground_ins: Any = None theta_coil: Any = None tan_theta_coil: Any = None - expected_tfareain: Any = None + i_tf_wp_geom: Any = None + + expected_wwp1: Any = None + + expected_awpc: Any = None + + expected_awptf: Any = None - expected_tftort: Any = None + expected_r_wp_inner: Any = None - expected_a_tf_leg_outboard: Any = None + expected_r_wp_outer: Any = None - expected_r_tf_outboard_in: Any = None + expected_r_wp_centre: Any = None - expected_r_tf_outboard_out: Any = None + expected_t_wp_toroidal: Any = None - expected_theta_coil: Any = None + expected_t_wp_toroidal_av: Any = None - expected_tan_theta_coil: Any = None + expected_a_ground_ins: Any = None @pytest.mark.parametrize( - "tfglobalgeometryparam", + "tfwpgeomparam", ( - TfGlobalGeometryParam( - r_tf_outboard_mid=16.519405859443332, - r_cp_top=4.20194118510911, + TfWpGeomParam( + dr_tf_inboard=1.208, r_tf_inboard_in=2.9939411851091102, r_tf_inboard_out=4.20194118510911, - dr_tf_outboard=1.208, - tfareain=0, - c_tf_total=0, - tftort=1, - n_tf_coils=16, - a_tf_leg_outboard=0, - i_tf_sup=1, - dztop=0, - i_tf_case_geom=0, - itart=0, - kappa=1.8480000000000001, - rminor=2.8677741935483869, - h_cp_top=0, - r_tf_outboard_in=0, - r_tf_outboard_out=0, - theta_coil=0, - tan_theta_coil=0, - expected_tfareain=27.308689677971632, - expected_tftort=1.6395161177915356, - expected_a_tf_leg_outboard=1.9805354702921749, - expected_r_tf_outboard_in=15.915405859443332, - expected_r_tf_outboard_out=17.123405859443331, - expected_theta_coil=0.19634954084936207, - expected_tan_theta_coil=0.19891236737965801, + dr_tf_wp=0.54261087836601019, + casthi=0.060000000000000012, + thkcas=0.52465000000000006, + casths=0.05000000000000001, + wwp1=0, + wwp2=0, + tinstf=0.0080000000000000019, + tfinsgap=0.01, + awpc=0, + awptf=0, + r_wp_inner=0, + r_wp_outer=0, + r_wp_centre=0, + t_wp_toroidal=0, + t_wp_toroidal_av=0, + a_ground_ins=0, + theta_coil=0.19634954084936207, + tan_theta_coil=0.19891236737965801, + i_tf_wp_geom=0, + expected_wwp1=1.299782604942499, + expected_awpc=0.70527618095271016, + expected_awptf=0.64024601555360383, + expected_r_wp_inner=3.5185911851091101, + expected_r_wp_outer=4.06120206347512, + expected_r_wp_centre=3.789896624292115, + expected_t_wp_toroidal=1.299782604942499, + expected_t_wp_toroidal_av=1.299782604942499, + expected_a_ground_ins=0.028582295732936136, ), - TfGlobalGeometryParam( - r_tf_outboard_mid=17.063351291812893, - r_cp_top=4.4822055399518357, - r_tf_inboard_in=2.9538679176819831, - r_tf_inboard_out=4.4822055399518357, - dr_tf_outboard=1.5283376222698528, - tfareain=35.703669036223495, - c_tf_total=241812532.66279837, - tftort=1.7488698442633552, - n_tf_coils=16, - a_tf_leg_outboard=2.6728635794409041, - i_tf_sup=1, - dztop=0, - i_tf_case_geom=0, - itart=0, - kappa=1.8480000000000001, - rminor=2.9620024998595755, - h_cp_top=0, - r_tf_outboard_in=16.299182480677967, - r_tf_outboard_out=17.827520102947819, + TfWpGeomParam( + dr_tf_inboard=1.208, + r_tf_inboard_in=2.9939411851091102, + r_tf_inboard_out=4.20194118510911, + dr_tf_wp=0.54261087836601019, + casthi=0.060000000000000012, + thkcas=0.52465000000000006, + casths=0.05000000000000001, + wwp1=1.299782604942499, + wwp2=0, + tinstf=0.0080000000000000019, + tfinsgap=0.01, + awpc=0.70527618095271016, + awptf=0.64024601555360383, + r_wp_inner=3.5185911851091101, + r_wp_outer=4.06120206347512, + r_wp_centre=3.789896624292115, + t_wp_toroidal=1.299782604942499, + t_wp_toroidal_av=1.299782604942499, + a_ground_ins=0.028582295732936136, theta_coil=0.19634954084936207, tan_theta_coil=0.19891236737965801, - expected_tfareain=35.703669036223495, - expected_tftort=1.7488698442633552, - expected_a_tf_leg_outboard=2.6728635794409041, - expected_r_tf_outboard_in=16.299182480677967, - expected_r_tf_outboard_out=17.827520102947819, - expected_theta_coil=0.19634954084936207, - expected_tan_theta_coil=0.19891236737965801, + i_tf_wp_geom=0, + expected_wwp1=1.299782604942499, + expected_awpc=0.70527618095271016, + expected_awptf=0.64024601555360383, + expected_r_wp_inner=3.5185911851091101, + expected_r_wp_outer=4.06120206347512, + expected_r_wp_centre=3.789896624292115, + expected_t_wp_toroidal=1.299782604942499, + expected_t_wp_toroidal_av=1.299782604942499, + expected_a_ground_ins=0.028582295732936136, ), ), ) -def test_tf_global_geometry(tfglobalgeometryparam, monkeypatch, sctfcoil): +def test_tf_wp_geom(tfwpgeomparam, monkeypatch, sctfcoil): """ - Automatically generated Regression Unit Test for tf_global_geometry. + Automatically generated Regression Unit Test for tf_wp_geom. This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. - :param tfglobalgeometryparam: the data used to mock and assert in this test. - :type tfglobalgeometryparam: tfglobalgeometryparam + :param tfwpgeomparam: the data used to mock and assert in this test. + :type tfwpgeomparam: tfwpgeomparam :param monkeypatch: pytest fixture used to mock module/class variables :type monkeypatch: _pytest.monkeypatch.monkeypatch - - :param sctfcoil: initialised Sctfcoil object - :type sctfcoil: process.sctfcoil.Sctfcoil """ - monkeypatch.setattr( - build_variables, "r_tf_outboard_mid", tfglobalgeometryparam.r_tf_outboard_mid - ) - - monkeypatch.setattr(build_variables, "r_cp_top", tfglobalgeometryparam.r_cp_top) + monkeypatch.setattr(build_variables, "dr_tf_inboard", tfwpgeomparam.dr_tf_inboard) monkeypatch.setattr( - build_variables, "r_tf_inboard_in", tfglobalgeometryparam.r_tf_inboard_in + build_variables, "r_tf_inboard_in", tfwpgeomparam.r_tf_inboard_in ) monkeypatch.setattr( - build_variables, "r_tf_inboard_out", tfglobalgeometryparam.r_tf_inboard_out + build_variables, "r_tf_inboard_out", tfwpgeomparam.r_tf_inboard_out ) - monkeypatch.setattr( - build_variables, "dr_tf_outboard", tfglobalgeometryparam.dr_tf_outboard - ) + monkeypatch.setattr(tfcoil_variables, "dr_tf_wp", tfwpgeomparam.dr_tf_wp) - monkeypatch.setattr(tfcoil_variables, "tfareain", tfglobalgeometryparam.tfareain) + monkeypatch.setattr(tfcoil_variables, "casthi", tfwpgeomparam.casthi) - monkeypatch.setattr( - tfcoil_variables, "c_tf_total", tfglobalgeometryparam.c_tf_total - ) + monkeypatch.setattr(tfcoil_variables, "thkcas", tfwpgeomparam.thkcas) - monkeypatch.setattr(tfcoil_variables, "tftort", tfglobalgeometryparam.tftort) + monkeypatch.setattr(tfcoil_variables, "casths", tfwpgeomparam.casths) - monkeypatch.setattr( - tfcoil_variables, "n_tf_coils", tfglobalgeometryparam.n_tf_coils - ) + monkeypatch.setattr(tfcoil_variables, "wwp1", tfwpgeomparam.wwp1) - monkeypatch.setattr( - tfcoil_variables, "a_tf_leg_outboard", tfglobalgeometryparam.a_tf_leg_outboard - ) + monkeypatch.setattr(tfcoil_variables, "wwp2", tfwpgeomparam.wwp2) - monkeypatch.setattr(tfcoil_variables, "i_tf_sup", tfglobalgeometryparam.i_tf_sup) + monkeypatch.setattr(tfcoil_variables, "tinstf", tfwpgeomparam.tinstf) - monkeypatch.setattr(tfcoil_variables, "dztop", tfglobalgeometryparam.dztop) + monkeypatch.setattr(tfcoil_variables, "tfinsgap", tfwpgeomparam.tfinsgap) - monkeypatch.setattr( - tfcoil_variables, "i_tf_case_geom", tfglobalgeometryparam.i_tf_case_geom - ) + monkeypatch.setattr(sctfcoil_module, "awpc", tfwpgeomparam.awpc) - monkeypatch.setattr(physics_variables, "itart", tfglobalgeometryparam.itart) + monkeypatch.setattr(sctfcoil_module, "awptf", tfwpgeomparam.awptf) - monkeypatch.setattr(physics_variables, "kappa", tfglobalgeometryparam.kappa) + monkeypatch.setattr(sctfcoil_module, "r_wp_inner", tfwpgeomparam.r_wp_inner) - monkeypatch.setattr(physics_variables, "rminor", tfglobalgeometryparam.rminor) + monkeypatch.setattr(sctfcoil_module, "r_wp_outer", tfwpgeomparam.r_wp_outer) - monkeypatch.setattr(sctfcoil_module, "h_cp_top", tfglobalgeometryparam.h_cp_top) + monkeypatch.setattr(sctfcoil_module, "r_wp_centre", tfwpgeomparam.r_wp_centre) - monkeypatch.setattr( - sctfcoil_module, "r_tf_outboard_in", tfglobalgeometryparam.r_tf_outboard_in - ) + monkeypatch.setattr(sctfcoil_module, "t_wp_toroidal", tfwpgeomparam.t_wp_toroidal) monkeypatch.setattr( - sctfcoil_module, "r_tf_outboard_out", tfglobalgeometryparam.r_tf_outboard_out + sctfcoil_module, "t_wp_toroidal_av", tfwpgeomparam.t_wp_toroidal_av ) - monkeypatch.setattr(sctfcoil_module, "theta_coil", tfglobalgeometryparam.theta_coil) + monkeypatch.setattr(sctfcoil_module, "a_ground_ins", tfwpgeomparam.a_ground_ins) - monkeypatch.setattr( - sctfcoil_module, "tan_theta_coil", tfglobalgeometryparam.tan_theta_coil - ) + monkeypatch.setattr(sctfcoil_module, "theta_coil", tfwpgeomparam.theta_coil) - sctfcoil.tf_global_geometry() + monkeypatch.setattr(sctfcoil_module, "tan_theta_coil", tfwpgeomparam.tan_theta_coil) - assert tfcoil_variables.tfareain == pytest.approx( - tfglobalgeometryparam.expected_tfareain - ) + sctfcoil.tf_wp_geom(i_tf_wp_geom=tfwpgeomparam.i_tf_wp_geom) - assert tfcoil_variables.tftort == pytest.approx( - tfglobalgeometryparam.expected_tftort - ) + assert tfcoil_variables.wwp1 == pytest.approx(tfwpgeomparam.expected_wwp1) - assert tfcoil_variables.a_tf_leg_outboard == pytest.approx( - tfglobalgeometryparam.expected_a_tf_leg_outboard - ) + assert sctfcoil_module.awpc == pytest.approx(tfwpgeomparam.expected_awpc) - assert sctfcoil_module.r_tf_outboard_in == pytest.approx( - tfglobalgeometryparam.expected_r_tf_outboard_in - ) + assert sctfcoil_module.awptf == pytest.approx(tfwpgeomparam.expected_awptf) - assert sctfcoil_module.r_tf_outboard_out == pytest.approx( - tfglobalgeometryparam.expected_r_tf_outboard_out + assert sctfcoil_module.r_wp_inner == pytest.approx( + tfwpgeomparam.expected_r_wp_inner ) - assert sctfcoil_module.theta_coil == pytest.approx( - tfglobalgeometryparam.expected_theta_coil + assert sctfcoil_module.r_wp_outer == pytest.approx( + tfwpgeomparam.expected_r_wp_outer ) - assert sctfcoil_module.tan_theta_coil == pytest.approx( - tfglobalgeometryparam.expected_tan_theta_coil + assert sctfcoil_module.r_wp_centre == pytest.approx( + tfwpgeomparam.expected_r_wp_centre ) + assert sctfcoil_module.t_wp_toroidal == pytest.approx( + tfwpgeomparam.expected_t_wp_toroidal + ) -@pytest.mark.parametrize( - "a, b, expected_circumference", - ( - (2.667950e9, 6.782819e8, 11464316399.111176), - (4.7186039761812131, 3.6192586838709673, 26.308134540723429), - ), - ids=["johndcook", "baseline_2018"], -) -def test_circumference(a, b, expected_circumference, sctfcoil): - """Unit test for the sctfcoil circumference routine. - - This unit test uses values from an external blog referenced in the - routine header (https://www.johndcook.com/blog/2013/05/05/ramanujan-circumference-ellipse/) - as well as results obtained from baseline 2018. - - :param a: the value of a (x/a)^2... in the formula of an ellipse - :type a: float - - :param b: the value of b ...(y/b)^2... in the formula of an ellipse - :type b: float - - :param expected_circumference: the expected result of the routine given inputs a and b - :type expected_circumference: float - - :param sctfcoil: initialised Sctfcoil object - :type sctfcoil: process.sctfcoil.Sctfcoil - """ - assert sctfcoil.circumference(a, b) == pytest.approx(expected_circumference) - + assert sctfcoil_module.t_wp_toroidal_av == pytest.approx( + tfwpgeomparam.expected_t_wp_toroidal_av + ) -class ResTfInternalGeomParam(NamedTuple): - n_tf_turn: Any = None + assert sctfcoil_module.a_ground_ins == pytest.approx( + tfwpgeomparam.expected_a_ground_ins + ) - thicndut: Any = None - thkcas: Any = None +class TfCaseGeomParam(NamedTuple): + acasetf: Any = None - dr_tf_wp: Any = None + acasetfo: Any = None - tftort: Any = None + a_tf_leg_outboard: Any = None tfareain: Any = None - c_tf_total: Any = None - - fcoolcp: Any = None - - cpttf: Any = None + n_tf_coils: Any = None - cdtfleg: Any = None + casths: Any = None casthi: Any = None - aiwp: Any = None + dr_tf_wp: Any = None - acasetf: Any = None + r_tf_inboard_in: Any = None - tinstf: Any = None + r_tf_inboard_out: Any = None - n_tf_coils: Any = None + awpc: Any = None - dr_tf_outboard: Any = None + r_wp_inner: Any = None - r_tf_inboard_in: Any = None + r_wp_outer: Any = None - r_tf_inboard_out: Any = None + t_lat_case_av: Any = None - r_cp_top: Any = None + a_case_front: Any = None - itart: Any = None + a_case_nose: Any = None - expected_n_tf_turn: Any = None + theta_coil: Any = None - expected_cpttf: Any = None + tan_theta_coil: Any = None - expected_cdtfleg: Any = None + i_tf_wp_geom: Any = None - expected_aiwp: Any = None + i_tf_case_geom: Any = None expected_acasetf: Any = None + expected_acasetfo: Any = None -class TfResHeatingParam(NamedTuple): - rho_cp: Any = None - temp_tf_legs_outboard: Any = None - thicndut: Any = None - th_joint_contact: Any = None - rho_tf_leg: Any = None - vol_cond_cp: Any = None - n_tf_turn: Any = None - thkcas: Any = None - tftort: Any = None - len_tf_coil: Any = None - res_tf_leg: Any = None - temp_cp_average: Any = None - a_tf_leg_outboard: Any = None - c_tf_total: Any = None - rho_tf_joints: Any = None - p_tf_leg_resistive: Any = None - p_cp_resistive: Any = None - pres_joints: Any = None - n_tf_joints_contact: Any = None - n_tf_joints: Any = None - n_tf_coils: Any = None - i_tf_sup: Any = None - frholeg: Any = None - frhocp: Any = None - fcoolcp: Any = None - casthi: Any = None - a_cp_cool: Any = None - f_a_tf_cool_outboard: Any = None - i_cp_joints: Any = None - tinstf: Any = None - dr_tf_outboard: Any = None - dr_tf_inboard: Any = None - r_cp_top: Any = None - hmax: Any = None - r_tf_inboard_in: Any = None - r_tf_inboard_out: Any = None - itart: Any = None - h_cp_top: Any = None - is_leg_cp_temp_same: Any = None - expected_rho_cp: Any = None - expected_rho_tf_leg: Any = None - expected_vol_cond_cp: Any = None - expected_res_tf_leg: Any = None - expected_p_tf_leg_resistive: Any = None - expected_p_cp_resistive: Any = None - expected_pres_joints: Any = None - expected_a_cp_cool: Any = None - expected_is_leg_cp_temp_same: Any = None + expected_t_lat_case_av: Any = None + + expected_a_case_front: Any = None + + expected_a_case_nose: Any = None @pytest.mark.parametrize( - "restfinternalgeomparam", + "tfcasegeomparam", ( - ResTfInternalGeomParam( - n_tf_turn=0, - thicndut=0.00080000000000000004, - thkcas=0, - dr_tf_wp=0.15483000000000002, - tftort=0.45367650933034859, - tfareain=0.0753112923616783, - c_tf_total=25500000, - fcoolcp=0.12725, - cpttf=70000, - cdtfleg=0, - casthi=0.0077415000000000019, - aiwp=0, + TfCaseGeomParam( acasetf=0, - tinstf=0, - n_tf_coils=12, - dr_tf_outboard=0.15483000000000002, - r_tf_inboard_in=0, - r_tf_inboard_out=0.15483000000000002, - r_cp_top=0.87643571428571443, - itart=1, - expected_n_tf_turn=1, - expected_cpttf=2125000, - expected_cdtfleg=421788350.27812088, - expected_aiwp=0.00030678028680367151, - expected_acasetf=0.00061190425043863676, + acasetfo=0, + a_tf_leg_outboard=1.9805354702921749, + tfareain=27.308689677971632, + n_tf_coils=16, + casths=0.05000000000000001, + casthi=0.060000000000000012, + dr_tf_wp=0.54261087836601019, + r_tf_inboard_in=2.9939411851091102, + r_tf_inboard_out=4.20194118510911, + awpc=0.70527618095271016, + r_wp_inner=3.5185911851091101, + r_wp_outer=4.06120206347512, + t_lat_case_av=0, + a_case_front=0, + a_case_nose=0, + theta_coil=0.19634954084936207, + tan_theta_coil=0.19891236737965801, + i_tf_wp_geom=0, + i_tf_case_geom=0, + expected_acasetf=1.0015169239205168, + expected_acasetfo=1.2752592893394648, + expected_t_lat_case_av=0.10396600719086938, + expected_a_case_front=0.18607458590131154, + expected_a_case_nose=0.70261616505511615, ), - ResTfInternalGeomParam( - n_tf_turn=1, - thicndut=0.00080000000000000004, - thkcas=0, - dr_tf_wp=0.14708850000000001, - tftort=0.44435902370665786, - tfareain=0.0753112923616783, - c_tf_total=25500000, - fcoolcp=0.12725, - cpttf=2125000, - cdtfleg=421788350.27812088, - casthi=0.0077415000000000019, - aiwp=0.00030678028680367151, - acasetf=0.00061190425043863676, - tinstf=0, - n_tf_coils=12, - dr_tf_outboard=0.15483000000000002, - r_tf_inboard_in=0, - r_tf_inboard_out=0.15483000000000002, - r_cp_top=0.85843571428571441, - itart=1, - expected_n_tf_turn=1, - expected_cpttf=2125000, - expected_cdtfleg=430664525.98439038, - expected_aiwp=0.00029439388680367086, - expected_acasetf=0.00061190425043863676, + TfCaseGeomParam( + acasetf=1.0015169239205168, + acasetfo=1.2752592893394648, + a_tf_leg_outboard=1.9805354702921749, + tfareain=27.308689677971632, + n_tf_coils=16, + casths=0.05000000000000001, + casthi=0.060000000000000012, + dr_tf_wp=0.54261087836601019, + r_tf_inboard_in=2.9939411851091102, + r_tf_inboard_out=4.20194118510911, + awpc=0.70527618095271016, + r_wp_inner=3.5185911851091101, + r_wp_outer=4.06120206347512, + t_lat_case_av=0.10396600719086938, + a_case_front=0.18607458590131154, + a_case_nose=0.70261616505511615, + theta_coil=0.19634954084936207, + tan_theta_coil=0.19891236737965801, + i_tf_wp_geom=0, + i_tf_case_geom=0, + expected_acasetf=1.0015169239205168, + expected_acasetfo=1.2752592893394648, + expected_t_lat_case_av=0.10396600719086938, + expected_a_case_front=0.18607458590131154, + expected_a_case_nose=0.70261616505511615, ), ), ) -def test_res_tf_internal_geom(restfinternalgeomparam, monkeypatch, sctfcoil): +def test_tf_case_geom(tfcasegeomparam, monkeypatch, sctfcoil): """ - Automatically generated Regression Unit Test for res_tf_internal_geom. + Automatically generated Regression Unit Test for tf_case_geom. - This test was generated using data from tests/regression/scenarios/FNSF/IN.DAT. + This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. - :param restfinternalgeomparam: the data used to mock and assert in this test. - :type restfinternalgeomparam: restfinternalgeomparam + :param tfcasegeomparam: the data used to mock and assert in this test. + :type tfcasegeomparam: tfcasegeomparam :param monkeypatch: pytest fixture used to mock module/class variables :type monkeypatch: _pytest.monkeypatch.monkeypatch - - :param sctfcoil: initialised Sctfcoil object - :type sctfcoil: process.sctfcoil.Sctfcoil """ - monkeypatch.setattr(tfcoil_variables, "n_tf_turn", restfinternalgeomparam.n_tf_turn) - - monkeypatch.setattr(tfcoil_variables, "thicndut", restfinternalgeomparam.thicndut) - - monkeypatch.setattr(tfcoil_variables, "thkcas", restfinternalgeomparam.thkcas) - - monkeypatch.setattr(tfcoil_variables, "dr_tf_wp", restfinternalgeomparam.dr_tf_wp) - - monkeypatch.setattr(tfcoil_variables, "tftort", restfinternalgeomparam.tftort) + monkeypatch.setattr(tfcoil_variables, "acasetf", tfcasegeomparam.acasetf) - monkeypatch.setattr(tfcoil_variables, "tfareain", restfinternalgeomparam.tfareain) + monkeypatch.setattr(tfcoil_variables, "acasetfo", tfcasegeomparam.acasetfo) monkeypatch.setattr( - tfcoil_variables, "c_tf_total", restfinternalgeomparam.c_tf_total + tfcoil_variables, "a_tf_leg_outboard", tfcasegeomparam.a_tf_leg_outboard ) - monkeypatch.setattr(tfcoil_variables, "fcoolcp", restfinternalgeomparam.fcoolcp) - - monkeypatch.setattr(tfcoil_variables, "cpttf", restfinternalgeomparam.cpttf) - - monkeypatch.setattr(tfcoil_variables, "cdtfleg", restfinternalgeomparam.cdtfleg) + monkeypatch.setattr(tfcoil_variables, "tfareain", tfcasegeomparam.tfareain) - monkeypatch.setattr(tfcoil_variables, "casthi", restfinternalgeomparam.casthi) + monkeypatch.setattr(tfcoil_variables, "n_tf_coils", tfcasegeomparam.n_tf_coils) - monkeypatch.setattr(tfcoil_variables, "aiwp", restfinternalgeomparam.aiwp) + monkeypatch.setattr(tfcoil_variables, "casths", tfcasegeomparam.casths) - monkeypatch.setattr(tfcoil_variables, "acasetf", restfinternalgeomparam.acasetf) + monkeypatch.setattr(tfcoil_variables, "casthi", tfcasegeomparam.casthi) - monkeypatch.setattr(tfcoil_variables, "tinstf", restfinternalgeomparam.tinstf) + monkeypatch.setattr(tfcoil_variables, "dr_tf_wp", tfcasegeomparam.dr_tf_wp) monkeypatch.setattr( - tfcoil_variables, "n_tf_coils", restfinternalgeomparam.n_tf_coils + build_variables, "r_tf_inboard_in", tfcasegeomparam.r_tf_inboard_in ) monkeypatch.setattr( - build_variables, "dr_tf_outboard", restfinternalgeomparam.dr_tf_outboard + build_variables, "r_tf_inboard_out", tfcasegeomparam.r_tf_inboard_out ) - monkeypatch.setattr( - build_variables, "r_tf_inboard_in", restfinternalgeomparam.r_tf_inboard_in - ) + monkeypatch.setattr(sctfcoil_module, "awpc", tfcasegeomparam.awpc) - monkeypatch.setattr( - build_variables, "r_tf_inboard_out", restfinternalgeomparam.r_tf_inboard_out - ) + monkeypatch.setattr(sctfcoil_module, "r_wp_inner", tfcasegeomparam.r_wp_inner) + + monkeypatch.setattr(sctfcoil_module, "r_wp_outer", tfcasegeomparam.r_wp_outer) - monkeypatch.setattr(build_variables, "r_cp_top", restfinternalgeomparam.r_cp_top) + monkeypatch.setattr(sctfcoil_module, "t_lat_case_av", tfcasegeomparam.t_lat_case_av) - monkeypatch.setattr(physics_variables, "itart", restfinternalgeomparam.itart) + monkeypatch.setattr(sctfcoil_module, "a_case_front", tfcasegeomparam.a_case_front) - sctfcoil.res_tf_internal_geom() + monkeypatch.setattr(sctfcoil_module, "a_case_nose", tfcasegeomparam.a_case_nose) - assert tfcoil_variables.n_tf_turn == pytest.approx( - restfinternalgeomparam.expected_n_tf_turn - ) + monkeypatch.setattr(sctfcoil_module, "theta_coil", tfcasegeomparam.theta_coil) - assert tfcoil_variables.cpttf == pytest.approx( - restfinternalgeomparam.expected_cpttf + monkeypatch.setattr( + sctfcoil_module, "tan_theta_coil", tfcasegeomparam.tan_theta_coil ) - assert tfcoil_variables.cdtfleg == pytest.approx( - restfinternalgeomparam.expected_cdtfleg + sctfcoil.tf_case_geom( + i_tf_wp_geom=tfcasegeomparam.i_tf_wp_geom, + i_tf_case_geom=tfcasegeomparam.i_tf_case_geom, ) - assert tfcoil_variables.aiwp == pytest.approx(restfinternalgeomparam.expected_aiwp) + assert tfcoil_variables.acasetf == pytest.approx(tfcasegeomparam.expected_acasetf) - assert tfcoil_variables.acasetf == pytest.approx( - restfinternalgeomparam.expected_acasetf - ) + assert tfcoil_variables.acasetfo == pytest.approx(tfcasegeomparam.expected_acasetfo) - assert tfcoil_variables.n_tf_turn == pytest.approx( - restfinternalgeomparam.expected_n_tf_turn + assert sctfcoil_module.t_lat_case_av == pytest.approx( + tfcasegeomparam.expected_t_lat_case_av ) - assert tfcoil_variables.cpttf == pytest.approx( - restfinternalgeomparam.expected_cpttf + assert sctfcoil_module.a_case_front == pytest.approx( + tfcasegeomparam.expected_a_case_front ) - assert tfcoil_variables.cdtfleg == pytest.approx( - restfinternalgeomparam.expected_cdtfleg + assert sctfcoil_module.a_case_nose == pytest.approx( + tfcasegeomparam.expected_a_case_nose ) - assert tfcoil_variables.aiwp == pytest.approx(restfinternalgeomparam.expected_aiwp) - assert tfcoil_variables.acasetf == pytest.approx( - restfinternalgeomparam.expected_acasetf - ) +class TfIntegerTurnGeomParam(NamedTuple): + dr_tf_wp: Any = None + tinstf: Any = None -@pytest.mark.parametrize( - "tfresheatingparam", - ( - TfResHeatingParam( - rho_cp=0, - temp_tf_legs_outboard=-1, - thicndut=0.00080000000000000004, - th_joint_contact=0.029999999999999999, - rho_tf_leg=0, - vol_cond_cp=0, - n_tf_turn=1, - thkcas=0, - tftort=0.45367650933034859, - len_tf_coil=15.582502857142856, - res_tf_leg=0, - temp_cp_average=347.13, - a_tf_leg_outboard=0.070242733939617885, - c_tf_total=25500000, - rho_tf_joints=2.5000000000000002e-10, - p_tf_leg_resistive=0, - p_cp_resistive=0, - pres_joints=0, - n_tf_joints_contact=6, - n_tf_joints=4, - n_tf_coils=12, - i_tf_sup=0, - frholeg=1, - frhocp=1, - fcoolcp=0.12725, - casthi=0.0077415000000000019, - a_cp_cool=0, - f_a_tf_cool_outboard=0.20000000000000001, - i_cp_joints=1, - tinstf=0, - dr_tf_outboard=0.15483000000000002, - dr_tf_inboard=0.15483000000000002, - r_cp_top=0.87643571428571443, - hmax=4.4214285714285717, - r_tf_inboard_in=0, - r_tf_inboard_out=0.15483000000000002, - itart=1, - h_cp_top=2.6714285714285717, - is_leg_cp_temp_same=0, - expected_rho_cp=2.0721414e-08, - expected_rho_tf_leg=2.0721414e-08, - expected_vol_cond_cp=12.020160732580297, - expected_res_tf_leg=5.826541893267926e-06, - expected_p_tf_leg_resistive=315725738.84145576, - expected_p_cp_resistive=446175692.00121117, - expected_pres_joints=1944336.7995005273, - expected_a_cp_cool=0.00068328705812121333, - expected_is_leg_cp_temp_same=1, - ), - TfResHeatingParam( - rho_cp=2.0721414e-08, - temp_tf_legs_outboard=-1, - thicndut=0.00080000000000000004, - th_joint_contact=0.029999999999999999, - rho_tf_leg=2.0721414e-08, - vol_cond_cp=12.020160732580297, - n_tf_turn=1, - thkcas=0, - tftort=0.44435902370665786, - len_tf_coil=15.654502857142857, - res_tf_leg=5.647653956699231e-06, - temp_cp_average=347.13, - a_tf_leg_outboard=0.068800107640501845, - c_tf_total=25500000, - rho_tf_joints=2.5000000000000002e-10, - p_tf_leg_resistive=332643748.67243439, - p_cp_resistive=432477095.0716282, - pres_joints=1944336.7995005273, - n_tf_joints_contact=6, - n_tf_joints=4, - n_tf_coils=12, - i_tf_sup=0, - frholeg=1, - frhocp=1, - fcoolcp=0.12725, - casthi=0.0077415000000000019, - a_cp_cool=0.00068328705812121333, - f_a_tf_cool_outboard=0.20000000000000001, - i_cp_joints=1, - tinstf=0, - dr_tf_outboard=0.15483000000000002, - dr_tf_inboard=0.15483000000000002, - r_cp_top=0.85843571428571441, - hmax=4.4214285714285717, - r_tf_inboard_in=0, - r_tf_inboard_out=0.15483000000000002, - itart=1, - h_cp_top=2.6714285714285717, - is_leg_cp_temp_same=1, - expected_rho_cp=2.0721414e-08, - expected_rho_tf_leg=2.0721414e-08, - expected_vol_cond_cp=11.545770024935592, - expected_res_tf_leg=5.9766449694251195e-06, - expected_p_tf_leg_resistive=323859449.2807237, - expected_p_cp_resistive=451516213.33864105, - expected_pres_joints=1944336.7995005273, - expected_a_cp_cool=0.00068328705812121333, - expected_is_leg_cp_temp_same=1, - ), - ), -) -def test_tf_res_heating(tfresheatingparam, monkeypatch, sctfcoil): - """ - Automatically generated Regression Unit Test for tf_res_heating. + tfinsgap: Any = None - This test was generated using data from tests/regression/scenarios/FNSF/IN.DAT. + t_conductor: Any = None - :param tfresheatingparam: the data used to mock and assert in this test. - :type tfresheatingparam: tfresheatingparam + t_turn_tf: Any = None - :param monkeypatch: pytest fixture used to mock module/class variables - :type monkeypatch: _pytest.monkeypatch.monkeypatch - """ + tfc_current: Any = None - monkeypatch.setattr(tfcoil_variables, "rho_cp", tfresheatingparam.rho_cp) + t_wp_toroidal: Any = None - monkeypatch.setattr( - tfcoil_variables, - "temp_tf_legs_outboard", - tfresheatingparam.temp_tf_legs_outboard, - ) + t_conductor_radial: Any = None - monkeypatch.setattr(tfcoil_variables, "thicndut", tfresheatingparam.thicndut) + t_conductor_toroidal: Any = None - monkeypatch.setattr( - tfcoil_variables, "th_joint_contact", tfresheatingparam.th_joint_contact - ) + t_cable_radial: Any = None - monkeypatch.setattr(tfcoil_variables, "rho_tf_leg", tfresheatingparam.rho_tf_leg) + t_cable_toroidal: Any = None - monkeypatch.setattr(tfcoil_variables, "vol_cond_cp", tfresheatingparam.vol_cond_cp) + t_turn_radial: Any = None - monkeypatch.setattr(tfcoil_variables, "n_tf_turn", tfresheatingparam.n_tf_turn) + t_turn_toroidal: Any = None - monkeypatch.setattr(tfcoil_variables, "thkcas", tfresheatingparam.thkcas) + t_cable: Any = None - monkeypatch.setattr(tfcoil_variables, "tftort", tfresheatingparam.tftort) + n_layer: Any = None - monkeypatch.setattr(tfcoil_variables, "len_tf_coil", tfresheatingparam.len_tf_coil) + n_pancake: Any = None - monkeypatch.setattr(tfcoil_variables, "res_tf_leg", tfresheatingparam.res_tf_leg) + thwcndut: Any = None - monkeypatch.setattr( - tfcoil_variables, "temp_cp_average", tfresheatingparam.temp_cp_average - ) + thicndut: Any = None - monkeypatch.setattr( - tfcoil_variables, "a_tf_leg_outboard", tfresheatingparam.a_tf_leg_outboard - ) + expected_t_conductor: Any = None - monkeypatch.setattr(tfcoil_variables, "c_tf_total", tfresheatingparam.c_tf_total) + expected_t_turn_tf: Any = None - monkeypatch.setattr( - tfcoil_variables, "rho_tf_joints", tfresheatingparam.rho_tf_joints - ) + expected_t_conductor_radial: Any = None - monkeypatch.setattr( - tfcoil_variables, "p_tf_leg_resistive", tfresheatingparam.p_tf_leg_resistive - ) + expected_t_conductor_toroidal: Any = None - monkeypatch.setattr( - tfcoil_variables, "p_cp_resistive", tfresheatingparam.p_cp_resistive - ) + expected_t_cable_radial: Any = None - monkeypatch.setattr(tfcoil_variables, "pres_joints", tfresheatingparam.pres_joints) + expected_t_cable_toroidal: Any = None - monkeypatch.setattr( - tfcoil_variables, "n_tf_joints_contact", tfresheatingparam.n_tf_joints_contact - ) + expected_t_turn_radial: Any = None - monkeypatch.setattr(tfcoil_variables, "n_tf_joints", tfresheatingparam.n_tf_joints) + expected_t_turn_toroidal: Any = None - monkeypatch.setattr(tfcoil_variables, "n_tf_coils", tfresheatingparam.n_tf_coils) + expected_t_cable: Any = None - monkeypatch.setattr(tfcoil_variables, "i_tf_sup", tfresheatingparam.i_tf_sup) + expected_acstf: Any = None - monkeypatch.setattr(tfcoil_variables, "frholeg", tfresheatingparam.frholeg) + expected_acndttf: Any = None - monkeypatch.setattr(tfcoil_variables, "frhocp", tfresheatingparam.frhocp) + expected_insulation_area: Any = None - monkeypatch.setattr(tfcoil_variables, "fcoolcp", tfresheatingparam.fcoolcp) + expected_cpttf: Any = None - monkeypatch.setattr(tfcoil_variables, "casthi", tfresheatingparam.casthi) + expected_n_tf_turn: Any = None - monkeypatch.setattr(tfcoil_variables, "a_cp_cool", tfresheatingparam.a_cp_cool) - monkeypatch.setattr( - tfcoil_variables, "f_a_tf_cool_outboard", tfresheatingparam.f_a_tf_cool_outboard - ) +@pytest.mark.parametrize( + "tfintegerturngeomparam", + ( + TfIntegerTurnGeomParam( + dr_tf_wp=0.54261087836601019, + tinstf=0.0080000000000000019, + tfinsgap=0.01, + t_conductor=0, + t_turn_tf=0, + tfc_current=14805350.287500001, + t_wp_toroidal=1.299782604942499, + t_conductor_radial=0, + t_conductor_toroidal=0, + t_cable_radial=0, + t_cable_toroidal=0, + t_turn_radial=0, + t_turn_toroidal=0, + t_cable=0, + n_layer=10, + n_pancake=20, + thwcndut=0.0080000000000000002, + thicndut=0.002, + expected_t_conductor=0.052553108427885735, + expected_t_turn_tf=0.056579413904423038, + expected_t_conductor_radial=0.046661087836601015, + expected_t_conductor_toroidal=0.059189130247124938, + expected_t_cable_radial=0.030661087836601014, + expected_t_cable_toroidal=0.043189130247124938, + expected_t_turn_radial=0.050661087836601018, + expected_t_turn_toroidal=0.063189130247124942, + expected_t_cable=0.036389912284773368, + expected_acstf=0.001293323051622732, + expected_acndttf=0.0014685061538103825, + expected_insulation_area=0.00043940087233490435, + expected_cpttf=74026.751437500003, + expected_n_tf_turn=200, + ), + TfIntegerTurnGeomParam( + dr_tf_wp=0.54261087836601019, + tinstf=0.0080000000000000019, + tfinsgap=0.01, + t_conductor=0.052553108427885735, + t_turn_tf=0.056579413904423038, + tfc_current=14805350.287500001, + t_wp_toroidal=1.299782604942499, + t_conductor_radial=0.046661087836601015, + t_conductor_toroidal=0.059189130247124938, + t_cable_radial=0.030661087836601014, + t_cable_toroidal=0.043189130247124938, + t_turn_radial=0.050661087836601018, + t_turn_toroidal=0.063189130247124942, + t_cable=0.036389912284773368, + n_layer=10, + n_pancake=20, + thwcndut=0.0080000000000000002, + thicndut=0.002, + expected_t_conductor=0.052553108427885735, + expected_t_turn_tf=0.056579413904423038, + expected_t_conductor_radial=0.046661087836601015, + expected_t_conductor_toroidal=0.059189130247124938, + expected_t_cable_radial=0.030661087836601014, + expected_t_cable_toroidal=0.043189130247124938, + expected_t_turn_radial=0.050661087836601018, + expected_t_turn_toroidal=0.063189130247124942, + expected_t_cable=0.036389912284773368, + expected_acstf=0.001293323051622732, + expected_acndttf=0.0014685061538103825, + expected_insulation_area=0.00043940087233490435, + expected_cpttf=74026.751437500003, + expected_n_tf_turn=200, + ), + ), +) +def test_tf_integer_turn_geom(tfintegerturngeomparam, monkeypatch, sctfcoil): + """ + Automatically generated Regression Unit Test for tf_integer_turn_geom. + + This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. + + :param tfintegerturngeomparam: the data used to mock and assert in this test. + :type tfintegerturngeomparam: tfintegerturngeomparam + + :param monkeypatch: pytest fixture used to mock module/class variables + :type monkeypatch: _pytest.monkeypatch.monkeypatch + """ + + monkeypatch.setattr(tfcoil_variables, "dr_tf_wp", tfintegerturngeomparam.dr_tf_wp) - monkeypatch.setattr(tfcoil_variables, "i_cp_joints", tfresheatingparam.i_cp_joints) + monkeypatch.setattr(tfcoil_variables, "tinstf", tfintegerturngeomparam.tinstf) - monkeypatch.setattr(tfcoil_variables, "tinstf", tfresheatingparam.tinstf) + monkeypatch.setattr(tfcoil_variables, "tfinsgap", tfintegerturngeomparam.tfinsgap) monkeypatch.setattr( - build_variables, "dr_tf_outboard", tfresheatingparam.dr_tf_outboard + tfcoil_variables, "t_conductor", tfintegerturngeomparam.t_conductor ) + monkeypatch.setattr(tfcoil_variables, "t_turn_tf", tfintegerturngeomparam.t_turn_tf) + monkeypatch.setattr( - build_variables, "dr_tf_inboard", tfresheatingparam.dr_tf_inboard + sctfcoil_module, "tfc_current", tfintegerturngeomparam.tfc_current ) - monkeypatch.setattr(build_variables, "r_cp_top", tfresheatingparam.r_cp_top) - - monkeypatch.setattr(build_variables, "hmax", tfresheatingparam.hmax) + monkeypatch.setattr( + sctfcoil_module, "t_wp_toroidal", tfintegerturngeomparam.t_wp_toroidal + ) monkeypatch.setattr( - build_variables, "r_tf_inboard_in", tfresheatingparam.r_tf_inboard_in + sctfcoil_module, "t_conductor_radial", tfintegerturngeomparam.t_conductor_radial ) monkeypatch.setattr( - build_variables, "r_tf_inboard_out", tfresheatingparam.r_tf_inboard_out + sctfcoil_module, + "t_conductor_toroidal", + tfintegerturngeomparam.t_conductor_toroidal, ) - monkeypatch.setattr(physics_variables, "itart", tfresheatingparam.itart) + monkeypatch.setattr( + sctfcoil_module, "t_cable_radial", tfintegerturngeomparam.t_cable_radial + ) - monkeypatch.setattr(sctfcoil_module, "h_cp_top", tfresheatingparam.h_cp_top) + monkeypatch.setattr( + sctfcoil_module, "t_cable_toroidal", tfintegerturngeomparam.t_cable_toroidal + ) monkeypatch.setattr( - sctfcoil_module, "is_leg_cp_temp_same", tfresheatingparam.is_leg_cp_temp_same + sctfcoil_module, "t_turn_radial", tfintegerturngeomparam.t_turn_radial ) - sctfcoil.tf_res_heating() + monkeypatch.setattr( + sctfcoil_module, "t_turn_toroidal", tfintegerturngeomparam.t_turn_toroidal + ) - assert tfcoil_variables.rho_cp == pytest.approx(tfresheatingparam.expected_rho_cp) + monkeypatch.setattr(sctfcoil_module, "t_cable", tfintegerturngeomparam.t_cable) - assert tfcoil_variables.rho_tf_leg == pytest.approx( - tfresheatingparam.expected_rho_tf_leg + ( + acstf, + acndttf, + insulation_area, + cpttf, + n_tf_turn, + ) = sctfcoil.tf_integer_turn_geom( + n_layer=tfintegerturngeomparam.n_layer, + n_pancake=tfintegerturngeomparam.n_pancake, + thwcndut=tfintegerturngeomparam.thwcndut, + thicndut=tfintegerturngeomparam.thicndut, ) - assert tfcoil_variables.vol_cond_cp == pytest.approx( - tfresheatingparam.expected_vol_cond_cp + assert tfcoil_variables.t_conductor == pytest.approx( + tfintegerturngeomparam.expected_t_conductor ) - assert tfcoil_variables.res_tf_leg == pytest.approx( - tfresheatingparam.expected_res_tf_leg + assert tfcoil_variables.t_turn_tf == pytest.approx( + tfintegerturngeomparam.expected_t_turn_tf ) - assert tfcoil_variables.p_tf_leg_resistive == pytest.approx( - tfresheatingparam.expected_p_tf_leg_resistive + assert sctfcoil_module.t_conductor_radial == pytest.approx( + tfintegerturngeomparam.expected_t_conductor_radial ) - assert tfcoil_variables.p_cp_resistive == pytest.approx( - tfresheatingparam.expected_p_cp_resistive + assert sctfcoil_module.t_conductor_toroidal == pytest.approx( + tfintegerturngeomparam.expected_t_conductor_toroidal ) - assert tfcoil_variables.pres_joints == pytest.approx( - tfresheatingparam.expected_pres_joints + assert sctfcoil_module.t_cable_radial == pytest.approx( + tfintegerturngeomparam.expected_t_cable_radial ) - assert tfcoil_variables.a_cp_cool == pytest.approx( - tfresheatingparam.expected_a_cp_cool + assert sctfcoil_module.t_cable_toroidal == pytest.approx( + tfintegerturngeomparam.expected_t_cable_toroidal ) - assert sctfcoil_module.is_leg_cp_temp_same == pytest.approx( - tfresheatingparam.expected_is_leg_cp_temp_same + assert sctfcoil_module.t_turn_radial == pytest.approx( + tfintegerturngeomparam.expected_t_turn_radial ) - -class CpostParam(NamedTuple): - n_tf_coils: Any = None - - hmax: Any = None - - r_tf_inboard_in: Any = None - - r_tf_inboard_out: Any = None - - r_cp_top: Any = None - - ztop: Any = None - - hmaxi: Any = None - - cas_in_th: Any = None - - cas_out_th: Any = None - - gr_ins_th: Any = None - - ins_th: Any = None - - n_tf_turn: Any = None - - curr: Any = None - - rho: Any = None - - fcool: Any = None - - expected_vol_ins_cp: Any = None - - expected_vol_gr_ins_cp: Any = None - - expected_vol_case_cp: Any = None - - expected_respow: Any = None - - expected_vol_cond_cp: Any = None - - expected_a_cp_cool: Any = None - - -@pytest.mark.parametrize( - "cpostparam", - ( - CpostParam( - n_tf_coils=12, - hmax=4.4214285714285717, - r_tf_inboard_in=0, - r_tf_inboard_out=0.15483000000000002, - r_cp_top=0.87643571428571443, - ztop=2.6714285714285717, - hmaxi=4.5762585714285713, - cas_in_th=0, - cas_out_th=0.0077415000000000019, - gr_ins_th=0, - ins_th=0.00080000000000000004, - n_tf_turn=1, - curr=25500000, - rho=2.1831760869565221e-08, - fcool=0.12725, - expected_vol_ins_cp=0.12917075053120922, - expected_vol_gr_ins_cp=0, - expected_vol_case_cp=0.12791418544773489, - expected_respow=470083798.99090022, - expected_vol_cond_cp=12.020160732580297, - expected_a_cp_cool=0.00068328705812121333, - ), - CpostParam( - n_tf_coils=12, - hmax=4.4214285714285717, - r_tf_inboard_in=0, - r_tf_inboard_out=0.15483000000000002, - r_cp_top=0.85843571428571441, - ztop=2.6714285714285717, - hmaxi=4.5762585714285713, - cas_in_th=0, - cas_out_th=0.0077415000000000019, - gr_ins_th=0, - ins_th=0.00080000000000000004, - n_tf_turn=1, - curr=25500000, - rho=2.1831760869565221e-08, - fcool=0.12725, - expected_vol_ins_cp=0.12679799009998483, - expected_vol_gr_ins_cp=0, - expected_vol_case_cp=0.12648575512245444, - expected_respow=475710489.56122422, - expected_vol_cond_cp=11.545770024935592, - expected_a_cp_cool=0.00068328705812121333, - ), - ), -) -def test_cpost(cpostparam, monkeypatch, sctfcoil): - """ - Automatically generated Regression Unit Test for cpost. - - This test was generated using data from tests/regression/scenarios/FNSF/IN.DAT. - - :param cpostparam: the data used to mock and assert in this test. - :type cpostparam: cpostparam - - :param monkeypatch: pytest fixture used to mock module/class variables - :type monkeypatch: _pytest.monkeypatch.monkeypatch - """ - - monkeypatch.setattr(build_variables, "hmax", cpostparam.hmax) - - ( - a_cp_cool, - vol_cond_cp, - respow, - vol_ins_cp, - vol_case_cp, - vol_gr_ins_cp, - ) = sctfcoil.cpost( - r_tf_inboard_in=cpostparam.r_tf_inboard_in, - r_tf_inboard_out=cpostparam.r_tf_inboard_out, - r_cp_top=cpostparam.r_cp_top, - ztop=cpostparam.ztop, - hmaxi=cpostparam.hmaxi, - cas_in_th=cpostparam.cas_in_th, - cas_out_th=cpostparam.cas_out_th, - gr_ins_th=cpostparam.gr_ins_th, - ins_th=cpostparam.ins_th, - n_tf_turn=cpostparam.n_tf_turn, - curr=cpostparam.curr, - rho=cpostparam.rho, - fcool=cpostparam.fcool, - n_tf_coils=cpostparam.n_tf_coils, + assert sctfcoil_module.t_turn_toroidal == pytest.approx( + tfintegerturngeomparam.expected_t_turn_toroidal ) - assert vol_ins_cp == pytest.approx(cpostparam.expected_vol_ins_cp) - - assert vol_gr_ins_cp == pytest.approx(cpostparam.expected_vol_gr_ins_cp) - - assert vol_case_cp == pytest.approx(cpostparam.expected_vol_case_cp) - - assert respow == pytest.approx(cpostparam.expected_respow) - - assert vol_cond_cp == pytest.approx(cpostparam.expected_vol_cond_cp) - - assert a_cp_cool == pytest.approx(cpostparam.expected_a_cp_cool) + assert sctfcoil_module.t_cable == pytest.approx( + tfintegerturngeomparam.expected_t_cable + ) + assert acstf == pytest.approx(tfintegerturngeomparam.expected_acstf) -class TfFieldAndForceParam(NamedTuple): - rminor: Any = None + assert acndttf == pytest.approx(tfintegerturngeomparam.expected_acndttf) - rmajor: Any = None + assert insulation_area == pytest.approx( + tfintegerturngeomparam.expected_insulation_area + ) - bt: Any = None + assert cpttf == pytest.approx(tfintegerturngeomparam.expected_cpttf) - itart: Any = None + assert n_tf_turn == pytest.approx(tfintegerturngeomparam.expected_n_tf_turn) - r_tf_outboard_mid: Any = None - r_vv_inboard_out: Any = None +class TfAveragedTurnGeomParam(NamedTuple): + layer_ins: Any = None - r_tf_inboard_mid: Any = None + t_conductor: Any = None - r_cp_top: Any = None + t_turn_tf: Any = None - vforce: Any = None + t_turn_tf_is_input: Any = None - n_tf_coils: Any = None + cpttf: Any = None - taucq: Any = None + t_cable_tf: Any = None - sigvvall: Any = None + t_cable_tf_is_input: Any = None - cforce: Any = None + awptf: Any = None - c_tf_total: Any = None + t_turn_radial: Any = None - bmaxtf: Any = None + t_turn_toroidal: Any = None - i_tf_sup: Any = None + t_cable: Any = None - f_vforce_inboard: Any = None + i_tf_sc_mat: Any = None - vforce_outboard: Any = None + jwptf: Any = None - tinstf: Any = None + thwcndut: Any = None thicndut: Any = None - dr_tf_wp: Any = None - - tfinsgap: Any = None - - i_cp_joints: Any = None - - casthi: Any = None + expected_t_conductor: Any = None - r_tf_outboard_in: Any = None + expected_t_turn_tf: Any = None - r_wp_inner: Any = None + expected_t_turn_radial: Any = None - r_wp_outer: Any = None + expected_t_turn_toroidal: Any = None - vforce_inboard_tot: Any = None + expected_t_cable: Any = None - expected_vforce: Any = None + expected_acstf: Any = None - expected_cforce: Any = None + expected_acndttf: Any = None - expected_f_vforce_inboard: Any = None + expected_insulation_area: Any = None - expected_vforce_outboard: Any = None - - expected_vforce_inboard_tot: Any = None + expected_n_tf_turn: Any = None @pytest.mark.parametrize( - "tffieldandforceparam", + "tfaveragedturngeomparam", ( - TfFieldAndForceParam( - rminor=0.97142857142857153, - rmajor=1.7000000000000002, - bt=3, - itart=1, - r_tf_outboard_mid=4.1688435714285719, - r_vv_inboard_out=0.20483000000000001, - r_tf_inboard_mid=0.077415000000000012, - r_cp_top=0.87643571428571443, - vforce=0, - n_tf_coils=12, - taucq=30, - sigvvall=93000000, - cforce=0, - c_tf_total=25500000, - bmaxtf=34.862617362267024, - i_tf_sup=0, - f_vforce_inboard=0.5, - vforce_outboard=0, - tinstf=0, + TfAveragedTurnGeomParam( + layer_ins=0, + t_conductor=0, + t_turn_tf=0, + t_turn_tf_is_input=False, + cpttf=65000, + t_cable_tf=0, + t_cable_tf_is_input=False, + awptf=0.60510952642236249, + t_turn_radial=0, + t_turn_toroidal=0, + t_cable=0, + i_tf_sc_mat=5, + jwptf=26493137.688284047, + thwcndut=0.0080000000000000019, thicndut=0.00080000000000000004, - dr_tf_wp=0.15483000000000002, - tfinsgap=0.01, - i_cp_joints=1, - casthi=0.0077415000000000019, - r_tf_outboard_in=4.0914285714285716, - r_wp_inner=0, - r_wp_outer=0.14708850000000001, - vforce_inboard_tot=0, - expected_vforce=12380916.66459452, - expected_cforce=37041530.947408713, - expected_f_vforce_inboard=0.59539634897566385, - expected_vforce_outboard=8413494.7991220243, - expected_vforce_inboard_tot=148570999.97513425, + expected_t_conductor=0.047932469413859431, + expected_t_turn_tf=0.049532469413859428, + expected_t_turn_radial=0.049532469413859428, + expected_t_turn_toroidal=0.049532469413859428, + expected_t_cable=0.031932469413859424, + expected_acstf=0.00098877993839630008, + expected_acndttf=0.0013087416857142699, + expected_insulation_area=0.00015594390212434958, + expected_n_tf_turn=246.63461538461544, ), - TfFieldAndForceParam( - rminor=0.97142857142857153, - rmajor=1.7000000000000002, - bt=3, - itart=1, - r_tf_outboard_mid=4.1868435714285717, - r_vv_inboard_out=0.20483000000000001, - r_tf_inboard_mid=0.077415000000000012, - r_cp_top=0.85843571428571441, - vforce=12380916.66459452, - n_tf_coils=12, - taucq=30, - sigvvall=93000000, - cforce=37041530.947408713, - c_tf_total=25500000, - bmaxtf=34.862617362267024, - i_tf_sup=0, - f_vforce_inboard=0.59539634897566385, - vforce_outboard=8413494.7991220243, - tinstf=0, + TfAveragedTurnGeomParam( + layer_ins=0, + t_conductor=0.047932469413859431, + t_turn_tf=0.049532469413859428, + t_turn_tf_is_input=False, + cpttf=65000, + t_cable_tf=0, + t_cable_tf_is_input=False, + awptf=0.60510952642236249, + t_turn_radial=0.049532469413859428, + t_turn_toroidal=0.049532469413859428, + t_cable=0.031932469413859424, + i_tf_sc_mat=5, + jwptf=26493137.688284047, + thwcndut=0.0080000000000000019, thicndut=0.00080000000000000004, - dr_tf_wp=0.14708850000000001, - tfinsgap=0.01, - i_cp_joints=1, - casthi=0.0077415000000000019, - r_tf_outboard_in=4.1094285714285714, - r_wp_inner=0, - r_wp_outer=0.14708850000000001, - vforce_inboard_tot=148570999.97513425, - expected_vforce=12268469.138442248, - expected_cforce=37041530.947408713, - expected_f_vforce_inboard=0.58932254522566518, - expected_vforce_outboard=8549450.0771621168, - expected_vforce_inboard_tot=147221629.66130698, + expected_t_conductor=0.047932469413859431, + expected_t_turn_tf=0.049532469413859428, + expected_t_turn_radial=0.049532469413859428, + expected_t_turn_toroidal=0.049532469413859428, + expected_t_cable=0.031932469413859424, + expected_acstf=0.00098877993839630008, + expected_acndttf=0.0013087416857142699, + expected_insulation_area=0.00015594390212434958, + expected_n_tf_turn=246.63461538461544, + ), + TfAveragedTurnGeomParam( + layer_ins=0, + t_conductor=5.712e-02, + t_turn_tf=0.05872, + t_turn_tf_is_input=True, + cpttf=0, + t_cable_tf=0, + t_cable_tf_is_input=False, + awptf=0.60510952642236249, + t_turn_radial=0.05872, + t_turn_toroidal=0.05872, + t_cable=0.04109, + i_tf_sc_mat=1, + jwptf=2.301e07, + thwcndut=8.015e-03, + thicndut=8.0e-4, + expected_t_conductor=5.712e-02, + expected_t_turn_tf=0.05872, + expected_t_turn_radial=0.05872, + expected_t_turn_toroidal=0.05872, + expected_t_cable=0.04109, + expected_acstf=0.001657369442, + expected_acndttf=0.001605324958, + expected_insulation_area=0.000185344, + expected_n_tf_turn=175.49384787, + ), + TfAveragedTurnGeomParam( + layer_ins=0, + t_conductor=0.058296, + t_turn_tf=0, + t_turn_tf_is_input=False, + cpttf=0, + t_cable_tf=0.042, + t_cable_tf_is_input=True, + awptf=0.60510952642236249, + t_turn_radial=0.05872, + t_turn_toroidal=0.05872, + t_cable=0.04109, + i_tf_sc_mat=1, + jwptf=2.673e07, + thwcndut=8.148e-03, + thicndut=8.0e-4, + expected_t_conductor=0.058296, + expected_t_turn_tf=0.059896, + expected_t_turn_radial=0.059896, + expected_t_turn_toroidal=0.059896, + expected_t_cable=0.042, + expected_acstf=0.001731943361, + expected_acndttf=0.001666480255, + expected_insulation_area=0.00018910719999999962, + expected_n_tf_turn=168.6701961481806, ), ), ) -def test_tf_field_and_force(tffieldandforceparam, monkeypatch, sctfcoil): +def test_tf_averaged_turn_geom(tfaveragedturngeomparam, monkeypatch, sctfcoil): """ - Automatically generated Regression Unit Test for tf_field_and_force. + Automatically generated Regression Unit Test for tf_averaged_turn_geom. - This test was generated using data from tests/regression/scenarios/FNSF/IN.DAT. + This test was generated using data from tests/regression/scenarios/i_mode/IN.DAT. - :param tffieldandforceparam: the data used to mock and assert in this test. - :type tffieldandforceparam: tffieldandforceparam + :param tfaveragedturngeomparam: the data used to mock and assert in this test. + :type tfaveragedturngeomparam: tfaveragedturngeomparam :param monkeypatch: pytest fixture used to mock module/class variables :type monkeypatch: _pytest.monkeypatch.monkeypatch """ - monkeypatch.setattr(physics_variables, "rminor", tffieldandforceparam.rminor) - - monkeypatch.setattr(physics_variables, "rmajor", tffieldandforceparam.rmajor) - - monkeypatch.setattr(physics_variables, "bt", tffieldandforceparam.bt) - - monkeypatch.setattr(physics_variables, "itart", tffieldandforceparam.itart) - monkeypatch.setattr( - build_variables, "r_tf_outboard_mid", tffieldandforceparam.r_tf_outboard_mid + tfcoil_variables, "layer_ins", tfaveragedturngeomparam.layer_ins ) monkeypatch.setattr( - build_variables, "r_vv_inboard_out", tffieldandforceparam.r_vv_inboard_out + tfcoil_variables, "t_conductor", tfaveragedturngeomparam.t_conductor ) monkeypatch.setattr( - build_variables, "r_tf_inboard_mid", tffieldandforceparam.r_tf_inboard_mid + tfcoil_variables, "t_turn_tf", tfaveragedturngeomparam.t_turn_tf ) - monkeypatch.setattr(build_variables, "r_cp_top", tffieldandforceparam.r_cp_top) - - monkeypatch.setattr(tfcoil_variables, "vforce", tffieldandforceparam.vforce) - - monkeypatch.setattr(tfcoil_variables, "n_tf_coils", tffieldandforceparam.n_tf_coils) - - monkeypatch.setattr(tfcoil_variables, "cforce", tffieldandforceparam.cforce) - - monkeypatch.setattr(tfcoil_variables, "c_tf_total", tffieldandforceparam.c_tf_total) - - monkeypatch.setattr(tfcoil_variables, "bmaxtf", tffieldandforceparam.bmaxtf) + monkeypatch.setattr( + tfcoil_variables, + "t_turn_tf_is_input", + tfaveragedturngeomparam.t_turn_tf_is_input, + ) - monkeypatch.setattr(tfcoil_variables, "i_tf_sup", tffieldandforceparam.i_tf_sup) + monkeypatch.setattr(tfcoil_variables, "cpttf", tfaveragedturngeomparam.cpttf) monkeypatch.setattr( - tfcoil_variables, "f_vforce_inboard", tffieldandforceparam.f_vforce_inboard + tfcoil_variables, "t_cable_tf", tfaveragedturngeomparam.t_cable_tf ) monkeypatch.setattr( - tfcoil_variables, "vforce_outboard", tffieldandforceparam.vforce_outboard + tfcoil_variables, + "t_cable_tf_is_input", + tfaveragedturngeomparam.t_cable_tf_is_input, ) - monkeypatch.setattr(tfcoil_variables, "tinstf", tffieldandforceparam.tinstf) - - monkeypatch.setattr(tfcoil_variables, "thicndut", tffieldandforceparam.thicndut) - - monkeypatch.setattr(tfcoil_variables, "dr_tf_wp", tffieldandforceparam.dr_tf_wp) - - monkeypatch.setattr(tfcoil_variables, "tfinsgap", tffieldandforceparam.tfinsgap) + monkeypatch.setattr(sctfcoil_module, "awptf", tfaveragedturngeomparam.awptf) monkeypatch.setattr( - tfcoil_variables, "i_cp_joints", tffieldandforceparam.i_cp_joints + sctfcoil_module, "t_turn_radial", tfaveragedturngeomparam.t_turn_radial ) - monkeypatch.setattr(tfcoil_variables, "casthi", tffieldandforceparam.casthi) - monkeypatch.setattr( - sctfcoil_module, "r_tf_outboard_in", tffieldandforceparam.r_tf_outboard_in + sctfcoil_module, "t_turn_toroidal", tfaveragedturngeomparam.t_turn_toroidal ) - monkeypatch.setattr(sctfcoil_module, "r_wp_inner", tffieldandforceparam.r_wp_inner) - - monkeypatch.setattr(sctfcoil_module, "r_wp_outer", tffieldandforceparam.r_wp_outer) + monkeypatch.setattr(sctfcoil_module, "t_cable", tfaveragedturngeomparam.t_cable) - monkeypatch.setattr( - sctfcoil_module, "vforce_inboard_tot", tffieldandforceparam.vforce_inboard_tot + acstf, acndttf, insulation_area, n_tf_turn = sctfcoil.tf_averaged_turn_geom( + i_tf_sc_mat=tfaveragedturngeomparam.i_tf_sc_mat, + jwptf=tfaveragedturngeomparam.jwptf, + thwcndut=tfaveragedturngeomparam.thwcndut, + thicndut=tfaveragedturngeomparam.thicndut, ) - sctfcoil.tf_field_and_force() + assert tfcoil_variables.t_conductor == pytest.approx( + tfaveragedturngeomparam.expected_t_conductor + ) - assert tfcoil_variables.vforce == pytest.approx( - tffieldandforceparam.expected_vforce + assert tfcoil_variables.t_turn_tf == pytest.approx( + tfaveragedturngeomparam.expected_t_turn_tf ) - assert tfcoil_variables.cforce == pytest.approx( - tffieldandforceparam.expected_cforce + assert sctfcoil_module.t_turn_radial == pytest.approx( + tfaveragedturngeomparam.expected_t_turn_radial ) - assert tfcoil_variables.f_vforce_inboard == pytest.approx( - tffieldandforceparam.expected_f_vforce_inboard + assert sctfcoil_module.t_turn_toroidal == pytest.approx( + tfaveragedturngeomparam.expected_t_turn_toroidal ) - assert tfcoil_variables.vforce_outboard == pytest.approx( - tffieldandforceparam.expected_vforce_outboard + assert sctfcoil_module.t_cable == pytest.approx( + tfaveragedturngeomparam.expected_t_cable ) - assert sctfcoil_module.vforce_inboard_tot == pytest.approx( - tffieldandforceparam.expected_vforce_inboard_tot + assert acstf == pytest.approx(tfaveragedturngeomparam.expected_acstf) + + assert acndttf == pytest.approx(tfaveragedturngeomparam.expected_acndttf) + + assert insulation_area == pytest.approx( + tfaveragedturngeomparam.expected_insulation_area ) + assert n_tf_turn == pytest.approx(tfaveragedturngeomparam.expected_n_tf_turn) -class TfcindParam(NamedTuple): - yarc: Any = None - xarc: Any = None +class TfWpCurrentsParam(NamedTuple): + c_tf_total: Any = None - tfind: Any = None + n_tf_coils: Any = None - tfthk: Any = None + jwptf: Any = None - expected_yarc: Any = None + awptf: Any = None - expected_tfind: Any = None + expected_jwptf: Any = None @pytest.mark.parametrize( - "tfcindparam", + "tfwpcurrentsparam", ( - TfcindParam( - yarc=np.array( - ( - 4.5228880258064512, - 7.5381467096774184, - 0, - -9.0730900215620327, - -5.4438540129372193, - ), - order="F", - ), - xarc=np.array( - ( - 4.20194118510911, - 8.316545161290323, - 15.915405859443332, - 8.316545161290323, - 4.20194118510911, - ), - order="F", - ), - tfind=0, - tfthk=1.208, - expected_tfind=5.4453892599192845e-06, + TfWpCurrentsParam( + c_tf_total=256500000.00000003, + n_tf_coils=16, + jwptf=0, + awptf=0.60510952642236249, + expected_jwptf=26493137.688284047, ), - TfcindParam( - yarc=np.array( - ( - 4.5336880258064509, - 7.5561467096774191, - 0, - -9.0730900215620327, - -5.4438540129372193, - ), - order="F", - ), - xarc=np.array( - ( - 4.20194118510911, - 8.316545161290323, - 15.915405859443332, - 8.316545161290323, - 4.20194118510911, - ), - order="F", - ), - tfind=5.4524893280368181e-06, - tfthk=1.208, - expected_tfind=5.4524893280368181e-06, + TfWpCurrentsParam( + c_tf_total=256500000.00000003, + n_tf_coils=16, + jwptf=26493137.688284047, + awptf=0.60510952642236249, + expected_jwptf=26493137.688284047, ), ), ) -def test_tfcind(tfcindparam, monkeypatch, sctfcoil): +def test_tf_wp_currents(tfwpcurrentsparam, monkeypatch, sctfcoil): """ - Automatically generated Regression Unit Test for tfcind. + Automatically generated Regression Unit Test for tf_wp_currents. - This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. + This test was generated using data from tests/regression/scenarios/i_mode/IN.DAT. - :param tfcindparam: the data used to mock and assert in this test. - :type tfcindparam: tfcindparam + :param tfwpcurrentsparam: the data used to mock and assert in this test. + :type tfwpcurrentsparam: tfwpcurrentsparam :param monkeypatch: pytest fixture used to mock module/class variables :type monkeypatch: _pytest.monkeypatch.monkeypatch """ - monkeypatch.setattr(tfcoil_variables, "tfind", tfcindparam.tfind) - - tfind = sctfcoil.tfcind( - tfthk=tfcindparam.tfthk, xarc=tfcindparam.xarc, yarc=tfcindparam.yarc - ) - - assert tfind == pytest.approx(tfcindparam.expected_tfind) - - -class TfCoilAreaAndMassesParam(NamedTuple): - hr1: Any = None - - r_tf_outboard_mid: Any = None - - dr_tf_inboard: Any = None - - r_tf_inboard_mid: Any = None - - r_tf_inboard_in: Any = None - - r_tf_inboard_out: Any = None - - hmax: Any = None - - denstl: Any = None - - whtconsh: Any = None - - whttf: Any = None - - whtcas: Any = None - - tficrn: Any = None - - tfcryoarea: Any = None - - whtgw: Any = None - - tfocrn: Any = None - - whtconsc: Any = None - - whtconcu: Any = None - - whtcon: Any = None - - whtconin: Any = None - - vftf: Any = None - - dcond: Any = None - - dcondins: Any = None - - len_tf_coil: Any = None - - dcase: Any = None - - acndttf: Any = None - - n_tf_turn: Any = None - - n_tf_coils: Any = None - - aiwp: Any = None - - acasetfo: Any = None - - acasetf: Any = None - - fcutfsu: Any = None - - awphec: Any = None - - acstf: Any = None - - whttflgs: Any = None - - whtcp: Any = None - - whtconal: Any = None - - vol_cond_cp: Any = None - - i_tf_sup: Any = None - - i_tf_sc_mat: Any = None - - a_tf_leg_outboard: Any = None - - thkcas: Any = None - - voltfleg: Any = None - - cplen: Any = None - - itart: Any = None - - awpc: Any = None + monkeypatch.setattr(tfcoil_variables, "c_tf_total", tfwpcurrentsparam.c_tf_total) - awptf: Any = None + monkeypatch.setattr(tfcoil_variables, "n_tf_coils", tfwpcurrentsparam.n_tf_coils) - vol_ins_cp: Any = None + monkeypatch.setattr(tfcoil_variables, "jwptf", tfwpcurrentsparam.jwptf) - vol_gr_ins_cp: Any = None + monkeypatch.setattr(sctfcoil_module, "awptf", tfwpcurrentsparam.awptf) - vol_case_cp: Any = None + sctfcoil.tf_wp_currents() - a_leg_ins: Any = None - - a_leg_gr_ins: Any = None - - a_leg_cond: Any = None - - theta_coil: Any = None - - tan_theta_coil: Any = None - - expected_whtconsh: Any = None - - expected_whtcas: Any = None - - expected_tficrn: Any = None - - expected_tfcryoarea: Any = None - - expected_whtgw: Any = None - - expected_tfocrn: Any = None - - expected_whtconsc: Any = None - - expected_whtconcu: Any = None - - expected_whtcon: Any = None - - expected_whtconin: Any = None - - expected_cplen: Any = None - - -@pytest.mark.parametrize( - "tfcoilareaandmassesparam", - ( - TfCoilAreaAndMassesParam( - hr1=0, - r_tf_outboard_mid=16.519405859443332, - dr_tf_inboard=1.208, - r_tf_inboard_mid=3.5979411851091103, - r_tf_inboard_in=2.9939411851091102, - r_tf_inboard_out=4.20194118510911, - hmax=9.0730900215620327, - denstl=7800, - whtconsh=0, - whttf=0, - whtcas=0, - tficrn=0, - tfcryoarea=0, - whtgw=0, - tfocrn=0, - whtconsc=0, - whtconcu=0, - whtcon=0, - whtconin=0, - vftf=0.30000000000000004, - dcond=np.array( - np.array( - (6080, 6080, 6070, 6080, 6080, 8500, 6070, 8500, 8500), order="F" - ), - order="F", - ).transpose(), - dcondins=1800, - len_tf_coil=50.483843027201402, - dcase=8000, - acndttf=0.0014685061538103825, - n_tf_turn=200, - n_tf_coils=16, - aiwp=0.087880174466980876, - acasetfo=1.2752592893394648, - acasetf=1.0015169239205168, - fcutfsu=0.80884, - awphec=0.015707963267948974, - acstf=0.001293323051622732, - whttflgs=0, - whtcp=0, - whtconal=0, - vol_cond_cp=0, - i_tf_sup=1, - i_tf_sc_mat=5, - a_tf_leg_outboard=1.9805354702921749, - thkcas=0.52465000000000006, - voltfleg=0, - cplen=0, - itart=0, - awpc=0.70527618095271016, - awptf=0.64024601555360383, - vol_ins_cp=0, - vol_gr_ins_cp=0, - vol_case_cp=0, - a_leg_ins=0, - a_leg_gr_ins=0, - a_leg_cond=0, - theta_coil=0.19634954084936207, - tan_theta_coil=0.19891236737965801, - expected_whtconsh=115651.90127937049, - expected_whtcas=1034021.9996272125, - expected_tficrn=0.8197580588957678, - expected_tfcryoarea=6381.2092203414386, - expected_whtgw=5909.3507916745702, - expected_tfocrn=0.59553192892551199, - expected_whtconsc=5802.5700395134345, - expected_whtconcu=58744.465423173802, - expected_whtcon=188184.68882144717, - expected_whtconin=7985.7520793894437, - expected_cplen=20.562180043124066, - ), - TfCoilAreaAndMassesParam( - hr1=0, - r_tf_outboard_mid=16.519405859443332, - dr_tf_inboard=1.208, - r_tf_inboard_mid=3.5979411851091103, - r_tf_inboard_in=2.9939411851091102, - r_tf_inboard_out=4.20194118510911, - hmax=9.0730900215620327, - denstl=7800, - whtconsh=115651.90127937049, - whttf=19649856.627845347, - whtcas=1034021.9996272125, - tficrn=0.8197580588957678, - tfcryoarea=6381.2092203414386, - whtgw=5909.3507916745702, - tfocrn=0.59553192892551199, - whtconsc=5802.5700395134345, - whtconcu=58744.465423173802, - whtcon=0, - whtconin=0, - vftf=0.30000000000000004, - dcond=np.array( - np.array( - (6080, 6080, 6070, 6080, 6080, 8500, 6070, 8500, 8500), order="F" - ), - order="F", - ).transpose(), - dcondins=1800, - len_tf_coil=50.514015976170839, - dcase=8000, - acndttf=0.0014685061538103825, - n_tf_turn=200, - n_tf_coils=16, - aiwp=0.087880174466980876, - acasetfo=1.2752592893394648, - acasetf=1.0015169239205168, - fcutfsu=0.80884, - awphec=0.015707963267948974, - acstf=0.001293323051622732, - whttflgs=0, - whtcp=0, - whtconal=0, - vol_cond_cp=0, - i_tf_sup=1, - i_tf_sc_mat=5, - a_tf_leg_outboard=1.9805354702921749, - thkcas=0.52465000000000006, - voltfleg=0, - cplen=20.562180043124066, - itart=0, - awpc=0.70527618095271016, - awptf=0.64024601555360383, - vol_ins_cp=0, - vol_gr_ins_cp=0, - vol_case_cp=0, - a_leg_ins=0, - a_leg_gr_ins=0, - a_leg_cond=0, - theta_coil=0.19634954084936207, - tan_theta_coil=0.19891236737965801, - expected_whtconsh=115721.02357090525, - expected_whtcas=1034699.2182961091, - expected_tficrn=0.8197580588957678, - expected_tfcryoarea=6385.0231118485681, - expected_whtgw=5912.8826650262808, - expected_tfocrn=0.59553192892551199, - expected_whtconsc=5806.038092640837, - expected_whtconcu=58779.575542593491, - expected_whtcon=188297.16217276, - expected_whtconin=7990.5249666247555, - expected_cplen=20.562180043124066, - ), - ), -) -def test_tf_coil_area_and_masses(tfcoilareaandmassesparam, monkeypatch, sctfcoil): - """ - Automatically generated Regression Unit Test for tf_coil_area_and_masses. - - This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. - - :param tfcoilareaandmassesparam: the data used to mock and assert in this test. - :type tfcoilareaandmassesparam: tfcoilareaandmassesparam - - :param monkeypatch: pytest fixture used to mock module/class variables - :type monkeypatch: _pytest.monkeypatch.monkeypatch - """ - - monkeypatch.setattr(build_variables, "hr1", tfcoilareaandmassesparam.hr1) - - monkeypatch.setattr( - build_variables, "r_tf_outboard_mid", tfcoilareaandmassesparam.r_tf_outboard_mid - ) - - monkeypatch.setattr( - build_variables, "dr_tf_inboard", tfcoilareaandmassesparam.dr_tf_inboard - ) - - monkeypatch.setattr( - build_variables, "r_tf_inboard_mid", tfcoilareaandmassesparam.r_tf_inboard_mid - ) - - monkeypatch.setattr( - build_variables, "r_tf_inboard_in", tfcoilareaandmassesparam.r_tf_inboard_in - ) - - monkeypatch.setattr( - build_variables, "r_tf_inboard_out", tfcoilareaandmassesparam.r_tf_inboard_out - ) - - monkeypatch.setattr(build_variables, "hmax", tfcoilareaandmassesparam.hmax) - - monkeypatch.setattr(fwbs_variables, "denstl", tfcoilareaandmassesparam.denstl) - - monkeypatch.setattr(tfcoil_variables, "whtconsh", tfcoilareaandmassesparam.whtconsh) - - monkeypatch.setattr(tfcoil_variables, "whttf", tfcoilareaandmassesparam.whttf) - - monkeypatch.setattr(tfcoil_variables, "whtcas", tfcoilareaandmassesparam.whtcas) - - monkeypatch.setattr(tfcoil_variables, "tficrn", tfcoilareaandmassesparam.tficrn) - - monkeypatch.setattr( - tfcoil_variables, "tfcryoarea", tfcoilareaandmassesparam.tfcryoarea - ) - - monkeypatch.setattr(tfcoil_variables, "whtgw", tfcoilareaandmassesparam.whtgw) - - monkeypatch.setattr(tfcoil_variables, "tfocrn", tfcoilareaandmassesparam.tfocrn) - - monkeypatch.setattr(tfcoil_variables, "whtconsc", tfcoilareaandmassesparam.whtconsc) - - monkeypatch.setattr(tfcoil_variables, "whtconcu", tfcoilareaandmassesparam.whtconcu) - - monkeypatch.setattr(tfcoil_variables, "whtcon", tfcoilareaandmassesparam.whtcon) - - monkeypatch.setattr(tfcoil_variables, "whtconin", tfcoilareaandmassesparam.whtconin) - - monkeypatch.setattr( - tfcoil_variables, "tfcryoarea", tfcoilareaandmassesparam.tfcryoarea - ) - - monkeypatch.setattr(tfcoil_variables, "vftf", tfcoilareaandmassesparam.vftf) - - monkeypatch.setattr(tfcoil_variables, "dcond", tfcoilareaandmassesparam.dcond) - - monkeypatch.setattr(tfcoil_variables, "dcondins", tfcoilareaandmassesparam.dcondins) - - monkeypatch.setattr( - tfcoil_variables, "len_tf_coil", tfcoilareaandmassesparam.len_tf_coil - ) - - monkeypatch.setattr(tfcoil_variables, "dcase", tfcoilareaandmassesparam.dcase) - - monkeypatch.setattr(tfcoil_variables, "acndttf", tfcoilareaandmassesparam.acndttf) - - monkeypatch.setattr( - tfcoil_variables, "n_tf_turn", tfcoilareaandmassesparam.n_tf_turn - ) - - monkeypatch.setattr( - tfcoil_variables, "n_tf_coils", tfcoilareaandmassesparam.n_tf_coils - ) - - monkeypatch.setattr(tfcoil_variables, "aiwp", tfcoilareaandmassesparam.aiwp) - - monkeypatch.setattr(tfcoil_variables, "acasetfo", tfcoilareaandmassesparam.acasetfo) - - monkeypatch.setattr(tfcoil_variables, "acasetf", tfcoilareaandmassesparam.acasetf) - - monkeypatch.setattr(tfcoil_variables, "fcutfsu", tfcoilareaandmassesparam.fcutfsu) - - monkeypatch.setattr(tfcoil_variables, "awphec", tfcoilareaandmassesparam.awphec) - - monkeypatch.setattr(tfcoil_variables, "acstf", tfcoilareaandmassesparam.acstf) - - monkeypatch.setattr(tfcoil_variables, "whttflgs", tfcoilareaandmassesparam.whttflgs) - - monkeypatch.setattr(tfcoil_variables, "whtcp", tfcoilareaandmassesparam.whtcp) - - monkeypatch.setattr(tfcoil_variables, "whtconal", tfcoilareaandmassesparam.whtconal) - - monkeypatch.setattr( - tfcoil_variables, "vol_cond_cp", tfcoilareaandmassesparam.vol_cond_cp - ) - - monkeypatch.setattr(tfcoil_variables, "i_tf_sup", tfcoilareaandmassesparam.i_tf_sup) - - monkeypatch.setattr( - tfcoil_variables, "i_tf_sc_mat", tfcoilareaandmassesparam.i_tf_sc_mat - ) - - monkeypatch.setattr( - tfcoil_variables, - "a_tf_leg_outboard", - tfcoilareaandmassesparam.a_tf_leg_outboard, - ) - - monkeypatch.setattr(tfcoil_variables, "thkcas", tfcoilareaandmassesparam.thkcas) - - monkeypatch.setattr(tfcoil_variables, "voltfleg", tfcoilareaandmassesparam.voltfleg) - - monkeypatch.setattr(tfcoil_variables, "cplen", tfcoilareaandmassesparam.cplen) - - monkeypatch.setattr(physics_variables, "itart", tfcoilareaandmassesparam.itart) - - monkeypatch.setattr(sctfcoil_module, "awpc", tfcoilareaandmassesparam.awpc) - - monkeypatch.setattr(sctfcoil_module, "awptf", tfcoilareaandmassesparam.awptf) - - monkeypatch.setattr( - sctfcoil_module, "vol_ins_cp", tfcoilareaandmassesparam.vol_ins_cp - ) - - monkeypatch.setattr( - sctfcoil_module, "vol_gr_ins_cp", tfcoilareaandmassesparam.vol_gr_ins_cp - ) - - monkeypatch.setattr( - sctfcoil_module, "vol_case_cp", tfcoilareaandmassesparam.vol_case_cp - ) - - monkeypatch.setattr( - sctfcoil_module, "a_leg_ins", tfcoilareaandmassesparam.a_leg_ins - ) - - monkeypatch.setattr( - sctfcoil_module, "a_leg_gr_ins", tfcoilareaandmassesparam.a_leg_gr_ins - ) - - monkeypatch.setattr( - sctfcoil_module, "a_leg_cond", tfcoilareaandmassesparam.a_leg_cond - ) - - monkeypatch.setattr( - sctfcoil_module, "theta_coil", tfcoilareaandmassesparam.theta_coil - ) - - monkeypatch.setattr( - sctfcoil_module, "tan_theta_coil", tfcoilareaandmassesparam.tan_theta_coil - ) - - sctfcoil.tf_coil_area_and_masses() - - assert tfcoil_variables.whtconsh == pytest.approx( - tfcoilareaandmassesparam.expected_whtconsh - ) - - assert tfcoil_variables.whtcas == pytest.approx( - tfcoilareaandmassesparam.expected_whtcas - ) - - assert tfcoil_variables.tficrn == pytest.approx( - tfcoilareaandmassesparam.expected_tficrn - ) - - assert tfcoil_variables.tfcryoarea == pytest.approx( - tfcoilareaandmassesparam.expected_tfcryoarea - ) - - assert tfcoil_variables.whtgw == pytest.approx( - tfcoilareaandmassesparam.expected_whtgw - ) - - assert tfcoil_variables.tfocrn == pytest.approx( - tfcoilareaandmassesparam.expected_tfocrn - ) - - assert tfcoil_variables.whtconsc == pytest.approx( - tfcoilareaandmassesparam.expected_whtconsc - ) - - assert tfcoil_variables.whtconcu == pytest.approx( - tfcoilareaandmassesparam.expected_whtconcu - ) - - assert tfcoil_variables.whtcon == pytest.approx( - tfcoilareaandmassesparam.expected_whtcon - ) - - assert tfcoil_variables.whtconin == pytest.approx( - tfcoilareaandmassesparam.expected_whtconin - ) - - assert tfcoil_variables.cplen == pytest.approx( - tfcoilareaandmassesparam.expected_cplen - ) - - -class PeakTfWithRippleParam(NamedTuple): - tf_fit_t: Any = None - - tf_fit_z: Any = None - - tf_fit_y: Any = None - - n_tf_coils: Any = None - - wwp1: Any = None - - dr_tf_wp: Any = None - - tfin: Any = None - - bmaxtf: Any = None - - expected_tf_fit_t: Any = None - - expected_tf_fit_z: Any = None - - expected_tf_fit_y: Any = None - - expected_bmaxtfrp: Any = None - - expected_flag: Any = None - - -@pytest.mark.parametrize( - "peaktfwithrippleparam", - ( - PeakTfWithRippleParam( - tf_fit_t=0, - tf_fit_z=0, - tf_fit_y=0, - n_tf_coils=16, - wwp1=1.299782604942499, - dr_tf_wp=0.50661087836601015, - tfin=3.789896624292115, - bmaxtf=11.717722779177526, - expected_tf_fit_t=0.80807838916035957, - expected_tf_fit_z=0.3149613642807837, - expected_tf_fit_y=1.0658869305062604, - expected_bmaxtfrp=12.48976756562082, - expected_flag=0, - ), - PeakTfWithRippleParam( - tf_fit_t=0.80807838916035957, - tf_fit_z=0.3149613642807837, - tf_fit_y=1.0658869305062604, - n_tf_coils=16, - wwp1=1.299782604942499, - dr_tf_wp=0.50661087836601015, - tfin=3.789896624292115, - bmaxtf=11.717722779177526, - expected_tf_fit_t=0.80807838916035957, - expected_tf_fit_z=0.3149613642807837, - expected_tf_fit_y=1.0658869305062604, - expected_bmaxtfrp=12.48976756562082, - expected_flag=0, - ), - ), -) -def test_peak_tf_with_ripple(peaktfwithrippleparam, monkeypatch, sctfcoil): - """ - Automatically generated Regression Unit Test for peak_tf_with_ripple. - - This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. - - :param peaktfwithrippleparam: the data used to mock and assert in this test. - :type peaktfwithrippleparam: peaktfwithrippleparam - - :param monkeypatch: pytest fixture used to mock module/class variables - :type monkeypatch: _pytest.monkeypatch.monkeypatch - """ - - monkeypatch.setattr(sctfcoil_module, "tf_fit_t", peaktfwithrippleparam.tf_fit_t) - - monkeypatch.setattr(sctfcoil_module, "tf_fit_z", peaktfwithrippleparam.tf_fit_z) - - monkeypatch.setattr(sctfcoil_module, "tf_fit_y", peaktfwithrippleparam.tf_fit_y) - - bmaxtfrp, flag = sctfcoil.peak_tf_with_ripple( - n_tf_coils=peaktfwithrippleparam.n_tf_coils, - wwp1=peaktfwithrippleparam.wwp1, - dr_tf_wp=peaktfwithrippleparam.dr_tf_wp, - tfin=peaktfwithrippleparam.tfin, - bmaxtf=peaktfwithrippleparam.bmaxtf, - ) - - assert sctfcoil_module.tf_fit_t == pytest.approx( - peaktfwithrippleparam.expected_tf_fit_t - ) - - assert sctfcoil_module.tf_fit_z == pytest.approx( - peaktfwithrippleparam.expected_tf_fit_z - ) - - assert sctfcoil_module.tf_fit_y == pytest.approx( - peaktfwithrippleparam.expected_tf_fit_y - ) - - assert bmaxtfrp == pytest.approx(peaktfwithrippleparam.expected_bmaxtfrp) - - assert flag == pytest.approx(peaktfwithrippleparam.expected_flag) - - -class TfWpGeomParam(NamedTuple): - dr_tf_inboard: Any = None - - r_tf_inboard_in: Any = None - - r_tf_inboard_out: Any = None - - dr_tf_wp: Any = None - - casthi: Any = None - - thkcas: Any = None - - casths: Any = None - - wwp1: Any = None - - wwp2: Any = None - - tinstf: Any = None - - tfinsgap: Any = None - - awpc: Any = None - - awptf: Any = None - - r_wp_inner: Any = None - - r_wp_outer: Any = None - - r_wp_centre: Any = None - - t_wp_toroidal: Any = None - - t_wp_toroidal_av: Any = None - - a_ground_ins: Any = None - - theta_coil: Any = None - - tan_theta_coil: Any = None - - i_tf_wp_geom: Any = None - - expected_wwp1: Any = None - - expected_awpc: Any = None - - expected_awptf: Any = None - - expected_r_wp_inner: Any = None - - expected_r_wp_outer: Any = None - - expected_r_wp_centre: Any = None - - expected_t_wp_toroidal: Any = None - - expected_t_wp_toroidal_av: Any = None - - expected_a_ground_ins: Any = None - - -@pytest.mark.parametrize( - "tfwpgeomparam", - ( - TfWpGeomParam( - dr_tf_inboard=1.208, - r_tf_inboard_in=2.9939411851091102, - r_tf_inboard_out=4.20194118510911, - dr_tf_wp=0.54261087836601019, - casthi=0.060000000000000012, - thkcas=0.52465000000000006, - casths=0.05000000000000001, - wwp1=0, - wwp2=0, - tinstf=0.0080000000000000019, - tfinsgap=0.01, - awpc=0, - awptf=0, - r_wp_inner=0, - r_wp_outer=0, - r_wp_centre=0, - t_wp_toroidal=0, - t_wp_toroidal_av=0, - a_ground_ins=0, - theta_coil=0.19634954084936207, - tan_theta_coil=0.19891236737965801, - i_tf_wp_geom=0, - expected_wwp1=1.299782604942499, - expected_awpc=0.70527618095271016, - expected_awptf=0.64024601555360383, - expected_r_wp_inner=3.5185911851091101, - expected_r_wp_outer=4.06120206347512, - expected_r_wp_centre=3.789896624292115, - expected_t_wp_toroidal=1.299782604942499, - expected_t_wp_toroidal_av=1.299782604942499, - expected_a_ground_ins=0.028582295732936136, - ), - TfWpGeomParam( - dr_tf_inboard=1.208, - r_tf_inboard_in=2.9939411851091102, - r_tf_inboard_out=4.20194118510911, - dr_tf_wp=0.54261087836601019, - casthi=0.060000000000000012, - thkcas=0.52465000000000006, - casths=0.05000000000000001, - wwp1=1.299782604942499, - wwp2=0, - tinstf=0.0080000000000000019, - tfinsgap=0.01, - awpc=0.70527618095271016, - awptf=0.64024601555360383, - r_wp_inner=3.5185911851091101, - r_wp_outer=4.06120206347512, - r_wp_centre=3.789896624292115, - t_wp_toroidal=1.299782604942499, - t_wp_toroidal_av=1.299782604942499, - a_ground_ins=0.028582295732936136, - theta_coil=0.19634954084936207, - tan_theta_coil=0.19891236737965801, - i_tf_wp_geom=0, - expected_wwp1=1.299782604942499, - expected_awpc=0.70527618095271016, - expected_awptf=0.64024601555360383, - expected_r_wp_inner=3.5185911851091101, - expected_r_wp_outer=4.06120206347512, - expected_r_wp_centre=3.789896624292115, - expected_t_wp_toroidal=1.299782604942499, - expected_t_wp_toroidal_av=1.299782604942499, - expected_a_ground_ins=0.028582295732936136, - ), - ), -) -def test_tf_wp_geom(tfwpgeomparam, monkeypatch, sctfcoil): - """ - Automatically generated Regression Unit Test for tf_wp_geom. - - This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. - - :param tfwpgeomparam: the data used to mock and assert in this test. - :type tfwpgeomparam: tfwpgeomparam - - :param monkeypatch: pytest fixture used to mock module/class variables - :type monkeypatch: _pytest.monkeypatch.monkeypatch - """ - - monkeypatch.setattr(build_variables, "dr_tf_inboard", tfwpgeomparam.dr_tf_inboard) - - monkeypatch.setattr( - build_variables, "r_tf_inboard_in", tfwpgeomparam.r_tf_inboard_in - ) - - monkeypatch.setattr( - build_variables, "r_tf_inboard_out", tfwpgeomparam.r_tf_inboard_out - ) - - monkeypatch.setattr(tfcoil_variables, "dr_tf_wp", tfwpgeomparam.dr_tf_wp) - - monkeypatch.setattr(tfcoil_variables, "casthi", tfwpgeomparam.casthi) - - monkeypatch.setattr(tfcoil_variables, "thkcas", tfwpgeomparam.thkcas) - - monkeypatch.setattr(tfcoil_variables, "casths", tfwpgeomparam.casths) - - monkeypatch.setattr(tfcoil_variables, "wwp1", tfwpgeomparam.wwp1) - - monkeypatch.setattr(tfcoil_variables, "wwp2", tfwpgeomparam.wwp2) - - monkeypatch.setattr(tfcoil_variables, "tinstf", tfwpgeomparam.tinstf) - - monkeypatch.setattr(tfcoil_variables, "tfinsgap", tfwpgeomparam.tfinsgap) - - monkeypatch.setattr(sctfcoil_module, "awpc", tfwpgeomparam.awpc) - - monkeypatch.setattr(sctfcoil_module, "awptf", tfwpgeomparam.awptf) - - monkeypatch.setattr(sctfcoil_module, "r_wp_inner", tfwpgeomparam.r_wp_inner) - - monkeypatch.setattr(sctfcoil_module, "r_wp_outer", tfwpgeomparam.r_wp_outer) - - monkeypatch.setattr(sctfcoil_module, "r_wp_centre", tfwpgeomparam.r_wp_centre) - - monkeypatch.setattr(sctfcoil_module, "t_wp_toroidal", tfwpgeomparam.t_wp_toroidal) - - monkeypatch.setattr( - sctfcoil_module, "t_wp_toroidal_av", tfwpgeomparam.t_wp_toroidal_av - ) - - monkeypatch.setattr(sctfcoil_module, "a_ground_ins", tfwpgeomparam.a_ground_ins) - - monkeypatch.setattr(sctfcoil_module, "theta_coil", tfwpgeomparam.theta_coil) - - monkeypatch.setattr(sctfcoil_module, "tan_theta_coil", tfwpgeomparam.tan_theta_coil) - - sctfcoil.tf_wp_geom(i_tf_wp_geom=tfwpgeomparam.i_tf_wp_geom) - - assert tfcoil_variables.wwp1 == pytest.approx(tfwpgeomparam.expected_wwp1) - - assert sctfcoil_module.awpc == pytest.approx(tfwpgeomparam.expected_awpc) - - assert sctfcoil_module.awptf == pytest.approx(tfwpgeomparam.expected_awptf) - - assert sctfcoil_module.r_wp_inner == pytest.approx( - tfwpgeomparam.expected_r_wp_inner - ) - - assert sctfcoil_module.r_wp_outer == pytest.approx( - tfwpgeomparam.expected_r_wp_outer - ) - - assert sctfcoil_module.r_wp_centre == pytest.approx( - tfwpgeomparam.expected_r_wp_centre - ) - - assert sctfcoil_module.t_wp_toroidal == pytest.approx( - tfwpgeomparam.expected_t_wp_toroidal - ) - - assert sctfcoil_module.t_wp_toroidal_av == pytest.approx( - tfwpgeomparam.expected_t_wp_toroidal_av - ) - - assert sctfcoil_module.a_ground_ins == pytest.approx( - tfwpgeomparam.expected_a_ground_ins - ) - - -class TfCaseGeomParam(NamedTuple): - acasetf: Any = None - - acasetfo: Any = None - - a_tf_leg_outboard: Any = None - - tfareain: Any = None - - n_tf_coils: Any = None - - casths: Any = None - - casthi: Any = None - - dr_tf_wp: Any = None - - r_tf_inboard_in: Any = None - - r_tf_inboard_out: Any = None - - awpc: Any = None - - r_wp_inner: Any = None - - r_wp_outer: Any = None - - t_lat_case_av: Any = None - - a_case_front: Any = None - - a_case_nose: Any = None - - theta_coil: Any = None - - tan_theta_coil: Any = None - - i_tf_wp_geom: Any = None - - i_tf_case_geom: Any = None - - expected_acasetf: Any = None - - expected_acasetfo: Any = None - - expected_t_lat_case_av: Any = None - - expected_a_case_front: Any = None - - expected_a_case_nose: Any = None - - -@pytest.mark.parametrize( - "tfcasegeomparam", - ( - TfCaseGeomParam( - acasetf=0, - acasetfo=0, - a_tf_leg_outboard=1.9805354702921749, - tfareain=27.308689677971632, - n_tf_coils=16, - casths=0.05000000000000001, - casthi=0.060000000000000012, - dr_tf_wp=0.54261087836601019, - r_tf_inboard_in=2.9939411851091102, - r_tf_inboard_out=4.20194118510911, - awpc=0.70527618095271016, - r_wp_inner=3.5185911851091101, - r_wp_outer=4.06120206347512, - t_lat_case_av=0, - a_case_front=0, - a_case_nose=0, - theta_coil=0.19634954084936207, - tan_theta_coil=0.19891236737965801, - i_tf_wp_geom=0, - i_tf_case_geom=0, - expected_acasetf=1.0015169239205168, - expected_acasetfo=1.2752592893394648, - expected_t_lat_case_av=0.10396600719086938, - expected_a_case_front=0.18607458590131154, - expected_a_case_nose=0.70261616505511615, - ), - TfCaseGeomParam( - acasetf=1.0015169239205168, - acasetfo=1.2752592893394648, - a_tf_leg_outboard=1.9805354702921749, - tfareain=27.308689677971632, - n_tf_coils=16, - casths=0.05000000000000001, - casthi=0.060000000000000012, - dr_tf_wp=0.54261087836601019, - r_tf_inboard_in=2.9939411851091102, - r_tf_inboard_out=4.20194118510911, - awpc=0.70527618095271016, - r_wp_inner=3.5185911851091101, - r_wp_outer=4.06120206347512, - t_lat_case_av=0.10396600719086938, - a_case_front=0.18607458590131154, - a_case_nose=0.70261616505511615, - theta_coil=0.19634954084936207, - tan_theta_coil=0.19891236737965801, - i_tf_wp_geom=0, - i_tf_case_geom=0, - expected_acasetf=1.0015169239205168, - expected_acasetfo=1.2752592893394648, - expected_t_lat_case_av=0.10396600719086938, - expected_a_case_front=0.18607458590131154, - expected_a_case_nose=0.70261616505511615, - ), - ), -) -def test_tf_case_geom(tfcasegeomparam, monkeypatch, sctfcoil): - """ - Automatically generated Regression Unit Test for tf_case_geom. - - This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. - - :param tfcasegeomparam: the data used to mock and assert in this test. - :type tfcasegeomparam: tfcasegeomparam - - :param monkeypatch: pytest fixture used to mock module/class variables - :type monkeypatch: _pytest.monkeypatch.monkeypatch - """ - - monkeypatch.setattr(tfcoil_variables, "acasetf", tfcasegeomparam.acasetf) - - monkeypatch.setattr(tfcoil_variables, "acasetfo", tfcasegeomparam.acasetfo) - - monkeypatch.setattr( - tfcoil_variables, "a_tf_leg_outboard", tfcasegeomparam.a_tf_leg_outboard - ) - - monkeypatch.setattr(tfcoil_variables, "tfareain", tfcasegeomparam.tfareain) - - monkeypatch.setattr(tfcoil_variables, "n_tf_coils", tfcasegeomparam.n_tf_coils) - - monkeypatch.setattr(tfcoil_variables, "casths", tfcasegeomparam.casths) - - monkeypatch.setattr(tfcoil_variables, "casthi", tfcasegeomparam.casthi) - - monkeypatch.setattr(tfcoil_variables, "dr_tf_wp", tfcasegeomparam.dr_tf_wp) - - monkeypatch.setattr( - build_variables, "r_tf_inboard_in", tfcasegeomparam.r_tf_inboard_in - ) - - monkeypatch.setattr( - build_variables, "r_tf_inboard_out", tfcasegeomparam.r_tf_inboard_out - ) - - monkeypatch.setattr(sctfcoil_module, "awpc", tfcasegeomparam.awpc) - - monkeypatch.setattr(sctfcoil_module, "r_wp_inner", tfcasegeomparam.r_wp_inner) - - monkeypatch.setattr(sctfcoil_module, "r_wp_outer", tfcasegeomparam.r_wp_outer) - - monkeypatch.setattr(sctfcoil_module, "t_lat_case_av", tfcasegeomparam.t_lat_case_av) - - monkeypatch.setattr(sctfcoil_module, "a_case_front", tfcasegeomparam.a_case_front) - - monkeypatch.setattr(sctfcoil_module, "a_case_nose", tfcasegeomparam.a_case_nose) - - monkeypatch.setattr(sctfcoil_module, "theta_coil", tfcasegeomparam.theta_coil) - - monkeypatch.setattr( - sctfcoil_module, "tan_theta_coil", tfcasegeomparam.tan_theta_coil - ) - - sctfcoil.tf_case_geom( - i_tf_wp_geom=tfcasegeomparam.i_tf_wp_geom, - i_tf_case_geom=tfcasegeomparam.i_tf_case_geom, - ) - - assert tfcoil_variables.acasetf == pytest.approx(tfcasegeomparam.expected_acasetf) - - assert tfcoil_variables.acasetfo == pytest.approx(tfcasegeomparam.expected_acasetfo) - - assert sctfcoil_module.t_lat_case_av == pytest.approx( - tfcasegeomparam.expected_t_lat_case_av - ) - - assert sctfcoil_module.a_case_front == pytest.approx( - tfcasegeomparam.expected_a_case_front - ) - - assert sctfcoil_module.a_case_nose == pytest.approx( - tfcasegeomparam.expected_a_case_nose - ) - - -class TfIntegerTurnGeomParam(NamedTuple): - dr_tf_wp: Any = None - - tinstf: Any = None - - tfinsgap: Any = None - - t_conductor: Any = None - - t_turn_tf: Any = None - - tfc_current: Any = None - - t_wp_toroidal: Any = None - - t_conductor_radial: Any = None - - t_conductor_toroidal: Any = None - - t_cable_radial: Any = None - - t_cable_toroidal: Any = None - - t_turn_radial: Any = None - - t_turn_toroidal: Any = None - - t_cable: Any = None - - n_layer: Any = None - - n_pancake: Any = None - - thwcndut: Any = None - - thicndut: Any = None - - expected_t_conductor: Any = None - - expected_t_turn_tf: Any = None - - expected_t_conductor_radial: Any = None - - expected_t_conductor_toroidal: Any = None - - expected_t_cable_radial: Any = None - - expected_t_cable_toroidal: Any = None - - expected_t_turn_radial: Any = None - - expected_t_turn_toroidal: Any = None - - expected_t_cable: Any = None - - expected_acstf: Any = None - - expected_acndttf: Any = None - - expected_insulation_area: Any = None - - expected_cpttf: Any = None - - expected_n_tf_turn: Any = None - - -@pytest.mark.parametrize( - "tfintegerturngeomparam", - ( - TfIntegerTurnGeomParam( - dr_tf_wp=0.54261087836601019, - tinstf=0.0080000000000000019, - tfinsgap=0.01, - t_conductor=0, - t_turn_tf=0, - tfc_current=14805350.287500001, - t_wp_toroidal=1.299782604942499, - t_conductor_radial=0, - t_conductor_toroidal=0, - t_cable_radial=0, - t_cable_toroidal=0, - t_turn_radial=0, - t_turn_toroidal=0, - t_cable=0, - n_layer=10, - n_pancake=20, - thwcndut=0.0080000000000000002, - thicndut=0.002, - expected_t_conductor=0.052553108427885735, - expected_t_turn_tf=0.056579413904423038, - expected_t_conductor_radial=0.046661087836601015, - expected_t_conductor_toroidal=0.059189130247124938, - expected_t_cable_radial=0.030661087836601014, - expected_t_cable_toroidal=0.043189130247124938, - expected_t_turn_radial=0.050661087836601018, - expected_t_turn_toroidal=0.063189130247124942, - expected_t_cable=0.036389912284773368, - expected_acstf=0.001293323051622732, - expected_acndttf=0.0014685061538103825, - expected_insulation_area=0.00043940087233490435, - expected_cpttf=74026.751437500003, - expected_n_tf_turn=200, - ), - TfIntegerTurnGeomParam( - dr_tf_wp=0.54261087836601019, - tinstf=0.0080000000000000019, - tfinsgap=0.01, - t_conductor=0.052553108427885735, - t_turn_tf=0.056579413904423038, - tfc_current=14805350.287500001, - t_wp_toroidal=1.299782604942499, - t_conductor_radial=0.046661087836601015, - t_conductor_toroidal=0.059189130247124938, - t_cable_radial=0.030661087836601014, - t_cable_toroidal=0.043189130247124938, - t_turn_radial=0.050661087836601018, - t_turn_toroidal=0.063189130247124942, - t_cable=0.036389912284773368, - n_layer=10, - n_pancake=20, - thwcndut=0.0080000000000000002, - thicndut=0.002, - expected_t_conductor=0.052553108427885735, - expected_t_turn_tf=0.056579413904423038, - expected_t_conductor_radial=0.046661087836601015, - expected_t_conductor_toroidal=0.059189130247124938, - expected_t_cable_radial=0.030661087836601014, - expected_t_cable_toroidal=0.043189130247124938, - expected_t_turn_radial=0.050661087836601018, - expected_t_turn_toroidal=0.063189130247124942, - expected_t_cable=0.036389912284773368, - expected_acstf=0.001293323051622732, - expected_acndttf=0.0014685061538103825, - expected_insulation_area=0.00043940087233490435, - expected_cpttf=74026.751437500003, - expected_n_tf_turn=200, - ), - ), -) -def test_tf_integer_turn_geom(tfintegerturngeomparam, monkeypatch, sctfcoil): - """ - Automatically generated Regression Unit Test for tf_integer_turn_geom. - - This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. - - :param tfintegerturngeomparam: the data used to mock and assert in this test. - :type tfintegerturngeomparam: tfintegerturngeomparam - - :param monkeypatch: pytest fixture used to mock module/class variables - :type monkeypatch: _pytest.monkeypatch.monkeypatch - """ - - monkeypatch.setattr(tfcoil_variables, "dr_tf_wp", tfintegerturngeomparam.dr_tf_wp) - - monkeypatch.setattr(tfcoil_variables, "tinstf", tfintegerturngeomparam.tinstf) - - monkeypatch.setattr(tfcoil_variables, "tfinsgap", tfintegerturngeomparam.tfinsgap) - - monkeypatch.setattr( - tfcoil_variables, "t_conductor", tfintegerturngeomparam.t_conductor - ) - - monkeypatch.setattr(tfcoil_variables, "t_turn_tf", tfintegerturngeomparam.t_turn_tf) - - monkeypatch.setattr( - sctfcoil_module, "tfc_current", tfintegerturngeomparam.tfc_current - ) - - monkeypatch.setattr( - sctfcoil_module, "t_wp_toroidal", tfintegerturngeomparam.t_wp_toroidal - ) - - monkeypatch.setattr( - sctfcoil_module, "t_conductor_radial", tfintegerturngeomparam.t_conductor_radial - ) - - monkeypatch.setattr( - sctfcoil_module, - "t_conductor_toroidal", - tfintegerturngeomparam.t_conductor_toroidal, - ) - - monkeypatch.setattr( - sctfcoil_module, "t_cable_radial", tfintegerturngeomparam.t_cable_radial - ) - - monkeypatch.setattr( - sctfcoil_module, "t_cable_toroidal", tfintegerturngeomparam.t_cable_toroidal - ) - - monkeypatch.setattr( - sctfcoil_module, "t_turn_radial", tfintegerturngeomparam.t_turn_radial - ) - - monkeypatch.setattr( - sctfcoil_module, "t_turn_toroidal", tfintegerturngeomparam.t_turn_toroidal - ) - - monkeypatch.setattr(sctfcoil_module, "t_cable", tfintegerturngeomparam.t_cable) - - ( - acstf, - acndttf, - insulation_area, - cpttf, - n_tf_turn, - ) = sctfcoil.tf_integer_turn_geom( - n_layer=tfintegerturngeomparam.n_layer, - n_pancake=tfintegerturngeomparam.n_pancake, - thwcndut=tfintegerturngeomparam.thwcndut, - thicndut=tfintegerturngeomparam.thicndut, - ) - - assert tfcoil_variables.t_conductor == pytest.approx( - tfintegerturngeomparam.expected_t_conductor - ) - - assert tfcoil_variables.t_turn_tf == pytest.approx( - tfintegerturngeomparam.expected_t_turn_tf - ) - - assert sctfcoil_module.t_conductor_radial == pytest.approx( - tfintegerturngeomparam.expected_t_conductor_radial - ) - - assert sctfcoil_module.t_conductor_toroidal == pytest.approx( - tfintegerturngeomparam.expected_t_conductor_toroidal - ) - - assert sctfcoil_module.t_cable_radial == pytest.approx( - tfintegerturngeomparam.expected_t_cable_radial - ) - - assert sctfcoil_module.t_cable_toroidal == pytest.approx( - tfintegerturngeomparam.expected_t_cable_toroidal - ) - - assert sctfcoil_module.t_turn_radial == pytest.approx( - tfintegerturngeomparam.expected_t_turn_radial - ) - - assert sctfcoil_module.t_turn_toroidal == pytest.approx( - tfintegerturngeomparam.expected_t_turn_toroidal - ) - - assert sctfcoil_module.t_cable == pytest.approx( - tfintegerturngeomparam.expected_t_cable - ) - - assert acstf == pytest.approx(tfintegerturngeomparam.expected_acstf) - - assert acndttf == pytest.approx(tfintegerturngeomparam.expected_acndttf) - - assert insulation_area == pytest.approx( - tfintegerturngeomparam.expected_insulation_area - ) - - assert cpttf == pytest.approx(tfintegerturngeomparam.expected_cpttf) - - assert n_tf_turn == pytest.approx(tfintegerturngeomparam.expected_n_tf_turn) - - -class TfAveragedTurnGeomParam(NamedTuple): - layer_ins: Any = None - - t_conductor: Any = None - - t_turn_tf: Any = None - - t_turn_tf_is_input: Any = None - - cpttf: Any = None - - t_cable_tf: Any = None - - t_cable_tf_is_input: Any = None - - awptf: Any = None - - t_turn_radial: Any = None - - t_turn_toroidal: Any = None - - t_cable: Any = None - - i_tf_sc_mat: Any = None - - jwptf: Any = None - - thwcndut: Any = None - - thicndut: Any = None - - expected_t_conductor: Any = None - - expected_t_turn_tf: Any = None - - expected_t_turn_radial: Any = None - - expected_t_turn_toroidal: Any = None - - expected_t_cable: Any = None - - expected_acstf: Any = None - - expected_acndttf: Any = None - - expected_insulation_area: Any = None - - expected_n_tf_turn: Any = None - - -@pytest.mark.parametrize( - "tfaveragedturngeomparam", - ( - TfAveragedTurnGeomParam( - layer_ins=0, - t_conductor=0, - t_turn_tf=0, - t_turn_tf_is_input=False, - cpttf=65000, - t_cable_tf=0, - t_cable_tf_is_input=False, - awptf=0.60510952642236249, - t_turn_radial=0, - t_turn_toroidal=0, - t_cable=0, - i_tf_sc_mat=5, - jwptf=26493137.688284047, - thwcndut=0.0080000000000000019, - thicndut=0.00080000000000000004, - expected_t_conductor=0.047932469413859431, - expected_t_turn_tf=0.049532469413859428, - expected_t_turn_radial=0.049532469413859428, - expected_t_turn_toroidal=0.049532469413859428, - expected_t_cable=0.031932469413859424, - expected_acstf=0.00098877993839630008, - expected_acndttf=0.0013087416857142699, - expected_insulation_area=0.00015594390212434958, - expected_n_tf_turn=246.63461538461544, - ), - TfAveragedTurnGeomParam( - layer_ins=0, - t_conductor=0.047932469413859431, - t_turn_tf=0.049532469413859428, - t_turn_tf_is_input=False, - cpttf=65000, - t_cable_tf=0, - t_cable_tf_is_input=False, - awptf=0.60510952642236249, - t_turn_radial=0.049532469413859428, - t_turn_toroidal=0.049532469413859428, - t_cable=0.031932469413859424, - i_tf_sc_mat=5, - jwptf=26493137.688284047, - thwcndut=0.0080000000000000019, - thicndut=0.00080000000000000004, - expected_t_conductor=0.047932469413859431, - expected_t_turn_tf=0.049532469413859428, - expected_t_turn_radial=0.049532469413859428, - expected_t_turn_toroidal=0.049532469413859428, - expected_t_cable=0.031932469413859424, - expected_acstf=0.00098877993839630008, - expected_acndttf=0.0013087416857142699, - expected_insulation_area=0.00015594390212434958, - expected_n_tf_turn=246.63461538461544, - ), - TfAveragedTurnGeomParam( - layer_ins=0, - t_conductor=5.712e-02, - t_turn_tf=0.05872, - t_turn_tf_is_input=True, - cpttf=0, - t_cable_tf=0, - t_cable_tf_is_input=False, - awptf=0.60510952642236249, - t_turn_radial=0.05872, - t_turn_toroidal=0.05872, - t_cable=0.04109, - i_tf_sc_mat=1, - jwptf=2.301e07, - thwcndut=8.015e-03, - thicndut=8.0e-4, - expected_t_conductor=5.712e-02, - expected_t_turn_tf=0.05872, - expected_t_turn_radial=0.05872, - expected_t_turn_toroidal=0.05872, - expected_t_cable=0.04109, - expected_acstf=0.001657369442, - expected_acndttf=0.001605324958, - expected_insulation_area=0.000185344, - expected_n_tf_turn=175.49384787, - ), - TfAveragedTurnGeomParam( - layer_ins=0, - t_conductor=0.058296, - t_turn_tf=0, - t_turn_tf_is_input=False, - cpttf=0, - t_cable_tf=0.042, - t_cable_tf_is_input=True, - awptf=0.60510952642236249, - t_turn_radial=0.05872, - t_turn_toroidal=0.05872, - t_cable=0.04109, - i_tf_sc_mat=1, - jwptf=2.673e07, - thwcndut=8.148e-03, - thicndut=8.0e-4, - expected_t_conductor=0.058296, - expected_t_turn_tf=0.059896, - expected_t_turn_radial=0.059896, - expected_t_turn_toroidal=0.059896, - expected_t_cable=0.042, - expected_acstf=0.001731943361, - expected_acndttf=0.001666480255, - expected_insulation_area=0.00018910719999999962, - expected_n_tf_turn=168.6701961481806, - ), - ), -) -def test_tf_averaged_turn_geom(tfaveragedturngeomparam, monkeypatch, sctfcoil): - """ - Automatically generated Regression Unit Test for tf_averaged_turn_geom. - - This test was generated using data from tests/regression/scenarios/i_mode/IN.DAT. - - :param tfaveragedturngeomparam: the data used to mock and assert in this test. - :type tfaveragedturngeomparam: tfaveragedturngeomparam - - :param monkeypatch: pytest fixture used to mock module/class variables - :type monkeypatch: _pytest.monkeypatch.monkeypatch - """ - - monkeypatch.setattr( - tfcoil_variables, "layer_ins", tfaveragedturngeomparam.layer_ins - ) - - monkeypatch.setattr( - tfcoil_variables, "t_conductor", tfaveragedturngeomparam.t_conductor - ) - - monkeypatch.setattr( - tfcoil_variables, "t_turn_tf", tfaveragedturngeomparam.t_turn_tf - ) - - monkeypatch.setattr( - tfcoil_variables, - "t_turn_tf_is_input", - tfaveragedturngeomparam.t_turn_tf_is_input, - ) - - monkeypatch.setattr(tfcoil_variables, "cpttf", tfaveragedturngeomparam.cpttf) - - monkeypatch.setattr( - tfcoil_variables, "t_cable_tf", tfaveragedturngeomparam.t_cable_tf - ) - - monkeypatch.setattr( - tfcoil_variables, - "t_cable_tf_is_input", - tfaveragedturngeomparam.t_cable_tf_is_input, - ) - - monkeypatch.setattr(sctfcoil_module, "awptf", tfaveragedturngeomparam.awptf) - - monkeypatch.setattr( - sctfcoil_module, "t_turn_radial", tfaveragedturngeomparam.t_turn_radial - ) - - monkeypatch.setattr( - sctfcoil_module, "t_turn_toroidal", tfaveragedturngeomparam.t_turn_toroidal - ) - - monkeypatch.setattr(sctfcoil_module, "t_cable", tfaveragedturngeomparam.t_cable) - - acstf, acndttf, insulation_area, n_tf_turn = sctfcoil.tf_averaged_turn_geom( - i_tf_sc_mat=tfaveragedturngeomparam.i_tf_sc_mat, - jwptf=tfaveragedturngeomparam.jwptf, - thwcndut=tfaveragedturngeomparam.thwcndut, - thicndut=tfaveragedturngeomparam.thicndut, - ) - - assert tfcoil_variables.t_conductor == pytest.approx( - tfaveragedturngeomparam.expected_t_conductor - ) - - assert tfcoil_variables.t_turn_tf == pytest.approx( - tfaveragedturngeomparam.expected_t_turn_tf - ) - - assert sctfcoil_module.t_turn_radial == pytest.approx( - tfaveragedturngeomparam.expected_t_turn_radial - ) - - assert sctfcoil_module.t_turn_toroidal == pytest.approx( - tfaveragedturngeomparam.expected_t_turn_toroidal - ) - - assert sctfcoil_module.t_cable == pytest.approx( - tfaveragedturngeomparam.expected_t_cable - ) - - assert acstf == pytest.approx(tfaveragedturngeomparam.expected_acstf) - - assert acndttf == pytest.approx(tfaveragedturngeomparam.expected_acndttf) - - assert insulation_area == pytest.approx( - tfaveragedturngeomparam.expected_insulation_area - ) - - assert n_tf_turn == pytest.approx(tfaveragedturngeomparam.expected_n_tf_turn) - - -class TfWpCurrentsParam(NamedTuple): - c_tf_total: Any = None - - n_tf_coils: Any = None - - jwptf: Any = None - - awptf: Any = None - - expected_jwptf: Any = None - - -@pytest.mark.parametrize( - "tfwpcurrentsparam", - ( - TfWpCurrentsParam( - c_tf_total=256500000.00000003, - n_tf_coils=16, - jwptf=0, - awptf=0.60510952642236249, - expected_jwptf=26493137.688284047, - ), - TfWpCurrentsParam( - c_tf_total=256500000.00000003, - n_tf_coils=16, - jwptf=26493137.688284047, - awptf=0.60510952642236249, - expected_jwptf=26493137.688284047, - ), - ), -) -def test_tf_wp_currents(tfwpcurrentsparam, monkeypatch, sctfcoil): - """ - Automatically generated Regression Unit Test for tf_wp_currents. - - This test was generated using data from tests/regression/scenarios/i_mode/IN.DAT. - - :param tfwpcurrentsparam: the data used to mock and assert in this test. - :type tfwpcurrentsparam: tfwpcurrentsparam - - :param monkeypatch: pytest fixture used to mock module/class variables - :type monkeypatch: _pytest.monkeypatch.monkeypatch - """ - - monkeypatch.setattr(tfcoil_variables, "c_tf_total", tfwpcurrentsparam.c_tf_total) - - monkeypatch.setattr(tfcoil_variables, "n_tf_coils", tfwpcurrentsparam.n_tf_coils) - - monkeypatch.setattr(tfcoil_variables, "jwptf", tfwpcurrentsparam.jwptf) - - monkeypatch.setattr(sctfcoil_module, "awptf", tfwpcurrentsparam.awptf) - - sctfcoil.tf_wp_currents() - - assert tfcoil_variables.jwptf == pytest.approx(tfwpcurrentsparam.expected_jwptf) - - -class StressclParam(NamedTuple): - dr_tf_inboard: Any = None - - r_tf_inboard_mid: Any = None - - dr_bore: Any = None - - dr_cs: Any = None - - i_tf_inside_cs: Any = None - - dr_cs_tf_gap: Any = None - - hmax: Any = None - - r_tf_inboard_in: Any = None - - casestr: Any = None - - n_tf_turn: Any = None - - dr_tf_wp: Any = None - - i_tf_tresca: Any = None - - acstf: Any = None - - vforce: Any = None - - c_tf_total: Any = None - - jwptf: Any = None - - sig_tf_cs_bucked: Any = None - - sig_tf_case: Any = None - - sig_tf_wp: Any = None - - thwcndut: Any = None - - insstrain: Any = None - - tinstf: Any = None - - thicndut: Any = None - - acndttf: Any = None - - tfinsgap: Any = None - - acasetf: Any = None - - sig_tf_case_max: Any = None - - poisson_steel: Any = None - - poisson_copper: Any = None - - poisson_al: Any = None - - n_tf_graded_layers: Any = None - - i_tf_sup: Any = None - - i_tf_bucking: Any = None - - fcoolcp: Any = None - - eyoung_cond_axial: Any = None - - eyoung_steel: Any = None - - eyoung_res_tf_buck: Any = None - - eyoung_ins: Any = None - - eyoung_al: Any = None - - eyoung_copper: Any = None - - aiwp: Any = None - - aswp: Any = None - - cpttf: Any = None - - n_tf_coils: Any = None - - i_tf_stress_model: Any = None - - sig_tf_wp_max: Any = None - - i_tf_turns_integer: Any = None - - casthi: Any = None - - acond: Any = None - - avwp: Any = None - - awphec: Any = None - - poisson_ins: Any = None - - eyoung_cond_trans: Any = None - - poisson_cond_axial: Any = None - - poisson_cond_trans: Any = None - - dhecoil: Any = None - - fcutfsu: Any = None - - str_wp: Any = None - - n_tf_wp_layers: Any = None - - i_pf_conductor: Any = None - - f_a_cs_steel: Any = None - - f_z_cs_tf_internal: Any = None - - j_cs_flat_top_end: Any = None - - j_cs_pulse_start: Any = None - - n_pf_coils_in_group: Any = None - - c_pf_coil_turn_peak_input: Any = None - - awpc: Any = None - - a_tf_steel: Any = None - - a_tf_ins: Any = None - - r_wp_inner: Any = None - - r_wp_outer: Any = None - - t_wp_toroidal: Any = None - - t_wp_toroidal_av: Any = None - - t_lat_case_av: Any = None - - a_case_front: Any = None - - a_case_nose: Any = None - - theta_coil: Any = None - - tan_theta_coil: Any = None - - t_cable_radial: Any = None - - t_cable: Any = None - - vforce_inboard_tot: Any = None - - iprint: Any = None - - outfile: Any = None - - n_radial_array: Any = None - - n_tf_layer: Any = None - - expected_casestr: Any = None - - expected_sig_tf_case: Any = None - - expected_sig_tf_wp: Any = None - - expected_insstrain: Any = None - - expected_str_wp: Any = None - - -@pytest.mark.parametrize( - "stressclparam", - ( - StressclParam( - dr_tf_inboard=1.208, - i_tf_inside_cs=0, - dr_cs_tf_gap=0.01, - r_tf_inboard_mid=3.5979411851091103, - dr_bore=2.3322000000000003, - dr_cs=0.55242000000000002, - hmax=9.0730900215620327, - r_tf_inboard_in=2.9939411851091102, - casestr=0, - n_tf_turn=200, - dr_tf_wp=0.54261087836601019, - i_tf_tresca=0, - acstf=0.001293323051622732, - vforce=250545611.13801825, - c_tf_total=236885604.60000002, - jwptf=23124470.793774806, - sig_tf_cs_bucked=0, - sig_tf_case=0, - sig_tf_wp=0, - thwcndut=0.0080000000000000002, - insstrain=0, - tinstf=0.0080000000000000019, - thicndut=0.002, - acndttf=0.0014685061538103825, - tfinsgap=0.01, - acasetf=1.0015169239205168, - sig_tf_case_max=580000000, - poisson_steel=0.29999999999999999, - poisson_copper=0.34999999999999998, - poisson_al=0.34999999999999998, - n_tf_graded_layers=1, - i_tf_sup=1, - i_tf_bucking=1, - fcoolcp=0.29999999999999999, - eyoung_cond_axial=0, - eyoung_steel=205000000000, - eyoung_res_tf_buck=150000000000, - eyoung_ins=20000000000, - eyoung_al=np.array(69000000000.0), - eyoung_copper=np.array(117000000000.0), - aiwp=0.087880174466980876, - aswp=0.29370123076207649, - cpttf=74026.751437500003, - n_tf_coils=16, - i_tf_stress_model=1, - sig_tf_wp_max=580000000, - i_tf_turns_integer=1, - casthi=0.060000000000000012, - acond=0.1653572639592335, - avwp=0.07759938309736393, - awphec=0.015707963267948974, - poisson_ins=0.34000000000000002, - eyoung_cond_trans=0, - poisson_cond_axial=0.30000001192092896, - poisson_cond_trans=0.30000001192092896, - dhecoil=0.010000000000000002, - fcutfsu=0.80884, - str_wp=0, - n_tf_wp_layers=5, - i_pf_conductor=0, - f_a_cs_steel=0.57874999999999999, - f_z_cs_tf_internal=0.90000000000000002, - j_cs_flat_top_end=20726000, - j_cs_pulse_start=0, - n_pf_coils_in_group=np.array( - np.array((1, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0), order="F"), order="F" - ).transpose(), - c_pf_coil_turn_peak_input=np.array( - np.array( - ( - 42200, - 42200, - 42200, - 42200, - 43000, - 43000, - 43000, - 43000, - 40000, - 40000, - 40000, - 40000, - 40000, - 40000, - 40000, - 40000, - 40000, - 40000, - 40000, - 40000, - 40000, - 40000, - ), - order="F", - ), - order="F", - ).transpose(), - awpc=0.70527618095271016, - a_tf_steel=1.2952181546825934, - a_tf_ins=0.11646247019991701, - r_wp_inner=3.5185911851091101, - r_wp_outer=4.06120206347512, - t_wp_toroidal=1.299782604942499, - t_wp_toroidal_av=1.299782604942499, - t_lat_case_av=0.10396600719086938, - a_case_front=0.18607458590131154, - a_case_nose=0.70261616505511615, - theta_coil=0.19634954084936207, - tan_theta_coil=0.19891236737965801, - t_cable_radial=0.030661087836601014, - t_cable=0.036389912284773368, - vforce_inboard_tot=4008729778.208292, - iprint=0, - outfile=11, - n_radial_array=100, - n_tf_layer=3, - expected_casestr=0.00094360452596334093, - expected_sig_tf_case=543381805.25001633, - expected_sig_tf_wp=397005702.35272157, - expected_insstrain=-0.006687152422925652, - expected_str_wp=0.0015619754370069119, - ), - StressclParam( - dr_tf_inboard=1.208, - i_tf_inside_cs=0, - dr_cs_tf_gap=0.01, - r_tf_inboard_mid=3.5979411851091103, - dr_bore=2.3322000000000003, - dr_cs=0.55242000000000002, - hmax=9.0730900215620327, - r_tf_inboard_in=2.9939411851091102, - casestr=0.00094360452596334093, - n_tf_turn=200, - dr_tf_wp=0.54261087836601019, - i_tf_tresca=0, - acstf=0.001293323051622732, - vforce=250545611.13801825, - c_tf_total=236885604.60000002, - jwptf=23124470.793774806, - sig_tf_cs_bucked=0, - sig_tf_case=543381805.25001633, - sig_tf_wp=397005702.35272157, - thwcndut=0.0080000000000000002, - insstrain=0, - tinstf=0.0080000000000000019, - thicndut=0.002, - acndttf=0.0014685061538103825, - tfinsgap=0.01, - acasetf=1.0015169239205168, - sig_tf_case_max=580000000, - poisson_steel=0.29999999999999999, - poisson_copper=0.34999999999999998, - poisson_al=0.34999999999999998, - n_tf_graded_layers=1, - i_tf_sup=1, - i_tf_bucking=1, - fcoolcp=0.29999999999999999, - eyoung_cond_axial=0, - eyoung_steel=205000000000, - eyoung_res_tf_buck=150000000000, - eyoung_ins=20000000000, - eyoung_al=np.array(69000000000.0), - eyoung_copper=np.array(117000000000.0), - aiwp=0.087880174466980876, - aswp=0.29370123076207649, - cpttf=74026.751437500003, - n_tf_coils=16, - i_tf_stress_model=1, - sig_tf_wp_max=580000000, - i_tf_turns_integer=1, - casthi=0.060000000000000012, - acond=0.1653572639592335, - avwp=0.07759938309736393, - awphec=0.015707963267948974, - poisson_ins=0.34000000000000002, - eyoung_cond_trans=0, - poisson_cond_axial=0.30000001192092896, - poisson_cond_trans=0.30000001192092896, - dhecoil=0.010000000000000002, - fcutfsu=0.80884, - str_wp=0.0015619754370069119, - n_tf_wp_layers=5, - i_pf_conductor=0, - f_a_cs_steel=0.57874999999999999, - f_z_cs_tf_internal=0.90000000000000002, - j_cs_flat_top_end=20726000, - j_cs_pulse_start=19311657.760000002, - n_pf_coils_in_group=np.array( - np.array((1, 1, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0), order="F"), order="F" - ).transpose(), - c_pf_coil_turn_peak_input=np.array( - np.array( - ( - 42200, - 42200, - 42200, - 42200, - 43000, - 43000, - 43000, - 43000, - 40000, - 40000, - 40000, - 40000, - 40000, - 40000, - 40000, - 40000, - 40000, - 40000, - 40000, - 40000, - 40000, - 40000, - ), - order="F", - ), - order="F", - ).transpose(), - awpc=0.70527618095271016, - a_tf_steel=1.2952181546825934, - a_tf_ins=0.11646247019991701, - r_wp_inner=3.5185911851091101, - r_wp_outer=4.06120206347512, - t_wp_toroidal=1.299782604942499, - t_wp_toroidal_av=1.299782604942499, - t_lat_case_av=0.10396600719086938, - a_case_front=0.18607458590131154, - a_case_nose=0.70261616505511615, - theta_coil=0.19634954084936207, - tan_theta_coil=0.19891236737965801, - t_cable_radial=0.030661087836601014, - t_cable=0.036389912284773368, - vforce_inboard_tot=4008729778.208292, - iprint=0, - outfile=11, - n_radial_array=100, - n_tf_layer=3, - expected_casestr=0.00094360452596334093, - expected_sig_tf_case=543381805.25001633, - expected_sig_tf_wp=397005702.35272157, - expected_insstrain=-0.006687152422925652, - expected_str_wp=0.0015619754370069119, - ), - ), -) -def test_stresscl(stressclparam, monkeypatch, sctfcoil): - """ - Automatically generated Regression Unit Test for stresscl. - - This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. - - :param stressclparam: the data used to mock and assert in this test. - :type stressclparam: stressclparam - - :param monkeypatch: pytest fixture used to mock module/class variables - :type monkeypatch: _pytest.monkeypatch.monkeypatch - """ - - ( - _, - _, - _, - _, - _, - _, - _, - _, - _, - _, - _, - _, - _, - _, - _, - _, - _, - _, - _, - _, - _, - _, - _, - _, - _, - _, - _, - sig_tf_wp, - sig_tf_case, - _, - str_wp, - casestr, - insstrain, - _, - ) = sctfcoil.stresscl( - stressclparam.n_tf_layer, - stressclparam.n_radial_array, - stressclparam.n_tf_wp_layers, - stressclparam.i_tf_bucking, - stressclparam.r_tf_inboard_in, - stressclparam.dr_bore, - stressclparam.hmax, - stressclparam.f_z_cs_tf_internal, - stressclparam.dr_cs, - stressclparam.i_tf_inside_cs, - stressclparam.dr_tf_inboard, - stressclparam.dr_cs_tf_gap, - stressclparam.i_pf_conductor, - stressclparam.j_cs_flat_top_end, - stressclparam.j_cs_pulse_start, - stressclparam.c_pf_coil_turn_peak_input, - stressclparam.n_pf_coils_in_group, - 70 / 22, - 3e-3, - stressclparam.f_a_cs_steel, - stressclparam.eyoung_steel, - stressclparam.poisson_steel, - stressclparam.eyoung_cond_axial, - stressclparam.poisson_cond_axial, - stressclparam.eyoung_cond_trans, - stressclparam.poisson_cond_trans, - stressclparam.eyoung_ins, - stressclparam.poisson_ins, - stressclparam.thicndut, - stressclparam.eyoung_copper, - stressclparam.poisson_copper, - stressclparam.i_tf_sup, - stressclparam.eyoung_res_tf_buck, - stressclparam.r_wp_inner, - stressclparam.tan_theta_coil, - stressclparam.theta_coil, - stressclparam.r_wp_outer, - stressclparam.a_tf_steel, - stressclparam.a_case_front, - stressclparam.a_case_nose, - stressclparam.tfinsgap, - stressclparam.tinstf, - stressclparam.n_tf_turn, - stressclparam.i_tf_turns_integer, - stressclparam.t_cable, - stressclparam.t_cable_radial, - stressclparam.dhecoil, - stressclparam.fcutfsu, - stressclparam.thwcndut, - stressclparam.t_lat_case_av, - stressclparam.t_wp_toroidal_av, - stressclparam.a_tf_ins, - stressclparam.aswp, - stressclparam.acond, - stressclparam.awpc, - stressclparam.eyoung_al, - stressclparam.poisson_al, - stressclparam.fcoolcp, - stressclparam.n_tf_graded_layers, - stressclparam.c_tf_total, - stressclparam.casthi, - stressclparam.i_tf_stress_model, - stressclparam.vforce_inboard_tot, - stressclparam.i_tf_tresca, - stressclparam.acasetf, - stressclparam.vforce, - stressclparam.acndttf, - ) - - assert casestr == pytest.approx(stressclparam.expected_casestr, rel=0.01) - - assert sig_tf_case == pytest.approx(stressclparam.expected_sig_tf_case, rel=0.01) - - assert sig_tf_wp == pytest.approx(stressclparam.expected_sig_tf_wp, rel=0.01) - - assert insstrain == pytest.approx(stressclparam.expected_insstrain, rel=0.01) - - assert str_wp == pytest.approx(stressclparam.expected_str_wp, rel=0.01) - - -class PlaneStressParam(NamedTuple): - linesolv: Any = None - - n_radial_array: Any = None - - nlayers: Any = None - - nu: Any = None - - rad: Any = None - - ey: Any = None - - j: Any = None - - expected_sigr: Any = None - - expected_sigt: Any = None - - expected_r_deflect: Any = None - - expected_rradius: Any = None - - -@pytest.mark.parametrize( - "planestressparam", - ( - PlaneStressParam( - n_radial_array=100, - nlayers=3, - nu=np.array( - np.array( - (0.29999999999999999, 0.30904421667064924, 0.29999999999999999), - order="F", - ), - order="F", - ).transpose(), - rad=np.array( - np.array( - ( - 2.9939411851091102, - 3.5414797139565706, - 4.0876202904571599, - 4.1476202904571595, - ), - order="F", - ), - order="F", - ).transpose(), - ey=np.array( - np.array((205000000000, 43126670035.025253, 205000000000), order="F"), - order="F", - ).transpose(), - j=np.array( - np.array((0, 18097185.781970859, 0), order="F"), order="F" - ).transpose(), - expected_sigr=[ - 0.0, - -638231.855766482, - -1272978.0149700674, - -1904263.8142719064, - -2532114.3605434406, - -3156554.533362813, - -3777608.9874793817, - -4395302.155247777, - -5009658.2490304215, - -5620701.263570679, - -6228454.97833536, - -6832942.959828614, - -7434188.563876412, - -8032214.937882571, - -8627045.023056716, - -9218701.556614282, - -9807207.07394974, - -10392583.910782054, - -10974854.205274347, - -11554039.900126804, - -12130162.744643984, - -12703244.296776803, - -13273305.925139055, - -13840368.810999524, - -14404453.950249525, - -14965582.155346265, - -15523774.05723289, - -16079050.107234456, - -16631430.578931632, - -17180935.57001094, - -17727585.00409306, - -18271398.632538624, - -18812396.036232498, - -19350596.627346307, - -19886019.651079975, - -20418684.18738201, - -20948609.1526495, - -21475813.301407494, - -22000315.22796835, - -22522133.368071266, - -23041286.00050247, - -23557791.24869583, - -24071667.082314458, - -24582931.31881398, - -25091601.624986775, - -25597695.518488254, - -26101230.36934514, - -26602223.401445802, - -27100691.6940132, - -27596652.183060553, - -28090121.6628299, - -28581116.78721381, - -29069654.071160544, - -29555749.892062735, - -30039420.491130088, - -30520681.974745892, - -30999550.31580798, - -31476041.355053987, - -31950170.802371547, - -32421954.23809305, - -32891407.114275593, - -33358544.755966377, - -33823382.3624533, - -34285935.00850141, - -34746217.64557501, - -35204245.10304597, - -35660032.08938813, - -36113593.19335821, - -36564942.88516308, - -37014095.51761406, - -37461065.327267796, - -37905866.4355546, - -38348512.84989362, - -38789018.46479585, - -39227397.06295431, - -39663662.31632225, - -40097827.78717919, - -40529906.92918485, - -40959913.08842154, - -41387859.50442459, - -41813759.31120163, - -42237625.5382402, - -42659471.11150431, - -43079308.85441998, - -43497151.48884965, - -43913011.6360561, - -44326901.817655385, - -44738834.45655951, - -45148821.87790862, - -45556876.30999292, - -45963009.88516445, - -46367234.64073896, - -46769562.51988788, - -47170005.372520424, - -47568574.956156105, - -47965282.936788, - -48360140.88973615, - -48753160.30049217, - -49144352.56555428, - -49533728.99325363, - -49921300.804571584, - -49954359.75079646, - -49974994.421951994, - -49983237.62599215, - -49979121.95724278, - -49962679.79819553, - -49933943.321276575, - -49892944.49059213, - -49839715.063674085, - -49774286.59318251, - -49696690.42861684, - -49606957.71798797, - -49505119.40949372, - -49391206.253157824, - -49265248.802470304, - -49127277.41599873, - -48977322.25898817, - -48815413.304947995, - -48641580.33721941, - -48455852.950529814, - -48258260.55252331, - -48048832.365294494, - -47827597.42688793, - -47594584.59279222, - -47349822.53742223, - -47093339.75557484, - -46825164.56388664, - -46545325.10226525, - -46253849.335310586, - -45950765.05372232, - -45636099.875691466, - -45309881.24828925, - -44972136.44881964, - -44622892.586189084, - -44262176.60223319, - -43890015.27305536, - -43506435.21033433, - -43111462.86262834, - -42705124.516666815, - -42287446.29862875, - -41858454.17540391, - -41418173.95584675, - -40966631.29202375, - -40503851.68043686, - -40029860.46324105, - -39544682.82945457, - -39048343.816151306, - -38540868.309644595, - -38022281.04666105, - -37492606.61550055, - -36951869.45718825, - -36400093.8666107, - -35837303.99364805, - -35263523.8442963, - -34678777.28176432, - -34083088.027581, - -33476479.662681654, - -32858975.628480688, - -32230599.22794289, - -31591373.626639504, - -30941321.853795685, - -30280466.803329386, - -29608831.23487964, - -28926437.774824906, - -28233308.91729218, - -27529467.025158796, - -26814934.33104299, - -26089732.938284557, - -25353884.821918193, - -24607411.829640254, - -23850335.682763584, - -23082677.977158453, - -22304460.184201445, - -21515703.651693877, - -20716429.60478688, - -19906659.14690248, - -19086413.26062489, - -18255712.808604427, - -17414578.534444164, - -16563031.063583972, - -15701090.904165361, - -14828778.44790294, - -13946113.970938675, - -13053117.634692835, - -12149809.48670104, - -11236209.461454624, - -10312337.381225117, - -9378212.956880113, - -8433855.788701806, - -7479285.367186312, - -6514521.073844995, - -5539582.181995865, - -4554487.85754239, - -3559257.1597569585, - -2553909.0420464603, - -1538462.3527192033, - -512935.83573593997, - 522651.86853248376, - 1568282.2225666705, - 2623936.7909615375, - 3689597.239691208, - 3651895.231238659, - 3614209.818926577, - 3576540.9930157103, - 3538888.743774036, - 3501253.061476492, - 3463633.9364053686, - 3426031.358849868, - 3388445.3191064713, - 3350875.807478765, - 3313322.8142772503, - 3275786.3298197784, - 3238266.3444311135, - 3200762.8484432492, - 3163275.832195042, - 3125805.2860326516, - 3088351.200309296, - 3050913.5653850324, - 3013492.3716272456, - 2976087.6094101095, - 2938699.2691151258, - 2901327.3411305123, - 2863971.8158518155, - 2826632.683681444, - 2789309.93502884, - 2752003.560310531, - 2714713.5499499296, - 2677439.8943775576, - 2640182.5840308485, - 2602941.609354294, - 2565716.9607993956, - 2528508.6288244454, - 2491316.6038949895, - 2454140.8764832406, - 2416981.4370685695, - 2379838.2761372332, - 2342711.3841824015, - 2305600.7517043273, - 2268506.36920998, - 2231428.2272134367, - 2194366.316235539, - 2157320.6268042377, - 2120291.149454175, - 2083277.8747270512, - 2046280.7931714796, - 2009299.8953427651, - 1972335.1718033939, - 1935386.6131224218, - 1898454.2098760372, - 1861537.9526470467, - 1824637.8320254136, - 1787753.838607672, - 1750885.962997364, - 1714034.1958049217, - 1677198.5276473942, - 1640378.949148915, - 1603575.450940211, - 1566788.0236590446, - 1530016.6579497962, - 1493261.3444638075, - 1456522.0738591857, - 1419798.8368006812, - 1383091.6239601525, - 1346400.4260158325, - 1309725.23365311, - 1273066.0375638467, - 1236422.8284469144, - 1199795.5970078032, - 1163184.33395872, - 1126589.0300187848, - 1090009.6759136862, - 1053446.262375977, - 1016898.7801448042, - 980367.2199661784, - 943851.5725928267, - 907351.8287839973, - 870867.979305924, - 834400.0149312643, - 797947.9264396608, - 761511.704617082, - 725091.3402565308, - 688686.8241575557, - 652298.1471262753, - 615925.2999756474, - 579568.2735251015, - 543227.0586009554, - 506901.646035926, - 470592.02666951995, - 434298.1913479606, - 398020.1309238218, - 361757.8362566382, - 325511.29821222165, - 289280.5076633691, - 253065.4554891793, - 216866.13257544299, - 180682.52981469216, - 144514.63810580914, - 108362.44835451504, - 72225.95147280802, - 36105.13837954948, - ], - expected_sigt=[ - -349942877.4275314, - -349304645.57176495, - -348669899.41256136, - -348038613.61325955, - -347410763.066988, - -346786322.8941686, - -346165268.44005203, - -345547575.2722837, - -344933219.178501, - -344322176.1639607, - -343714422.44919604, - -343109934.4677028, - -342508688.863655, - -341910662.4896488, - -341315832.4044747, - -340724175.87091714, - -340135670.35358167, - -339550293.5167493, - -338968023.222257, - -338388837.5274046, - -337812714.68288743, - -337239633.1307546, - -336669571.5023923, - -336102508.61653185, - -335538423.4772818, - -334977295.2721851, - -334419103.37029845, - -333863827.3202969, - -333311446.84859973, - -332761941.85752034, - -332215292.42343825, - -331671478.7949927, - -331130481.39129883, - -330592280.800185, - -330056857.7764513, - -329524193.2401493, - -328994268.2748818, - -328467064.1261238, - -327942562.1995629, - -327420744.05946004, - -326901591.42702883, - -326385086.17883545, - -325871210.3452168, - -325359946.10871726, - -324851275.8025445, - -324345181.90904295, - -323841647.0581861, - -323340654.02608544, - -322842185.73351806, - -322346225.24447066, - -321852755.7647013, - -321361760.64031744, - -320873223.3563707, - -320387127.5354685, - -319903456.9364011, - -319422195.4527854, - -318943327.11172324, - -318466836.0724772, - -317992706.6251597, - -317520923.18943816, - -317051470.3132556, - -316584332.6715648, - -316119495.0650779, - -315656942.4190298, - -315196659.7819562, - -314738632.32448524, - -314282845.33814305, - -313829284.23417294, - -313377934.54236805, - -312928781.9099172, - -312481812.10026336, - -312037010.99197656, - -311594364.57763755, - -311153858.9627353, - -310715480.3645769, - -310279215.1112089, - -309845049.640352, - -309412970.4983463, - -308982964.3391096, - -308555017.92310655, - -308129118.11632955, - -307705251.8892909, - -307283406.3160268, - -306863568.5731112, - -306445725.9386815, - -306029865.791475, - -305615975.60987574, - -305204042.97097164, - -304794055.5496225, - -304386001.1175382, - -303979867.5423667, - -303575642.78679216, - -303173314.9076432, - -302772872.0550107, - -302374302.471375, - -301977594.4907431, - -301582736.53779495, - -301189717.12703896, - -300798524.8619768, - -300409148.4342775, - -75393985.39087032, - -75352895.95330013, - -75308186.27146304, - -75259848.14709243, - -75207873.48230305, - -75152254.27849749, - -75092982.635284, - -75030050.74940914, - -74963450.91370073, - -74893175.51602733, - -74819217.03826267, - -74741568.05526796, - -74660221.23388477, - -74575169.33193818, - -74486405.1972532, - -74393921.76667804, - -74297712.06512664, - -74197769.20462456, - -74094086.38337062, - -73986656.88480622, - -73875474.07669795, - -73760531.41022801, - -73641822.41909933, - -73519340.71864453, - -73393080.00495018, - -73263034.05399033, - -73129196.72076792, - -72991561.93846779, - -72850123.71761769, - -72704876.14526016, - -72555813.3841336, - -72402929.67186344, - -72246219.32015613, - -72085676.71401668, - -71921296.31095727, - -71753072.64022708, - -71581000.30204895, - -71405073.9668568, - -71225288.37455449, - -71041638.33377115, - -70854118.72113262, - -70662724.48053451, - -70467450.62243325, - -70268292.22313243, - -70065244.4240877, - -69858302.43121327, - -69647461.51419875, - -69432717.00583516, - -69214064.30134167, - -68991498.85770856, - -68765016.19304176, - -68534611.8859144, - 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3.8117909451102676, - 3.8123909451102675, - 3.8129909451102675, - 3.8135909451102674, - 3.8141909451102674, - 3.8147909451102673, - 3.8153909451102677, - 3.8159909451102676, - 3.8165909451102675, - 3.8171909451102675, - 3.8177909451102674, - 3.8183909451102673, - 3.8189909451102673, - 3.8195909451102676, - 3.8201909451102676, - 3.8207909451102675, - 3.8213909451102674, - 3.8219909451102674, - 3.8225909451102673, - 3.8231909451102677, - 3.8237909451102676, - 3.8243909451102676, - ], - ), - ), -) -def test_plane_stress(planestressparam, monkeypatch, sctfcoil): - """ - Automatically generated Regression Unit Test for plane_stress. - - This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. - - :param planestressparam: the data used to mock and assert in this test. - :type planestressparam: planestressparam - - :param monkeypatch: pytest fixture used to mock module/class variables - :type monkeypatch: _pytest.monkeypatch.monkeypatch - """ - - sigr, sigt, r_deflect, rradius = sctf.plane_stress( - n_radial_array=planestressparam.n_radial_array, - nlayers=planestressparam.nlayers, - nu=planestressparam.nu, - rad=planestressparam.rad, - ey=planestressparam.ey, - j=planestressparam.j, - ) - - assert sigr == pytest.approx(planestressparam.expected_sigr) - assert sigt == pytest.approx(planestressparam.expected_sigt) - assert r_deflect == pytest.approx(planestressparam.expected_r_deflect) - assert rradius == pytest.approx(planestressparam.expected_rradius) - - -class ExtendedPlaneStrainParam(NamedTuple): - n_radial_array: Any = None - - nlayers: Any = None - - i_tf_bucking: Any = None - - nu_t: Any = None - - nu_zt: Any = None - - ey_t: Any = None - - ey_z: Any = None - - d_curr: Any = None - - rad: Any = None - - v_force: Any = None - - expected_sigr: Any = None - - expected_sigt: Any = None - - expected_sigz: Any = None - - expected_str_r: Any = None - - expected_str_t: Any = None - - expected_str_z: Any = None - - expected_r_deflect: Any = None - - expected_rradius: Any = None - - -@pytest.mark.parametrize( - "extendedplanestrainparam", - ( - ExtendedPlaneStrainParam( - n_radial_array=100, - nlayers=2, - i_tf_bucking=0, - nu_t=np.array( - np.array((0.34999999999999998, 0.29999999999999999), order="F"), - order="F", - ).transpose(), - nu_zt=np.array( - np.array((0.34948024015688117, 0.29999999999999999), order="F"), - order="F", - ).transpose(), - ey_t=np.array( - np.array((117000000000, 205000000000), order="F"), order="F" - ).transpose(), - ey_z=np.array( - np.array((97843910970.178864, 205000000000.00003), order="F"), - order="F", - ).transpose(), - d_curr=np.array( - np.array((375174117.44492483, 0), order="F"), order="F" - ).transpose(), - rad=np.array( - np.array((0, 0.14708850000000001, 0.15483000000000002), order="F"), - order="F", - ).transpose(), - v_force=147221629.66130698, - expected_sigr=[ - np.nan, - -829140939.1329328, - -828878786.3701528, - -828441865.0984682, - -827830175.3181431, - -827043717.0291449, 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2564904.455468096, - 2306656.041387208, - 2048800.4005258176, - 1791336.7367895895, - 1534264.2560995868, - 1277582.1663874835, - 1021289.6775879061, - 765386.0016326876, - 509870.35244656017, - 254741.94593901612, - -4.787169592359073e-07, - ], - expected_sigt=[ - np.nan, - -829161394.3774397, - -828960607.3461856, - -828625962.2944794, - -828157459.2220584, - -827555098.1289548, - -826818879.0151731, - -825948801.8807079, - -824944866.7255567, - -823807073.5497193, - -822535422.3531967, - -821129913.1359856, - -819590545.89809, - -817917320.6395062, - -816110237.3602372, - -814169296.0602809, - -812094496.7396379, - -809885839.3983089, - -807543324.0362918, - -805066950.6535902, - -802456719.2502012, - -799712629.8261249, - -796834682.3813626, - -793822876.9159135, - -790677213.4297779, - -787397691.9229555, - -783984312.3954465, - -780437074.8472508, - -776755979.2783681, - -772941025.6887994, - -768992214.0785445, - -764909544.4476022, - -760693016.7959738, - -756342631.1236581, - 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4.035727140761491, - 4.036333201367551, - 4.036939261973612, - 4.037545322579673, - 4.038151383185733, - 4.038757443791794, - 4.039363504397854, - 4.039969565003915, - 4.040575625609976, - 4.041181686216036, - 4.041787746822097, - 4.042393807428158, - 4.042999868034218, - 4.043605928640279, - 4.044211989246339, - 4.0448180498524, - 4.045424110458461, - 4.046030171064521, - 4.046636231670582, - 4.047242292276642, - 4.047848352882703, - 4.048454413488764, - 4.049060474094824, - 4.049666534700885, - 4.050272595306946, - 4.050878655913006, - 4.051484716519067, - 4.052090777125127, - 4.052696837731188, - 4.0533028983372485, - 4.053908958943309, - 4.0545150195493695, - 4.05512108015543, - 4.0557271407614905, - 4.0563332013675515, - 4.0569392619736115, - 4.0575453225796725, - 4.058151383185733, - 4.0587574437917935, - 4.059363504397854, - 4.0599695650039145, - 4.060575625609975, - 4.061181686216036, - 4.061787746822096, - 4.062393807428157, - 4.062999868034218, - 4.063605928640278, - 4.064211989246339, - 4.064818049852399, - 4.06542411045846, - 4.066030171064521, - 4.066636231670581, - 4.067242292276642, - 4.067848352882702, - 4.068454413488763, - 4.069060474094824, - 4.069666534700884, - 4.070272595306945, - 4.070878655913006, - 4.071484716519066, - 4.072090777125127, - 4.072696837731187, - 4.073302898337248, - 4.073908958943309, - 4.074515019549369, - 4.07512108015543, - 4.07572714076149, - 4.076333201367551, - 4.076939261973612, - 4.077545322579672, - 4.078151383185733, - 4.078757443791794, - 4.079363504397854, - 4.079969565003915, - 4.080575625609975, - 4.081181686216036, - 4.081787746822097, - 4.082393807428157, - 4.082999868034218, - 4.083605928640278, - 4.084211989246339, - 4.0848180498524, - 4.08542411045846, - 4.086030171064521, - 4.086636231670582, - 4.087242292276642, - 4.087848352882703, - 4.088454413488763, - 4.089060474094824, - ], - ), - ), -) -def test_extended_plane_strain(extendedplanestrainparam, monkeypatch, sctfcoil): - """ - Automatically generated Regression Unit Test for extended_plane_strain. - - This test was generated using data from tests/regression/scenarios/FNSF/IN.DAT. - - :param extendedplanestrainparam: the data used to mock and assert in this test. - :type extendedplanestrainparam: extendedplanestrainparam - - :param monkeypatch: pytest fixture used to mock module/class variables - :type monkeypatch: _pytest.monkeypatch.monkeypatch - """ - - ( - rradius, - sigr, - sigt, - sigz, - str_r, - str_t, - str_z, - r_deflect, - ) = sctf.extended_plane_strain( - n_radial_array=extendedplanestrainparam.n_radial_array, - nlayers=extendedplanestrainparam.nlayers, - i_tf_bucking=extendedplanestrainparam.i_tf_bucking, - nu_t=extendedplanestrainparam.nu_t, - nu_zt=extendedplanestrainparam.nu_zt, - ey_t=extendedplanestrainparam.ey_t, - ey_z=extendedplanestrainparam.ey_z, - d_curr=extendedplanestrainparam.d_curr, - rad=extendedplanestrainparam.rad, - v_force=extendedplanestrainparam.v_force, - ) - - # assert sigr == pytest.approx(extendedplanestrainparam.expected_sigr, rel=0.01) - - np.testing.assert_array_almost_equal( - sigr, np.array(extendedplanestrainparam.expected_sigr), decimal=3 - ) - - np.testing.assert_array_almost_equal( - sigt, np.array(extendedplanestrainparam.expected_sigt), decimal=3 - ) - - np.testing.assert_array_almost_equal( - sigz, np.array(extendedplanestrainparam.expected_sigz), decimal=3 - ) - - np.testing.assert_array_almost_equal( - str_r, np.array(extendedplanestrainparam.expected_str_r) - ) - - np.testing.assert_array_almost_equal( - str_t, np.array(extendedplanestrainparam.expected_str_t) - ) - - np.testing.assert_array_almost_equal( - str_z, np.array(extendedplanestrainparam.expected_str_z) - ) - - np.testing.assert_array_almost_equal( - r_deflect, np.array(extendedplanestrainparam.expected_r_deflect) - ) - - np.testing.assert_array_almost_equal( - rradius, np.array(extendedplanestrainparam.expected_rradius) - ) - - -class EyoungParallelParam(NamedTuple): - eyoung_j_1: Any = None - - eyoung_j_2: Any = None - - a_1: Any = None - - a_2: Any = None - - poisson_j_perp_1: Any = None - - poisson_j_perp_2: Any = None - - expected_eyoung_j_3: Any = None - - expected_a_3: Any = None - - expected_poisson_j_perp_3: Any = None - - -@pytest.mark.parametrize( - "eyoungparallelparam", - ( - EyoungParallelParam( - eyoung_j_1=0, - eyoung_j_2=0, - a_1=0.010000000000000002, - a_2=0, - poisson_j_perp_1=0.30000001192092896, - poisson_j_perp_2=0, - expected_eyoung_j_3=0, - expected_a_3=0.010000000000000002, - expected_poisson_j_perp_3=0.30000001192092896, - ), - EyoungParallelParam( - eyoung_j_1=0, - eyoung_j_2=0, - a_1=0.020661087836601012, - a_2=0.010000000000000002, - poisson_j_perp_1=0.30000001192092896, - poisson_j_perp_2=0.30000001192092896, - expected_eyoung_j_3=0, - expected_a_3=0.030661087836601014, - expected_poisson_j_perp_3=0.30000001192092896, - ), - ), -) -def test_eyoung_parallel(eyoungparallelparam, monkeypatch, sctfcoil): - """ - Automatically generated Regression Unit Test for eyoung_parallel. - - This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. - - :param eyoungparallelparam: the data used to mock and assert in this test. - :type eyoungparallelparam: eyoungparallelparam - - :param monkeypatch: pytest fixture used to mock module/class variables - :type monkeypatch: _pytest.monkeypatch.monkeypatch - """ - - eyoung_j_3, a_3, poisson_j_perp_3 = sctf.eyoung_parallel( - eyoung_j_1=eyoungparallelparam.eyoung_j_1, - eyoung_j_2=eyoungparallelparam.eyoung_j_2, - a_1=eyoungparallelparam.a_1, - a_2=eyoungparallelparam.a_2, - poisson_j_perp_1=eyoungparallelparam.poisson_j_perp_1, - poisson_j_perp_2=eyoungparallelparam.poisson_j_perp_2, - ) - - assert eyoung_j_3 == pytest.approx(eyoungparallelparam.expected_eyoung_j_3) - - assert a_3 == pytest.approx(eyoungparallelparam.expected_a_3) - - assert poisson_j_perp_3 == pytest.approx( - eyoungparallelparam.expected_poisson_j_perp_3 - ) - - -class EyoungTNestedSquaresParam(NamedTuple): - n: Any = None - - eyoung_j_in: Any = None - - l_in: Any = None - - poisson_j_perp_in: Any = None - - expected_eyoung_j_out: Any = None - - expected_l_out: Any = None - - expected_poisson_j_perp_out: Any = None - - expected_eyoung_stiffest: Any = None - - -class EyoungSeriesParam(NamedTuple): - eyoung_j_1: Any = None - - eyoung_j_2: Any = None - - l_1: Any = None - - l_2: Any = None - - poisson_j_perp_1: Any = None - - poisson_j_perp_2: Any = None - - expected_eyoung_j_3: Any = None - - expected_l_3: Any = None - - expected_poisson_j_perp_3: Any = None - - -class EyoungParallelArrayParam(NamedTuple): - n: Any = None - - eyoung_j_in: Any = None - - a_in: Any = None - - poisson_j_perp_in: Any = None - - expected_eyoung_j_out: Any = None - - expected_a_out: Any = None - - expected_poisson_j_perp_out: Any = None - - -@pytest.mark.parametrize( - "eyoungtnestedsquaresparam", - ( - EyoungTNestedSquaresParam( - n=4, - eyoung_j_in=np.array( - np.array((0, 0, 205000000000, 20000000000, 0), order="F"), order="F" - ).transpose(), - l_in=np.array( - np.array( - ( - 0.010000000000000002, - 0.020661087836601012, - 0.016, - 0.0041799999999999997, - 0, - ), - order="F", - ), - order="F", - ).transpose(), - poisson_j_perp_in=np.array( - np.array( - ( - 0.29999999999999999, - 0.30000001192092896, - 0.29999999999999999, - 0.34000000000000002, - 0, - ), - order="F", - ), - order="F", - ).transpose(), - expected_eyoung_j_out=38289891367.115105, - expected_l_out=0.050841087836601018, - expected_poisson_j_perp_out=0.30931445806415137, - expected_eyoung_stiffest=116443733140.5881, - ), - EyoungTNestedSquaresParam( - n=4, - eyoung_j_in=np.array( - np.array((0, 0, 205000000000, 20000000000, 0), order="F"), order="F" - ).transpose(), - l_in=np.array( - np.array( - ( - 0.010000000000000002, - 0.020661087836601012, - 0.016, - 0.0041799999999999997, - 0, - ), - order="F", - ), - order="F", - ).transpose(), - poisson_j_perp_in=np.array( - np.array( - ( - 0.29999999999999999, - 0.30000001192092896, - 0.29999999999999999, - 0.34000000000000002, - 0, - ), - order="F", - ), - order="F", - ).transpose(), - expected_eyoung_j_out=38289891367.115105, - expected_l_out=0.050841087836601018, - expected_poisson_j_perp_out=0.30931445806415137, - expected_eyoung_stiffest=116443733140.5881, - ), - ), -) -def test_eyoung_t_nested_squares(eyoungtnestedsquaresparam, monkeypatch, sctfcoil): - """ - Automatically generated Regression Unit Test for eyoung_t_nested_squares. - - This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. - - :param eyoungtnestedsquaresparam: the data used to mock and assert in this test. - :type eyoungtnestedsquaresparam: eyoungtnestedsquaresparam - - :param monkeypatch: pytest fixture used to mock module/class variables - :type monkeypatch: _pytest.monkeypatch.monkeypatch - """ - - ( - eyoung_j_out, - l_out, - poisson_j_perp_out, - eyoung_stiffest, - ) = sctf.eyoung_t_nested_squares( - n=eyoungtnestedsquaresparam.n, - eyoung_j_in=eyoungtnestedsquaresparam.eyoung_j_in, - l_in=eyoungtnestedsquaresparam.l_in, - poisson_j_perp_in=eyoungtnestedsquaresparam.poisson_j_perp_in, - ) - - # assert eyoung_j_out == pytest.approx( - # eyoungtnestedsquaresparam.expected_eyoung_j_out - # ) - - # assert l_out == pytest.approx(eyoungtnestedsquaresparam.expected_l_out) - - # assert poisson_j_perp_out == pytest.approx( - # eyoungtnestedsquaresparam.expected_poisson_j_perp_out - # ) - - assert eyoung_stiffest == pytest.approx( - eyoungtnestedsquaresparam.expected_eyoung_stiffest - ) - - -@pytest.mark.parametrize( - "eyoungseriesparam", - ( - EyoungSeriesParam( - eyoung_j_1=0, - eyoung_j_2=117000000000, - l_1=0.003949573550844649, - l_2=0.016711514285756363, - poisson_j_perp_1=0.30000001192092896, - poisson_j_perp_2=0.34999999999999998, - expected_eyoung_j_3=0, - expected_l_3=0.020661087836601012, - expected_poisson_j_perp_3=0.30000001192092896, - ), - EyoungSeriesParam( - eyoung_j_1=0, - eyoung_j_2=0, - l_1=0.020661087836601012, - l_2=0.010000000000000002, - poisson_j_perp_1=0.30000001192092896, - poisson_j_perp_2=0.29999999999999999, - expected_eyoung_j_3=0, - expected_l_3=0.030661087836601014, - expected_poisson_j_perp_3=0.30000001192092896, - ), - ), -) -def test_eyoung_series(eyoungseriesparam, monkeypatch, sctfcoil): - """ - Automatically generated Regression Unit Test for eyoung_series. - - This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. - - :param eyoungseriesparam: the data used to mock and assert in this test. - :type eyoungseriesparam: eyoungseriesparam - - :param monkeypatch: pytest fixture used to mock module/class variables - :type monkeypatch: _pytest.monkeypatch.monkeypatch - """ - - eyoung_j_3, l_3, poisson_j_perp_3 = sctf.eyoung_series( - eyoung_j_1=eyoungseriesparam.eyoung_j_1, - eyoung_j_2=eyoungseriesparam.eyoung_j_2, - l_1=eyoungseriesparam.l_1, - l_2=eyoungseriesparam.l_2, - poisson_j_perp_1=eyoungseriesparam.poisson_j_perp_1, - poisson_j_perp_2=eyoungseriesparam.poisson_j_perp_2, - ) - - assert eyoung_j_3 == pytest.approx(eyoungseriesparam.expected_eyoung_j_3) - - assert l_3 == pytest.approx(eyoungseriesparam.expected_l_3) - - assert poisson_j_perp_3 == pytest.approx( - eyoungseriesparam.expected_poisson_j_perp_3 - ) - - -@pytest.mark.parametrize( - "eyoungparallelarrayparam", - ( - EyoungParallelArrayParam( - n=5, - eyoung_j_in=np.array( - np.array((205000000000, 20000000000, 117000000000, 0, 0), order="F"), - order="F", - ).transpose(), - a_in=np.array( - np.array( - ( - 0.29370123076207649, - 0.11646247019991701, - 0.13374756938078641, - 0.031609694578447076, - 0.1297552160314831, - ), - order="F", - ), - order="F", - ).transpose(), - poisson_j_perp_in=np.array( - np.array( - ( - 0.29999999999999999, - 0.34000000000000002, - 0.34999999999999998, - 0.30000001192092896, - 0.29999999999999999, - ), - order="F", - ), - order="F", - ).transpose(), - expected_eyoung_j_out=110859361820.72557, - expected_a_out=0.70527618095271016, - expected_poisson_j_perp_out=0.31608714140682664, - ), - EyoungParallelArrayParam( - n=5, - eyoung_j_in=np.array( - np.array((205000000000, 20000000000, 117000000000, 0, 0), order="F"), - order="F", - ).transpose(), - a_in=np.array( - np.array( - ( - 0.29370123076207649, - 0.11646247019991701, - 0.13374756938078641, - 0.031609694578447076, - 0.1297552160314831, - ), - order="F", - ), - order="F", - ).transpose(), - poisson_j_perp_in=np.array( - np.array( - ( - 0.29999999999999999, - 0.34000000000000002, - 0.34999999999999998, - 0.30000001192092896, - 0.29999999999999999, - ), - order="F", - ), - order="F", - ).transpose(), - expected_eyoung_j_out=110859361820.72557, - expected_a_out=0.70527618095271016, - expected_poisson_j_perp_out=0.31608714140682664, - ), - ), -) -def test_eyoung_parallel_array(eyoungparallelarrayparam, monkeypatch, sctfcoil): - """ - Automatically generated Regression Unit Test for eyoung_parallel_array. - - This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. - - :param eyoungparallelarrayparam: the data used to mock and assert in this test. - :type eyoungparallelarrayparam: eyoungparallelarrayparam - - :param monkeypatch: pytest fixture used to mock module/class variables - :type monkeypatch: _pytest.monkeypatch.monkeypatch - """ - - eyoung_j_out, a_out, poisson_j_perp_out = sctf.eyoung_parallel_array( - n=eyoungparallelarrayparam.n, - eyoung_j_in=eyoungparallelarrayparam.eyoung_j_in, - a_in=eyoungparallelarrayparam.a_in, - poisson_j_perp_in=eyoungparallelarrayparam.poisson_j_perp_in, - ) - - assert eyoung_j_out == pytest.approx(eyoungparallelarrayparam.expected_eyoung_j_out) - - assert a_out == pytest.approx(eyoungparallelarrayparam.expected_a_out) - - assert poisson_j_perp_out == pytest.approx( - eyoungparallelarrayparam.expected_poisson_j_perp_out - ) - - -@pytest.mark.parametrize( - "sx, sy, sz, expected", - ( - (0, -3.2e8, 2.4e8, 486621002.42385757), - (-2.8e8, 0, 2.4e8, 450777106.7833858), - ), -) -def test_sigvm(sx, sy, sz, expected, sctfcoil): - # could not find an example of a use in PROCESS where - # tx, ty, or tz were anything other than 0 - ret = sctf.sigvm(sx, sy, sz, 0, 0, 0) - - assert ret == pytest.approx(expected) + assert tfcoil_variables.jwptf == pytest.approx(tfwpcurrentsparam.expected_jwptf) def test_vv_stress_on_quench(): diff --git a/tests/unit/test_tfcoil.py b/tests/unit/test_tfcoil.py index 0a400ee3d8..b7105ad8f0 100644 --- a/tests/unit/test_tfcoil.py +++ b/tests/unit/test_tfcoil.py @@ -1,28 +1,36 @@ """Unit and Integration tests for tfcoil.f90.""" -from typing import NamedTuple +from typing import Any, NamedTuple +import numpy as np import pytest +import process.tf_coil as tfcoil_module from process.build import Build +from process.fortran import ( + build_variables, + fwbs_variables, + physics_variables, + sctfcoil_module, + tfcoil_variables, +) from process.fortran import build_variables as bv from process.fortran import fwbs_variables as fwbsv from process.fortran import tfcoil_variables as tfv -from process.sctfcoil import Sctfcoil -from process.tfcoil import TFcoil +from process.tf_coil import TFCoil @pytest.fixture def tfcoil(): - """Provides TFcoil object for testing. + """Provides TFCoil object for testing. :param monkeypatch: pytest mocking fixture :type monkeypatch: MonkeyPatch - :return tfcoil: initialised TFcoil object - :type tfcoil: process.tfcoil.TFcoil + :return tfcoil: initialised TFCoil object + :type tfcoil: process.tfcoil.TFCoil """ - return TFcoil(build=Build(), sctfcoil=Sctfcoil()) + return TFCoil(build=Build()) @pytest.mark.parametrize( @@ -38,7 +46,7 @@ def test_he_density(temperature, expected_density, tfcoil): :param expected_density: expected result of the routine. :type expected_density: float - :param tfcoil: fixture containing an initialised `TFcoil` object + :param tfcoil: fixture containing an initialised `TFCoil` object :type tfcoil: tests.unit.test_tfcoil.tfcoil (functional fixture) """ density = tfcoil.he_density(temperature) @@ -59,7 +67,7 @@ def test_he_cp(temperature, expected_cp, tfcoil): :param expected_cp: expected result of the routine. :type expected_cp: float - :param tfcoil: fixture containing an initialised `TFcoil` object + :param tfcoil: fixture containing an initialised `TFCoil` object :type tfcoil: tests.unit.test_tfcoil.tfcoil (functional fixture) """ @@ -85,7 +93,7 @@ def test_he_visco(temperature, expected_visco, tfcoil): :param expected_visco: expected result of the routine. :type expected_visco: float - :param tfcoil: fixture containing an initialised `TFcoil` object + :param tfcoil: fixture containing an initialised `TFCoil` object :type tfcoil: tests.unit.test_tfcoil.tfcoil (functional fixture) """ visco = tfcoil.he_visco(temperature) @@ -116,7 +124,7 @@ def test_he_th_cond(temperature, expected_th_cond, reinitialise_error_module, tf :param reinitialise_error_module: teardown any error side-effects :type reinitialise_error_module: None - :param tfcoil: fixture containing an initialised `TFcoil` object + :param tfcoil: fixture containing an initialised `TFCoil` object :type tfcoil: tests.unit.test_tfcoil.tfcoil (functional fixture) """ th_cond = tfcoil.he_th_cond(temperature) @@ -142,7 +150,7 @@ def test_al_th_cond(temperature, expected_th_cond, tfcoil): :param al_th_cond: expected result of the routine. :type al_th_cond: float - :param tfcoil: fixture containing an initialised `TFcoil` object + :param tfcoil: fixture containing an initialised `TFCoil` object :type tfcoil: tests.unit.test_tfcoil.tfcoil (functional fixture) """ th_cond = tfcoil.al_th_cond(temperature) @@ -206,7 +214,7 @@ def test_cntrpst(cntrpst_asset, monkeypatch, reinitialise_error_module, tfcoil): :param reinitialise_error_module: teardown any error side-effects :type reinitialise_error_module: None - :param tfcoil: fixture containing an initialised `TFcoil` object + :param tfcoil: fixture containing an initialised `TFCoil` object :type tfcoil: tests.unit.test_tfcoil.tfcoil (functional fixture) """ monkeypatch.setattr(tfv, "a_cp_cool", 1) @@ -230,3 +238,11889 @@ def test_cntrpst(cntrpst_asset, monkeypatch, reinitialise_error_module, tfcoil): assert pytest.approx(tfv.tcpav2) == cntrpst_asset.expected_tcpav2 assert pytest.approx(tfv.tcpmax) == cntrpst_asset.expected_tcpmax assert pytest.approx(tfv.ppump) == cntrpst_asset.expected_ppump + + +class TfGlobalGeometryParam(NamedTuple): + r_tf_outboard_mid: Any = None + + r_cp_top: Any = None + + r_tf_inboard_in: Any = None + + r_tf_inboard_out: Any = None + + dr_tf_outboard: Any = None + + tfareain: Any = None + + c_tf_total: Any = None + + tftort: Any = None + + n_tf_coils: Any = None + + a_tf_leg_outboard: Any = None + + i_tf_sup: Any = None + + dztop: Any = None + + i_tf_case_geom: Any = None + + itart: Any = None + + kappa: Any = None + + rminor: Any = None + + h_cp_top: Any = None + + r_tf_outboard_in: Any = None + + r_tf_outboard_out: Any = None + + theta_coil: Any = None + + tan_theta_coil: Any = None + + expected_tfareain: Any = None + + expected_tftort: Any = None + + expected_a_tf_leg_outboard: Any = None + + expected_r_tf_outboard_in: Any = None + + expected_r_tf_outboard_out: Any = None + + expected_theta_coil: Any = None + + expected_tan_theta_coil: Any = None + + +@pytest.mark.parametrize( + "tfglobalgeometryparam", + ( + TfGlobalGeometryParam( + r_tf_outboard_mid=16.519405859443332, + r_cp_top=4.20194118510911, + r_tf_inboard_in=2.9939411851091102, + r_tf_inboard_out=4.20194118510911, + dr_tf_outboard=1.208, + tfareain=0, + c_tf_total=0, + tftort=1, + n_tf_coils=16, + a_tf_leg_outboard=0, + i_tf_sup=1, + dztop=0, + i_tf_case_geom=0, + itart=0, + kappa=1.8480000000000001, + rminor=2.8677741935483869, + h_cp_top=0, + r_tf_outboard_in=0, + r_tf_outboard_out=0, + theta_coil=0, + tan_theta_coil=0, + expected_tfareain=27.308689677971632, + expected_tftort=1.6395161177915356, + expected_a_tf_leg_outboard=1.9805354702921749, + expected_r_tf_outboard_in=15.915405859443332, + expected_r_tf_outboard_out=17.123405859443331, + expected_theta_coil=0.19634954084936207, + expected_tan_theta_coil=0.19891236737965801, + ), + TfGlobalGeometryParam( + r_tf_outboard_mid=17.063351291812893, + r_cp_top=4.4822055399518357, + r_tf_inboard_in=2.9538679176819831, + r_tf_inboard_out=4.4822055399518357, + dr_tf_outboard=1.5283376222698528, + tfareain=35.703669036223495, + c_tf_total=241812532.66279837, + tftort=1.7488698442633552, + n_tf_coils=16, + a_tf_leg_outboard=2.6728635794409041, + i_tf_sup=1, + dztop=0, + i_tf_case_geom=0, + itart=0, + kappa=1.8480000000000001, + rminor=2.9620024998595755, + h_cp_top=0, + r_tf_outboard_in=16.299182480677967, + r_tf_outboard_out=17.827520102947819, + theta_coil=0.19634954084936207, + tan_theta_coil=0.19891236737965801, + expected_tfareain=35.703669036223495, + expected_tftort=1.7488698442633552, + expected_a_tf_leg_outboard=2.6728635794409041, + expected_r_tf_outboard_in=16.299182480677967, + expected_r_tf_outboard_out=17.827520102947819, + expected_theta_coil=0.19634954084936207, + expected_tan_theta_coil=0.19891236737965801, + ), + ), +) +def test_tf_global_geometry(tfglobalgeometryparam, monkeypatch, tfcoil): + """ + Automatically generated Regression Unit Test for tf_global_geometry. + + This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. + + :param tfglobalgeometryparam: the data used to mock and assert in this test. + :type tfglobalgeometryparam: tfglobalgeometryparam + + :param monkeypatch: pytest fixture used to mock module/class variables + :type monkeypatch: _pytest.monkeypatch.monkeypatch + + :param sctfcoil: initialised SuperconductingTFCoil object + :type sctfcoil: process.sctfcoil.SuperconductingTFCoil + """ + + monkeypatch.setattr( + build_variables, "r_tf_outboard_mid", tfglobalgeometryparam.r_tf_outboard_mid + ) + + monkeypatch.setattr(build_variables, "r_cp_top", tfglobalgeometryparam.r_cp_top) + + monkeypatch.setattr( + build_variables, "r_tf_inboard_in", tfglobalgeometryparam.r_tf_inboard_in + ) + + monkeypatch.setattr( + build_variables, "r_tf_inboard_out", tfglobalgeometryparam.r_tf_inboard_out + ) + + monkeypatch.setattr( + build_variables, "dr_tf_outboard", tfglobalgeometryparam.dr_tf_outboard + ) + + monkeypatch.setattr(tfcoil_variables, "tfareain", tfglobalgeometryparam.tfareain) + + monkeypatch.setattr( + tfcoil_variables, "c_tf_total", tfglobalgeometryparam.c_tf_total + ) + + monkeypatch.setattr(tfcoil_variables, "tftort", tfglobalgeometryparam.tftort) + + monkeypatch.setattr( + tfcoil_variables, "n_tf_coils", tfglobalgeometryparam.n_tf_coils + ) + + monkeypatch.setattr( + tfcoil_variables, "a_tf_leg_outboard", tfglobalgeometryparam.a_tf_leg_outboard + ) + + monkeypatch.setattr(tfcoil_variables, "i_tf_sup", tfglobalgeometryparam.i_tf_sup) + + monkeypatch.setattr(tfcoil_variables, "dztop", tfglobalgeometryparam.dztop) + + monkeypatch.setattr( + tfcoil_variables, "i_tf_case_geom", tfglobalgeometryparam.i_tf_case_geom + ) + + monkeypatch.setattr(physics_variables, "itart", tfglobalgeometryparam.itart) + + monkeypatch.setattr(physics_variables, "kappa", tfglobalgeometryparam.kappa) + + monkeypatch.setattr(physics_variables, "rminor", tfglobalgeometryparam.rminor) + + monkeypatch.setattr(sctfcoil_module, "h_cp_top", tfglobalgeometryparam.h_cp_top) + + monkeypatch.setattr( + sctfcoil_module, "r_tf_outboard_in", tfglobalgeometryparam.r_tf_outboard_in + ) + + monkeypatch.setattr( + sctfcoil_module, "r_tf_outboard_out", tfglobalgeometryparam.r_tf_outboard_out + ) + + monkeypatch.setattr(sctfcoil_module, "theta_coil", tfglobalgeometryparam.theta_coil) + + monkeypatch.setattr( + sctfcoil_module, "tan_theta_coil", tfglobalgeometryparam.tan_theta_coil + ) + + tfcoil.tf_global_geometry() + + assert tfcoil_variables.tfareain == pytest.approx( + tfglobalgeometryparam.expected_tfareain + ) + + assert tfcoil_variables.tftort == pytest.approx( + tfglobalgeometryparam.expected_tftort + ) + + assert tfcoil_variables.a_tf_leg_outboard == pytest.approx( + tfglobalgeometryparam.expected_a_tf_leg_outboard + ) + + assert sctfcoil_module.r_tf_outboard_in == pytest.approx( + tfglobalgeometryparam.expected_r_tf_outboard_in + ) + + assert sctfcoil_module.r_tf_outboard_out == pytest.approx( + tfglobalgeometryparam.expected_r_tf_outboard_out + ) + + assert sctfcoil_module.theta_coil == pytest.approx( + tfglobalgeometryparam.expected_theta_coil + ) + + assert sctfcoil_module.tan_theta_coil == pytest.approx( + tfglobalgeometryparam.expected_tan_theta_coil + ) + + +class TfCurrentParam(NamedTuple): + casthi: Any = None + + c_tf_total: Any = None + + rbmax: Any = None + + i_tf_sup: Any = None + + casths_fraction: Any = None + + tinstf: Any = None + + tftort: Any = None + + bmaxtf: Any = None + + tfinsgap: Any = None + + tfc_sidewall_is_fraction: Any = None + + casths: Any = None + + casthi_is_fraction: Any = None + + casthi_fraction: Any = None + + n_tf_coils: Any = None + + thicndut: Any = None + + thkcas: Any = None + + oacdcp: Any = None + + tfareain: Any = None + + r_tf_inboard_out: Any = None + + r_tf_inboard_in: Any = None + + dr_tf_inboard: Any = None + + bt: Any = None + + rmajor: Any = None + + tfc_current: Any = None + + theta_coil: Any = None + + expected_c_tf_total: Any = None + + expected_rbmax: Any = None + + expected_bmaxtf: Any = None + + expected_oacdcp: Any = None + + expected_tfc_current: Any = None + + +@pytest.mark.parametrize( + "tfcurrentparam", + ( + TfCurrentParam( + casthi=0.060000000000000012, + c_tf_total=0, + rbmax=0, + i_tf_sup=1, + casths_fraction=0.059999999999999998, + tinstf=0.0080000000000000019, + tftort=1.6395161177915356, + bmaxtf=0, + tfinsgap=0.01, + tfc_sidewall_is_fraction=False, + casths=0.05000000000000001, + casthi_is_fraction=False, + casthi_fraction=0.050000000000000003, + n_tf_coils=16, + thicndut=0.002, + thkcas=0.52465000000000006, + oacdcp=8673900, + tfareain=27.308689677971632, + r_tf_inboard_out=4.20194118510911, + r_tf_inboard_in=2.9939411851091102, + dr_tf_inboard=1.208, + bt=5.3292000000000002, + rmajor=8.8901000000000003, + tfc_current=0, + theta_coil=0.19634954084936207, + expected_c_tf_total=236885604.60000002, + expected_rbmax=4.0432020634751211, + expected_bmaxtf=11.717722779177526, + expected_oacdcp=8674367.2945641987, + expected_tfc_current=14805350.287500001, + ), + ), +) +def test_tf_current(tfcurrentparam, monkeypatch, tfcoil): + """ + Automatically generated Regression Unit Test for tf_current. + + This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. + + :param tfcurrentparam: the data used to mock and assert in this test. + :type tfcurrentparam: tfcurrentparam + + :param monkeypatch: pytest fixture used to mock module/class variables + :type monkeypatch: _pytest.monkeypatch.monkeypatch + + :param sctfcoil: initialised SuperconductingTFCoil object + :type sctfcoil: process.sctfcoil.SuperconductingTFCoil + """ + + monkeypatch.setattr(tfcoil_variables, "casthi", tfcurrentparam.casthi) + + monkeypatch.setattr(tfcoil_variables, "c_tf_total", tfcurrentparam.c_tf_total) + + monkeypatch.setattr(tfcoil_variables, "rbmax", tfcurrentparam.rbmax) + + monkeypatch.setattr(tfcoil_variables, "i_tf_sup", tfcurrentparam.i_tf_sup) + + monkeypatch.setattr( + tfcoil_variables, "casths_fraction", tfcurrentparam.casths_fraction + ) + + monkeypatch.setattr(tfcoil_variables, "tinstf", tfcurrentparam.tinstf) + + monkeypatch.setattr(tfcoil_variables, "tftort", tfcurrentparam.tftort) + + monkeypatch.setattr(tfcoil_variables, "bmaxtf", tfcurrentparam.bmaxtf) + + monkeypatch.setattr(tfcoil_variables, "tfinsgap", tfcurrentparam.tfinsgap) + + monkeypatch.setattr( + tfcoil_variables, + "tfc_sidewall_is_fraction", + tfcurrentparam.tfc_sidewall_is_fraction, + ) + + monkeypatch.setattr(tfcoil_variables, "casths", tfcurrentparam.casths) + + monkeypatch.setattr( + tfcoil_variables, "casthi_is_fraction", tfcurrentparam.casthi_is_fraction + ) + + monkeypatch.setattr( + tfcoil_variables, "casthi_fraction", tfcurrentparam.casthi_fraction + ) + + monkeypatch.setattr(tfcoil_variables, "n_tf_coils", tfcurrentparam.n_tf_coils) + + monkeypatch.setattr(tfcoil_variables, "thicndut", tfcurrentparam.thicndut) + + monkeypatch.setattr(tfcoil_variables, "thkcas", tfcurrentparam.thkcas) + + monkeypatch.setattr(tfcoil_variables, "oacdcp", tfcurrentparam.oacdcp) + + monkeypatch.setattr(tfcoil_variables, "tfareain", tfcurrentparam.tfareain) + + monkeypatch.setattr( + build_variables, "r_tf_inboard_out", tfcurrentparam.r_tf_inboard_out + ) + + monkeypatch.setattr( + build_variables, "r_tf_inboard_in", tfcurrentparam.r_tf_inboard_in + ) + + monkeypatch.setattr(build_variables, "dr_tf_inboard", tfcurrentparam.dr_tf_inboard) + + monkeypatch.setattr(physics_variables, "bt", tfcurrentparam.bt) + + monkeypatch.setattr(physics_variables, "rmajor", tfcurrentparam.rmajor) + + monkeypatch.setattr(sctfcoil_module, "tfc_current", tfcurrentparam.tfc_current) + + monkeypatch.setattr(sctfcoil_module, "theta_coil", tfcurrentparam.theta_coil) + + tfcoil.tf_current() + + assert tfcoil_variables.c_tf_total == pytest.approx( + tfcurrentparam.expected_c_tf_total + ) + + assert tfcoil_variables.rbmax == pytest.approx(tfcurrentparam.expected_rbmax) + + assert tfcoil_variables.bmaxtf == pytest.approx(tfcurrentparam.expected_bmaxtf) + + assert tfcoil_variables.oacdcp == pytest.approx(tfcurrentparam.expected_oacdcp) + + assert sctfcoil_module.tfc_current == pytest.approx( + tfcurrentparam.expected_tfc_current + ) + + +@pytest.mark.parametrize( + "a, b, expected_circumference", + ( + (2.667950e9, 6.782819e8, 11464316399.111176), + (4.7186039761812131, 3.6192586838709673, 26.308134540723429), + ), + ids=["johndcook", "baseline_2018"], +) +def test_circumference(a, b, expected_circumference, tfcoil): + """Unit test for the sctfcoil circumference routine. + + This unit test uses values from an external blog referenced in the + routine header (https://www.johndcook.com/blog/2013/05/05/ramanujan-circumference-ellipse/) + as well as results obtained from baseline 2018. + + :param a: the value of a (x/a)^2... in the formula of an ellipse + :type a: float + + :param b: the value of b ...(y/b)^2... in the formula of an ellipse + :type b: float + + :param expected_circumference: the expected result of the routine given inputs a and b + :type expected_circumference: float + + :param sctfcoil: initialised SuperconductingTFCoil object + :type sctfcoil: process.sctfcoil.SuperconductingTFCoil + """ + assert tfcoil.circumference(a, b) == pytest.approx(expected_circumference) + + +class TfFieldAndForceParam(NamedTuple): + rminor: Any = None + + rmajor: Any = None + + bt: Any = None + + itart: Any = None + + r_tf_outboard_mid: Any = None + + r_vv_inboard_out: Any = None + + r_tf_inboard_mid: Any = None + + r_cp_top: Any = None + + vforce: Any = None + + n_tf_coils: Any = None + + taucq: Any = None + + sigvvall: Any = None + + cforce: Any = None + + c_tf_total: Any = None + + bmaxtf: Any = None + + i_tf_sup: Any = None + + f_vforce_inboard: Any = None + + vforce_outboard: Any = None + + tinstf: Any = None + + thicndut: Any = None + + dr_tf_wp: Any = None + + tfinsgap: Any = None + + i_cp_joints: Any = None + + casthi: Any = None + + r_tf_outboard_in: Any = None + + r_wp_inner: Any = None + + r_wp_outer: Any = None + + vforce_inboard_tot: Any = None + + expected_vforce: Any = None + + expected_cforce: Any = None + + expected_f_vforce_inboard: Any = None + + expected_vforce_outboard: Any = None + + expected_vforce_inboard_tot: Any = None + + +@pytest.mark.parametrize( + "tffieldandforceparam", + ( + TfFieldAndForceParam( + rminor=0.97142857142857153, + rmajor=1.7000000000000002, + bt=3, + itart=1, + r_tf_outboard_mid=4.1688435714285719, + r_vv_inboard_out=0.20483000000000001, + r_tf_inboard_mid=0.077415000000000012, + r_cp_top=0.87643571428571443, + vforce=0, + n_tf_coils=12, + taucq=30, + sigvvall=93000000, + cforce=0, + c_tf_total=25500000, + bmaxtf=34.862617362267024, + i_tf_sup=0, + f_vforce_inboard=0.5, + vforce_outboard=0, + tinstf=0, + thicndut=0.00080000000000000004, + dr_tf_wp=0.15483000000000002, + tfinsgap=0.01, + i_cp_joints=1, + casthi=0.0077415000000000019, + r_tf_outboard_in=4.0914285714285716, + r_wp_inner=0, + r_wp_outer=0.14708850000000001, + vforce_inboard_tot=0, + expected_vforce=12380916.66459452, + expected_cforce=37041530.947408713, + expected_f_vforce_inboard=0.59539634897566385, + expected_vforce_outboard=8413494.7991220243, + expected_vforce_inboard_tot=148570999.97513425, + ), + TfFieldAndForceParam( + rminor=0.97142857142857153, + rmajor=1.7000000000000002, + bt=3, + itart=1, + r_tf_outboard_mid=4.1868435714285717, + r_vv_inboard_out=0.20483000000000001, + r_tf_inboard_mid=0.077415000000000012, + r_cp_top=0.85843571428571441, + vforce=12380916.66459452, + n_tf_coils=12, + taucq=30, + sigvvall=93000000, + cforce=37041530.947408713, + c_tf_total=25500000, + bmaxtf=34.862617362267024, + i_tf_sup=0, + f_vforce_inboard=0.59539634897566385, + vforce_outboard=8413494.7991220243, + tinstf=0, + thicndut=0.00080000000000000004, + dr_tf_wp=0.14708850000000001, + tfinsgap=0.01, + i_cp_joints=1, + casthi=0.0077415000000000019, + r_tf_outboard_in=4.1094285714285714, + r_wp_inner=0, + r_wp_outer=0.14708850000000001, + vforce_inboard_tot=148570999.97513425, + expected_vforce=12268469.138442248, + expected_cforce=37041530.947408713, + expected_f_vforce_inboard=0.58932254522566518, + expected_vforce_outboard=8549450.0771621168, + expected_vforce_inboard_tot=147221629.66130698, + ), + ), +) +def test_tf_field_and_force(tffieldandforceparam, monkeypatch, tfcoil): + """ + Automatically generated Regression Unit Test for tf_field_and_force. + + This test was generated using data from tests/regression/scenarios/FNSF/IN.DAT. + + :param tffieldandforceparam: the data used to mock and assert in this test. + :type tffieldandforceparam: tffieldandforceparam + + :param monkeypatch: pytest fixture used to mock module/class variables + :type monkeypatch: _pytest.monkeypatch.monkeypatch + """ + + monkeypatch.setattr(physics_variables, "rminor", tffieldandforceparam.rminor) + + monkeypatch.setattr(physics_variables, "rmajor", tffieldandforceparam.rmajor) + + monkeypatch.setattr(physics_variables, "bt", tffieldandforceparam.bt) + + monkeypatch.setattr(physics_variables, "itart", tffieldandforceparam.itart) + + monkeypatch.setattr( + build_variables, "r_tf_outboard_mid", tffieldandforceparam.r_tf_outboard_mid + ) + + monkeypatch.setattr( + build_variables, "r_vv_inboard_out", tffieldandforceparam.r_vv_inboard_out + ) + + monkeypatch.setattr( + build_variables, "r_tf_inboard_mid", tffieldandforceparam.r_tf_inboard_mid + ) + + monkeypatch.setattr(build_variables, "r_cp_top", tffieldandforceparam.r_cp_top) + + monkeypatch.setattr(tfcoil_variables, "vforce", tffieldandforceparam.vforce) + + monkeypatch.setattr(tfcoil_variables, "n_tf_coils", tffieldandforceparam.n_tf_coils) + + monkeypatch.setattr(tfcoil_variables, "cforce", tffieldandforceparam.cforce) + + monkeypatch.setattr(tfcoil_variables, "c_tf_total", tffieldandforceparam.c_tf_total) + + monkeypatch.setattr(tfcoil_variables, "bmaxtf", tffieldandforceparam.bmaxtf) + + monkeypatch.setattr(tfcoil_variables, "i_tf_sup", tffieldandforceparam.i_tf_sup) + + monkeypatch.setattr( + tfcoil_variables, "f_vforce_inboard", tffieldandforceparam.f_vforce_inboard + ) + + monkeypatch.setattr( + tfcoil_variables, "vforce_outboard", tffieldandforceparam.vforce_outboard + ) + + monkeypatch.setattr(tfcoil_variables, "tinstf", tffieldandforceparam.tinstf) + + monkeypatch.setattr(tfcoil_variables, "thicndut", tffieldandforceparam.thicndut) + + monkeypatch.setattr(tfcoil_variables, "dr_tf_wp", tffieldandforceparam.dr_tf_wp) + + monkeypatch.setattr(tfcoil_variables, "tfinsgap", tffieldandforceparam.tfinsgap) + + monkeypatch.setattr( + tfcoil_variables, "i_cp_joints", tffieldandforceparam.i_cp_joints + ) + + monkeypatch.setattr(tfcoil_variables, "casthi", tffieldandforceparam.casthi) + + monkeypatch.setattr( + sctfcoil_module, "r_tf_outboard_in", tffieldandforceparam.r_tf_outboard_in + ) + + monkeypatch.setattr(sctfcoil_module, "r_wp_inner", tffieldandforceparam.r_wp_inner) + + monkeypatch.setattr(sctfcoil_module, "r_wp_outer", tffieldandforceparam.r_wp_outer) + + monkeypatch.setattr( + sctfcoil_module, "vforce_inboard_tot", tffieldandforceparam.vforce_inboard_tot + ) + + tfcoil.tf_field_and_force() + + assert tfcoil_variables.vforce == pytest.approx( + tffieldandforceparam.expected_vforce + ) + + assert tfcoil_variables.cforce == pytest.approx( + tffieldandforceparam.expected_cforce + ) + + assert tfcoil_variables.f_vforce_inboard == pytest.approx( + tffieldandforceparam.expected_f_vforce_inboard + ) + + assert tfcoil_variables.vforce_outboard == pytest.approx( + tffieldandforceparam.expected_vforce_outboard + ) + + assert sctfcoil_module.vforce_inboard_tot == pytest.approx( + tffieldandforceparam.expected_vforce_inboard_tot + ) + + +class TfcindParam(NamedTuple): + yarc: Any = None + + xarc: Any = None + + tfind: Any = None + + tfthk: Any = None + + expected_yarc: Any = None + + expected_tfind: Any = None + + +@pytest.mark.parametrize( + "tfcindparam", + ( + TfcindParam( + yarc=np.array( + ( + 4.5228880258064512, + 7.5381467096774184, + 0, + -9.0730900215620327, + -5.4438540129372193, + ), + order="F", + ), + xarc=np.array( + ( + 4.20194118510911, + 8.316545161290323, + 15.915405859443332, + 8.316545161290323, + 4.20194118510911, + ), + order="F", + ), + tfind=0, + tfthk=1.208, + expected_tfind=5.4453892599192845e-06, + ), + TfcindParam( + yarc=np.array( + ( + 4.5336880258064509, + 7.5561467096774191, + 0, + -9.0730900215620327, + -5.4438540129372193, + ), + order="F", + ), + xarc=np.array( + ( + 4.20194118510911, + 8.316545161290323, + 15.915405859443332, + 8.316545161290323, + 4.20194118510911, + ), + order="F", + ), + tfind=5.4524893280368181e-06, + tfthk=1.208, + expected_tfind=5.4524893280368181e-06, + ), + ), +) +def test_tfcind(tfcindparam, monkeypatch, tfcoil): + """ + Automatically generated Regression Unit Test for tfcind. + + This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. + + :param tfcindparam: the data used to mock and assert in this test. + :type tfcindparam: tfcindparam + + :param monkeypatch: pytest fixture used to mock module/class variables + :type monkeypatch: _pytest.monkeypatch.monkeypatch + """ + + monkeypatch.setattr(tfcoil_variables, "tfind", tfcindparam.tfind) + + tfind = tfcoil.tfcind( + tfthk=tfcindparam.tfthk, xarc=tfcindparam.xarc, yarc=tfcindparam.yarc + ) + + assert tfind == pytest.approx(tfcindparam.expected_tfind) + + +class TfCoilAreaAndMassesParam(NamedTuple): + hr1: Any = None + + r_tf_outboard_mid: Any = None + + dr_tf_inboard: Any = None + + r_tf_inboard_mid: Any = None + + r_tf_inboard_in: Any = None + + r_tf_inboard_out: Any = None + + hmax: Any = None + + denstl: Any = None + + whtconsh: Any = None + + whttf: Any = None + + whtcas: Any = None + + tficrn: Any = None + + tfcryoarea: Any = None + + whtgw: Any = None + + tfocrn: Any = None + + whtconsc: Any = None + + whtconcu: Any = None + + whtcon: Any = None + + whtconin: Any = None + + vftf: Any = None + + dcond: Any = None + + dcondins: Any = None + + len_tf_coil: Any = None + + dcase: Any = None + + acndttf: Any = None + + n_tf_turn: Any = None + + n_tf_coils: Any = None + + aiwp: Any = None + + acasetfo: Any = None + + acasetf: Any = None + + fcutfsu: Any = None + + awphec: Any = None + + acstf: Any = None + + whttflgs: Any = None + + whtcp: Any = None + + whtconal: Any = None + + vol_cond_cp: Any = None + + i_tf_sup: Any = None + + i_tf_sc_mat: Any = None + + a_tf_leg_outboard: Any = None + + thkcas: Any = None + + voltfleg: Any = None + + cplen: Any = None + + itart: Any = None + + awpc: Any = None + + awptf: Any = None + + vol_ins_cp: Any = None + + vol_gr_ins_cp: Any = None + + vol_case_cp: Any = None + + a_leg_ins: Any = None + + a_leg_gr_ins: Any = None + + a_leg_cond: Any = None + + theta_coil: Any = None + + tan_theta_coil: Any = None + + expected_whtconsh: Any = None + + expected_whtcas: Any = None + + expected_tficrn: Any = None + + expected_tfcryoarea: Any = None + + expected_whtgw: Any = None + + expected_tfocrn: Any = None + + expected_whtconsc: Any = None + + expected_whtconcu: Any = None + + expected_whtcon: Any = None + + expected_whtconin: Any = None + + expected_cplen: Any = None + + +@pytest.mark.parametrize( + "tfcoilareaandmassesparam", + ( + TfCoilAreaAndMassesParam( + hr1=0, + r_tf_outboard_mid=16.519405859443332, + dr_tf_inboard=1.208, + r_tf_inboard_mid=3.5979411851091103, + r_tf_inboard_in=2.9939411851091102, + r_tf_inboard_out=4.20194118510911, + hmax=9.0730900215620327, + denstl=7800, + whtconsh=0, + whttf=0, + whtcas=0, + tficrn=0, + tfcryoarea=0, + whtgw=0, + tfocrn=0, + whtconsc=0, + whtconcu=0, + whtcon=0, + whtconin=0, + vftf=0.30000000000000004, + dcond=np.array( + np.array( + (6080, 6080, 6070, 6080, 6080, 8500, 6070, 8500, 8500), order="F" + ), + order="F", + ).transpose(), + dcondins=1800, + len_tf_coil=50.483843027201402, + dcase=8000, + acndttf=0.0014685061538103825, + n_tf_turn=200, + n_tf_coils=16, + aiwp=0.087880174466980876, + acasetfo=1.2752592893394648, + acasetf=1.0015169239205168, + fcutfsu=0.80884, + awphec=0.015707963267948974, + acstf=0.001293323051622732, + whttflgs=0, + whtcp=0, + whtconal=0, + vol_cond_cp=0, + i_tf_sup=1, + i_tf_sc_mat=5, + a_tf_leg_outboard=1.9805354702921749, + thkcas=0.52465000000000006, + voltfleg=0, + cplen=0, + itart=0, + awpc=0.70527618095271016, + awptf=0.64024601555360383, + vol_ins_cp=0, + vol_gr_ins_cp=0, + vol_case_cp=0, + a_leg_ins=0, + a_leg_gr_ins=0, + a_leg_cond=0, + theta_coil=0.19634954084936207, + tan_theta_coil=0.19891236737965801, + expected_whtconsh=115651.90127937049, + expected_whtcas=1034021.9996272125, + expected_tficrn=0.8197580588957678, + expected_tfcryoarea=6381.2092203414386, + expected_whtgw=5909.3507916745702, + expected_tfocrn=0.59553192892551199, + expected_whtconsc=5802.5700395134345, + expected_whtconcu=58744.465423173802, + expected_whtcon=188184.68882144717, + expected_whtconin=7985.7520793894437, + expected_cplen=20.562180043124066, + ), + TfCoilAreaAndMassesParam( + hr1=0, + r_tf_outboard_mid=16.519405859443332, + dr_tf_inboard=1.208, + r_tf_inboard_mid=3.5979411851091103, + r_tf_inboard_in=2.9939411851091102, + r_tf_inboard_out=4.20194118510911, + hmax=9.0730900215620327, + denstl=7800, + whtconsh=115651.90127937049, + whttf=19649856.627845347, + whtcas=1034021.9996272125, + tficrn=0.8197580588957678, + tfcryoarea=6381.2092203414386, + whtgw=5909.3507916745702, + tfocrn=0.59553192892551199, + whtconsc=5802.5700395134345, + whtconcu=58744.465423173802, + whtcon=0, + whtconin=0, + vftf=0.30000000000000004, + dcond=np.array( + np.array( + (6080, 6080, 6070, 6080, 6080, 8500, 6070, 8500, 8500), order="F" + ), + order="F", + ).transpose(), + dcondins=1800, + len_tf_coil=50.514015976170839, + dcase=8000, + acndttf=0.0014685061538103825, + n_tf_turn=200, + n_tf_coils=16, + aiwp=0.087880174466980876, + acasetfo=1.2752592893394648, + acasetf=1.0015169239205168, + fcutfsu=0.80884, + awphec=0.015707963267948974, + acstf=0.001293323051622732, + whttflgs=0, + whtcp=0, + whtconal=0, + vol_cond_cp=0, + i_tf_sup=1, + i_tf_sc_mat=5, + a_tf_leg_outboard=1.9805354702921749, + thkcas=0.52465000000000006, + voltfleg=0, + cplen=20.562180043124066, + itart=0, + awpc=0.70527618095271016, + awptf=0.64024601555360383, + vol_ins_cp=0, + vol_gr_ins_cp=0, + vol_case_cp=0, + a_leg_ins=0, + a_leg_gr_ins=0, + a_leg_cond=0, + theta_coil=0.19634954084936207, + tan_theta_coil=0.19891236737965801, + expected_whtconsh=115721.02357090525, + expected_whtcas=1034699.2182961091, + expected_tficrn=0.8197580588957678, + expected_tfcryoarea=6385.0231118485681, + expected_whtgw=5912.8826650262808, + expected_tfocrn=0.59553192892551199, + expected_whtconsc=5806.038092640837, + expected_whtconcu=58779.575542593491, + expected_whtcon=188297.16217276, + expected_whtconin=7990.5249666247555, + expected_cplen=20.562180043124066, + ), + ), +) +def test_tf_coil_area_and_masses(tfcoilareaandmassesparam, monkeypatch, tfcoil): + """ + Automatically generated Regression Unit Test for tf_coil_area_and_masses. + + This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. + + :param tfcoilareaandmassesparam: the data used to mock and assert in this test. + :type tfcoilareaandmassesparam: tfcoilareaandmassesparam + + :param monkeypatch: pytest fixture used to mock module/class variables + :type monkeypatch: _pytest.monkeypatch.monkeypatch + """ + + monkeypatch.setattr(build_variables, "hr1", tfcoilareaandmassesparam.hr1) + + monkeypatch.setattr( + build_variables, "r_tf_outboard_mid", tfcoilareaandmassesparam.r_tf_outboard_mid + ) + + monkeypatch.setattr( + build_variables, "dr_tf_inboard", tfcoilareaandmassesparam.dr_tf_inboard + ) + + monkeypatch.setattr( + build_variables, "r_tf_inboard_mid", tfcoilareaandmassesparam.r_tf_inboard_mid + ) + + monkeypatch.setattr( + build_variables, "r_tf_inboard_in", tfcoilareaandmassesparam.r_tf_inboard_in + ) + + monkeypatch.setattr( + build_variables, "r_tf_inboard_out", tfcoilareaandmassesparam.r_tf_inboard_out + ) + + monkeypatch.setattr(build_variables, "hmax", tfcoilareaandmassesparam.hmax) + + monkeypatch.setattr(fwbs_variables, "denstl", tfcoilareaandmassesparam.denstl) + + monkeypatch.setattr(tfcoil_variables, "whtconsh", tfcoilareaandmassesparam.whtconsh) + + monkeypatch.setattr(tfcoil_variables, "whttf", tfcoilareaandmassesparam.whttf) + + monkeypatch.setattr(tfcoil_variables, "whtcas", tfcoilareaandmassesparam.whtcas) + + monkeypatch.setattr(tfcoil_variables, "tficrn", tfcoilareaandmassesparam.tficrn) + + monkeypatch.setattr( + tfcoil_variables, "tfcryoarea", tfcoilareaandmassesparam.tfcryoarea + ) + + monkeypatch.setattr(tfcoil_variables, "whtgw", tfcoilareaandmassesparam.whtgw) + + monkeypatch.setattr(tfcoil_variables, "tfocrn", tfcoilareaandmassesparam.tfocrn) + + monkeypatch.setattr(tfcoil_variables, "whtconsc", tfcoilareaandmassesparam.whtconsc) + + monkeypatch.setattr(tfcoil_variables, "whtconcu", tfcoilareaandmassesparam.whtconcu) + + monkeypatch.setattr(tfcoil_variables, "whtcon", tfcoilareaandmassesparam.whtcon) + + monkeypatch.setattr(tfcoil_variables, "whtconin", tfcoilareaandmassesparam.whtconin) + + monkeypatch.setattr( + tfcoil_variables, "tfcryoarea", tfcoilareaandmassesparam.tfcryoarea + ) + + monkeypatch.setattr(tfcoil_variables, "vftf", tfcoilareaandmassesparam.vftf) + + monkeypatch.setattr(tfcoil_variables, "dcond", tfcoilareaandmassesparam.dcond) + + monkeypatch.setattr(tfcoil_variables, "dcondins", tfcoilareaandmassesparam.dcondins) + + monkeypatch.setattr( + tfcoil_variables, "len_tf_coil", tfcoilareaandmassesparam.len_tf_coil + ) + + monkeypatch.setattr(tfcoil_variables, "dcase", tfcoilareaandmassesparam.dcase) + + monkeypatch.setattr(tfcoil_variables, "acndttf", tfcoilareaandmassesparam.acndttf) + + monkeypatch.setattr( + tfcoil_variables, "n_tf_turn", tfcoilareaandmassesparam.n_tf_turn + ) + + monkeypatch.setattr( + tfcoil_variables, "n_tf_coils", tfcoilareaandmassesparam.n_tf_coils + ) + + monkeypatch.setattr(tfcoil_variables, "aiwp", tfcoilareaandmassesparam.aiwp) + + monkeypatch.setattr(tfcoil_variables, "acasetfo", tfcoilareaandmassesparam.acasetfo) + + monkeypatch.setattr(tfcoil_variables, "acasetf", tfcoilareaandmassesparam.acasetf) + + monkeypatch.setattr(tfcoil_variables, "fcutfsu", tfcoilareaandmassesparam.fcutfsu) + + monkeypatch.setattr(tfcoil_variables, "awphec", tfcoilareaandmassesparam.awphec) + + monkeypatch.setattr(tfcoil_variables, "acstf", tfcoilareaandmassesparam.acstf) + + monkeypatch.setattr(tfcoil_variables, "whttflgs", tfcoilareaandmassesparam.whttflgs) + + monkeypatch.setattr(tfcoil_variables, "whtcp", tfcoilareaandmassesparam.whtcp) + + monkeypatch.setattr(tfcoil_variables, "whtconal", tfcoilareaandmassesparam.whtconal) + + monkeypatch.setattr( + tfcoil_variables, "vol_cond_cp", tfcoilareaandmassesparam.vol_cond_cp + ) + + monkeypatch.setattr(tfcoil_variables, "i_tf_sup", tfcoilareaandmassesparam.i_tf_sup) + + monkeypatch.setattr( + tfcoil_variables, "i_tf_sc_mat", tfcoilareaandmassesparam.i_tf_sc_mat + ) + + monkeypatch.setattr( + tfcoil_variables, + "a_tf_leg_outboard", + tfcoilareaandmassesparam.a_tf_leg_outboard, + ) + + monkeypatch.setattr(tfcoil_variables, "thkcas", tfcoilareaandmassesparam.thkcas) + + monkeypatch.setattr(tfcoil_variables, "voltfleg", tfcoilareaandmassesparam.voltfleg) + + monkeypatch.setattr(tfcoil_variables, "cplen", tfcoilareaandmassesparam.cplen) + + monkeypatch.setattr(physics_variables, "itart", tfcoilareaandmassesparam.itart) + + monkeypatch.setattr(sctfcoil_module, "awpc", tfcoilareaandmassesparam.awpc) + + monkeypatch.setattr(sctfcoil_module, "awptf", tfcoilareaandmassesparam.awptf) + + monkeypatch.setattr( + sctfcoil_module, "vol_ins_cp", tfcoilareaandmassesparam.vol_ins_cp + ) + + monkeypatch.setattr( + sctfcoil_module, "vol_gr_ins_cp", tfcoilareaandmassesparam.vol_gr_ins_cp + ) + + monkeypatch.setattr( + sctfcoil_module, "vol_case_cp", tfcoilareaandmassesparam.vol_case_cp + ) + + monkeypatch.setattr( + sctfcoil_module, "a_leg_ins", tfcoilareaandmassesparam.a_leg_ins + ) + + monkeypatch.setattr( + sctfcoil_module, "a_leg_gr_ins", tfcoilareaandmassesparam.a_leg_gr_ins + ) + + monkeypatch.setattr( + sctfcoil_module, "a_leg_cond", tfcoilareaandmassesparam.a_leg_cond + ) + + monkeypatch.setattr( + sctfcoil_module, "theta_coil", tfcoilareaandmassesparam.theta_coil + ) + + monkeypatch.setattr( + sctfcoil_module, "tan_theta_coil", tfcoilareaandmassesparam.tan_theta_coil + ) + + tfcoil.tf_coil_area_and_masses() + + assert tfcoil_variables.whtconsh == pytest.approx( + tfcoilareaandmassesparam.expected_whtconsh + ) + + assert tfcoil_variables.whtcas == pytest.approx( + tfcoilareaandmassesparam.expected_whtcas + ) + + assert tfcoil_variables.tficrn == pytest.approx( + tfcoilareaandmassesparam.expected_tficrn + ) + + assert tfcoil_variables.tfcryoarea == pytest.approx( + tfcoilareaandmassesparam.expected_tfcryoarea + ) + + assert tfcoil_variables.whtgw == pytest.approx( + tfcoilareaandmassesparam.expected_whtgw + ) + + assert tfcoil_variables.tfocrn == pytest.approx( + tfcoilareaandmassesparam.expected_tfocrn + ) + + assert tfcoil_variables.whtconsc == pytest.approx( + tfcoilareaandmassesparam.expected_whtconsc + ) + + assert tfcoil_variables.whtconcu == pytest.approx( + tfcoilareaandmassesparam.expected_whtconcu + ) + + assert tfcoil_variables.whtcon == pytest.approx( + tfcoilareaandmassesparam.expected_whtcon + ) + + assert tfcoil_variables.whtconin == pytest.approx( + tfcoilareaandmassesparam.expected_whtconin + ) + + assert tfcoil_variables.cplen == pytest.approx( + tfcoilareaandmassesparam.expected_cplen + ) + + +class StressclParam(NamedTuple): + dr_tf_inboard: Any = None + + r_tf_inboard_mid: Any = None + + dr_bore: Any = None + + dr_cs: Any = None + + i_tf_inside_cs: Any = None + + dr_cs_tf_gap: Any = None + + hmax: Any = None + + r_tf_inboard_in: Any = None + + casestr: Any = None + + n_tf_turn: Any = None + + dr_tf_wp: Any = None + + i_tf_tresca: Any = None + + acstf: Any = None + + vforce: Any = None + + c_tf_total: Any = None + + jwptf: Any = None + + sig_tf_cs_bucked: Any = None + + sig_tf_case: Any = None + + sig_tf_wp: Any = None + + thwcndut: Any = None + + insstrain: Any = None + + tinstf: Any = None + + thicndut: Any = None + + acndttf: Any = None + + tfinsgap: Any = None + + acasetf: Any = None + + sig_tf_case_max: Any = None + + poisson_steel: Any = None + + poisson_copper: Any = None + + poisson_al: Any = None + + n_tf_graded_layers: Any = None + + i_tf_sup: Any = None + + i_tf_bucking: Any = None + + fcoolcp: Any = None + + eyoung_cond_axial: Any = None + + eyoung_steel: Any = None + + eyoung_res_tf_buck: Any = None + + eyoung_ins: Any = None + + eyoung_al: Any = None + + eyoung_copper: Any = None + + aiwp: Any = None + + aswp: Any = None + + cpttf: Any = None + + n_tf_coils: Any = None + + i_tf_stress_model: Any = None + + sig_tf_wp_max: Any = None + + i_tf_turns_integer: Any = None + + casthi: Any = None + + acond: Any = None + + avwp: Any = None + + awphec: Any = None + + poisson_ins: Any = None + + eyoung_cond_trans: Any = None + + poisson_cond_axial: Any = None + + poisson_cond_trans: Any = None + + dhecoil: Any = None + + fcutfsu: Any = None + + str_wp: Any = None + + n_tf_wp_layers: Any = None + + i_pf_conductor: Any = None + + f_a_cs_steel: Any = None + + f_z_cs_tf_internal: Any = None + + j_cs_flat_top_end: Any = None + + j_cs_pulse_start: Any = None + + n_pf_coils_in_group: Any = None + + c_pf_coil_turn_peak_input: Any = None + + awpc: Any = None + + a_tf_steel: Any = None + + a_tf_ins: Any = None + + r_wp_inner: Any = None + + r_wp_outer: Any = None + + t_wp_toroidal: Any = None + + t_wp_toroidal_av: Any = None + + t_lat_case_av: Any = None + + a_case_front: Any = None + + a_case_nose: Any = None + + theta_coil: Any = None + + tan_theta_coil: Any = None + + t_cable_radial: Any = None + + t_cable: Any = None + + vforce_inboard_tot: Any = None + + iprint: Any = None + + outfile: Any = None + + n_radial_array: Any = None + + n_tf_layer: Any = None + + expected_casestr: Any = None + + expected_sig_tf_case: Any = None + + expected_sig_tf_wp: Any = None + + expected_insstrain: Any = None + + expected_str_wp: Any = None + + +@pytest.mark.parametrize( + "stressclparam", + ( + StressclParam( + dr_tf_inboard=1.208, + i_tf_inside_cs=0, + dr_cs_tf_gap=0.01, + r_tf_inboard_mid=3.5979411851091103, + dr_bore=2.3322000000000003, + dr_cs=0.55242000000000002, + hmax=9.0730900215620327, + r_tf_inboard_in=2.9939411851091102, + casestr=0, + n_tf_turn=200, + dr_tf_wp=0.54261087836601019, + i_tf_tresca=0, + acstf=0.001293323051622732, + vforce=250545611.13801825, + c_tf_total=236885604.60000002, + jwptf=23124470.793774806, + sig_tf_cs_bucked=0, + sig_tf_case=0, + sig_tf_wp=0, + thwcndut=0.0080000000000000002, + insstrain=0, + tinstf=0.0080000000000000019, + thicndut=0.002, + acndttf=0.0014685061538103825, + tfinsgap=0.01, + acasetf=1.0015169239205168, + sig_tf_case_max=580000000, + poisson_steel=0.29999999999999999, + poisson_copper=0.34999999999999998, + poisson_al=0.34999999999999998, + n_tf_graded_layers=1, + i_tf_sup=1, + i_tf_bucking=1, + fcoolcp=0.29999999999999999, + eyoung_cond_axial=0, + eyoung_steel=205000000000, + eyoung_res_tf_buck=150000000000, + eyoung_ins=20000000000, + eyoung_al=np.array(69000000000.0), + eyoung_copper=np.array(117000000000.0), + aiwp=0.087880174466980876, + aswp=0.29370123076207649, + cpttf=74026.751437500003, + n_tf_coils=16, + i_tf_stress_model=1, + sig_tf_wp_max=580000000, + i_tf_turns_integer=1, + casthi=0.060000000000000012, + acond=0.1653572639592335, + avwp=0.07759938309736393, + awphec=0.015707963267948974, + poisson_ins=0.34000000000000002, + eyoung_cond_trans=0, + poisson_cond_axial=0.30000001192092896, + poisson_cond_trans=0.30000001192092896, + dhecoil=0.010000000000000002, + fcutfsu=0.80884, + str_wp=0, + n_tf_wp_layers=5, + i_pf_conductor=0, + f_a_cs_steel=0.57874999999999999, + f_z_cs_tf_internal=0.90000000000000002, + j_cs_flat_top_end=20726000, + j_cs_pulse_start=0, + n_pf_coils_in_group=np.array( + np.array((1, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0), order="F"), order="F" + ).transpose(), + c_pf_coil_turn_peak_input=np.array( + np.array( + ( + 42200, + 42200, + 42200, + 42200, + 43000, + 43000, + 43000, + 43000, + 40000, + 40000, + 40000, + 40000, + 40000, + 40000, + 40000, + 40000, + 40000, + 40000, + 40000, + 40000, + 40000, + 40000, + ), + order="F", + ), + order="F", + ).transpose(), + awpc=0.70527618095271016, + a_tf_steel=1.2952181546825934, + a_tf_ins=0.11646247019991701, + r_wp_inner=3.5185911851091101, + r_wp_outer=4.06120206347512, + t_wp_toroidal=1.299782604942499, + t_wp_toroidal_av=1.299782604942499, + t_lat_case_av=0.10396600719086938, + a_case_front=0.18607458590131154, + a_case_nose=0.70261616505511615, + theta_coil=0.19634954084936207, + tan_theta_coil=0.19891236737965801, + t_cable_radial=0.030661087836601014, + t_cable=0.036389912284773368, + vforce_inboard_tot=4008729778.208292, + iprint=0, + outfile=11, + n_radial_array=100, + n_tf_layer=3, + expected_casestr=0.00094360452596334093, + expected_sig_tf_case=543381805.25001633, + expected_sig_tf_wp=397005702.35272157, + expected_insstrain=-0.006687152422925652, + expected_str_wp=0.0015619754370069119, + ), + StressclParam( + dr_tf_inboard=1.208, + i_tf_inside_cs=0, + dr_cs_tf_gap=0.01, + r_tf_inboard_mid=3.5979411851091103, + dr_bore=2.3322000000000003, + dr_cs=0.55242000000000002, + hmax=9.0730900215620327, + r_tf_inboard_in=2.9939411851091102, + casestr=0.00094360452596334093, + n_tf_turn=200, + dr_tf_wp=0.54261087836601019, + i_tf_tresca=0, + acstf=0.001293323051622732, + vforce=250545611.13801825, + c_tf_total=236885604.60000002, + jwptf=23124470.793774806, + sig_tf_cs_bucked=0, + sig_tf_case=543381805.25001633, + sig_tf_wp=397005702.35272157, + thwcndut=0.0080000000000000002, + insstrain=0, + tinstf=0.0080000000000000019, + thicndut=0.002, + acndttf=0.0014685061538103825, + tfinsgap=0.01, + acasetf=1.0015169239205168, + sig_tf_case_max=580000000, + poisson_steel=0.29999999999999999, + poisson_copper=0.34999999999999998, + poisson_al=0.34999999999999998, + n_tf_graded_layers=1, + i_tf_sup=1, + i_tf_bucking=1, + fcoolcp=0.29999999999999999, + eyoung_cond_axial=0, + eyoung_steel=205000000000, + eyoung_res_tf_buck=150000000000, + eyoung_ins=20000000000, + eyoung_al=np.array(69000000000.0), + eyoung_copper=np.array(117000000000.0), + aiwp=0.087880174466980876, + aswp=0.29370123076207649, + cpttf=74026.751437500003, + n_tf_coils=16, + i_tf_stress_model=1, + sig_tf_wp_max=580000000, + i_tf_turns_integer=1, + casthi=0.060000000000000012, + acond=0.1653572639592335, + avwp=0.07759938309736393, + awphec=0.015707963267948974, + poisson_ins=0.34000000000000002, + eyoung_cond_trans=0, + poisson_cond_axial=0.30000001192092896, + poisson_cond_trans=0.30000001192092896, + dhecoil=0.010000000000000002, + fcutfsu=0.80884, + str_wp=0.0015619754370069119, + n_tf_wp_layers=5, + i_pf_conductor=0, + f_a_cs_steel=0.57874999999999999, + f_z_cs_tf_internal=0.90000000000000002, + j_cs_flat_top_end=20726000, + j_cs_pulse_start=19311657.760000002, + n_pf_coils_in_group=np.array( + np.array((1, 1, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0), order="F"), order="F" + ).transpose(), + c_pf_coil_turn_peak_input=np.array( + np.array( + ( + 42200, + 42200, + 42200, + 42200, + 43000, + 43000, + 43000, + 43000, + 40000, + 40000, + 40000, + 40000, + 40000, + 40000, + 40000, + 40000, + 40000, + 40000, + 40000, + 40000, + 40000, + 40000, + ), + order="F", + ), + order="F", + ).transpose(), + awpc=0.70527618095271016, + a_tf_steel=1.2952181546825934, + a_tf_ins=0.11646247019991701, + r_wp_inner=3.5185911851091101, + r_wp_outer=4.06120206347512, + t_wp_toroidal=1.299782604942499, + t_wp_toroidal_av=1.299782604942499, + t_lat_case_av=0.10396600719086938, + a_case_front=0.18607458590131154, + a_case_nose=0.70261616505511615, + theta_coil=0.19634954084936207, + tan_theta_coil=0.19891236737965801, + t_cable_radial=0.030661087836601014, + t_cable=0.036389912284773368, + vforce_inboard_tot=4008729778.208292, + iprint=0, + outfile=11, + n_radial_array=100, + n_tf_layer=3, + expected_casestr=0.00094360452596334093, + expected_sig_tf_case=543381805.25001633, + expected_sig_tf_wp=397005702.35272157, + expected_insstrain=-0.006687152422925652, + expected_str_wp=0.0015619754370069119, + ), + ), +) +def test_stresscl(stressclparam, monkeypatch, tfcoil): + """ + Automatically generated Regression Unit Test for stresscl. + + This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. + + :param stressclparam: the data used to mock and assert in this test. + :type stressclparam: stressclparam + + :param monkeypatch: pytest fixture used to mock module/class variables + :type monkeypatch: _pytest.monkeypatch.monkeypatch + """ + + ( + _, + _, + _, + _, + _, + _, + _, + _, + _, + _, + _, + _, + _, + _, + _, + _, + _, + _, + _, + _, + _, + _, + _, + _, + _, + _, + _, + sig_tf_wp, + sig_tf_case, + _, + str_wp, + casestr, + insstrain, + _, + ) = tfcoil.stresscl( + stressclparam.n_tf_layer, + stressclparam.n_radial_array, + stressclparam.n_tf_wp_layers, + stressclparam.i_tf_bucking, + stressclparam.r_tf_inboard_in, + stressclparam.dr_bore, + stressclparam.hmax, + stressclparam.f_z_cs_tf_internal, + stressclparam.dr_cs, + stressclparam.i_tf_inside_cs, + stressclparam.dr_tf_inboard, + stressclparam.dr_cs_tf_gap, + stressclparam.i_pf_conductor, + stressclparam.j_cs_flat_top_end, + stressclparam.j_cs_pulse_start, + stressclparam.c_pf_coil_turn_peak_input, + stressclparam.n_pf_coils_in_group, + 70 / 22, + 3e-3, + stressclparam.f_a_cs_steel, + stressclparam.eyoung_steel, + stressclparam.poisson_steel, + stressclparam.eyoung_cond_axial, + stressclparam.poisson_cond_axial, + stressclparam.eyoung_cond_trans, + stressclparam.poisson_cond_trans, + stressclparam.eyoung_ins, + stressclparam.poisson_ins, + stressclparam.thicndut, + stressclparam.eyoung_copper, + stressclparam.poisson_copper, + stressclparam.i_tf_sup, + stressclparam.eyoung_res_tf_buck, + stressclparam.r_wp_inner, + stressclparam.tan_theta_coil, + stressclparam.theta_coil, + stressclparam.r_wp_outer, + stressclparam.a_tf_steel, + stressclparam.a_case_front, + stressclparam.a_case_nose, + stressclparam.tfinsgap, + stressclparam.tinstf, + stressclparam.n_tf_turn, + stressclparam.i_tf_turns_integer, + stressclparam.t_cable, + stressclparam.t_cable_radial, + stressclparam.dhecoil, + stressclparam.fcutfsu, + stressclparam.thwcndut, + stressclparam.t_lat_case_av, + stressclparam.t_wp_toroidal_av, + stressclparam.a_tf_ins, + stressclparam.aswp, + stressclparam.acond, + stressclparam.awpc, + stressclparam.eyoung_al, + stressclparam.poisson_al, + stressclparam.fcoolcp, + stressclparam.n_tf_graded_layers, + stressclparam.c_tf_total, + stressclparam.casthi, + stressclparam.i_tf_stress_model, + stressclparam.vforce_inboard_tot, + stressclparam.i_tf_tresca, + stressclparam.acasetf, + stressclparam.vforce, + stressclparam.acndttf, + ) + + assert casestr == pytest.approx(stressclparam.expected_casestr, rel=0.01) + + assert sig_tf_case == pytest.approx(stressclparam.expected_sig_tf_case, rel=0.01) + + assert sig_tf_wp == pytest.approx(stressclparam.expected_sig_tf_wp, rel=0.01) + + assert insstrain == pytest.approx(stressclparam.expected_insstrain, rel=0.01) + + assert str_wp == pytest.approx(stressclparam.expected_str_wp, rel=0.01) + + +class PlaneStressParam(NamedTuple): + linesolv: Any = None + + n_radial_array: Any = None + + nlayers: Any = None + + nu: Any = None + + rad: Any = None + + ey: Any = None + + j: Any = None + + expected_sigr: Any = None + + expected_sigt: Any = None + + expected_r_deflect: Any = None + + expected_rradius: Any = None + + +@pytest.mark.parametrize( + "planestressparam", + ( + PlaneStressParam( + n_radial_array=100, + nlayers=3, + nu=np.array( + np.array( + (0.29999999999999999, 0.30904421667064924, 0.29999999999999999), + order="F", + ), + order="F", + ).transpose(), + rad=np.array( + np.array( + ( + 2.9939411851091102, + 3.5414797139565706, + 4.0876202904571599, + 4.1476202904571595, + ), + order="F", + ), + order="F", + ).transpose(), + ey=np.array( + np.array((205000000000, 43126670035.025253, 205000000000), order="F"), + order="F", + ).transpose(), + j=np.array( + np.array((0, 18097185.781970859, 0), order="F"), order="F" + ).transpose(), + expected_sigr=[ + 0.0, + -638231.855766482, + -1272978.0149700674, + -1904263.8142719064, + -2532114.3605434406, + -3156554.533362813, + -3777608.9874793817, + -4395302.155247777, + -5009658.2490304215, + -5620701.263570679, + -6228454.97833536, + -6832942.959828614, + -7434188.563876412, + -8032214.937882571, + -8627045.023056716, + -9218701.556614282, + -9807207.07394974, + -10392583.910782054, + -10974854.205274347, + -11554039.900126804, + -12130162.744643984, + -12703244.296776803, + -13273305.925139055, + -13840368.810999524, + -14404453.950249525, + -14965582.155346265, + -15523774.05723289, + -16079050.107234456, + -16631430.578931632, + -17180935.57001094, + -17727585.00409306, + -18271398.632538624, + -18812396.036232498, + -19350596.627346307, + -19886019.651079975, + -20418684.18738201, + -20948609.1526495, + -21475813.301407494, + -22000315.22796835, + -22522133.368071266, + -23041286.00050247, + -23557791.24869583, + -24071667.082314458, + -24582931.31881398, + -25091601.624986775, + -25597695.518488254, + -26101230.36934514, + -26602223.401445802, + -27100691.6940132, + -27596652.183060553, + -28090121.6628299, + -28581116.78721381, + -29069654.071160544, + -29555749.892062735, + -30039420.491130088, + -30520681.974745892, + -30999550.31580798, + -31476041.355053987, + -31950170.802371547, + -32421954.23809305, + -32891407.114275593, + -33358544.755966377, + -33823382.3624533, + -34285935.00850141, + -34746217.64557501, + -35204245.10304597, + -35660032.08938813, + -36113593.19335821, + -36564942.88516308, + -37014095.51761406, + -37461065.327267796, + -37905866.4355546, + -38348512.84989362, + -38789018.46479585, + -39227397.06295431, + -39663662.31632225, + -40097827.78717919, + -40529906.92918485, + -40959913.08842154, + -41387859.50442459, + -41813759.31120163, + -42237625.5382402, + -42659471.11150431, + -43079308.85441998, + 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module/class variables + :type monkeypatch: _pytest.monkeypatch.monkeypatch + """ + + sigr, sigt, r_deflect, rradius = tfcoil_module.plane_stress( + n_radial_array=planestressparam.n_radial_array, + nlayers=planestressparam.nlayers, + nu=planestressparam.nu, + rad=planestressparam.rad, + ey=planestressparam.ey, + j=planestressparam.j, + ) + + assert sigr == pytest.approx(planestressparam.expected_sigr) + assert sigt == pytest.approx(planestressparam.expected_sigt) + assert r_deflect == pytest.approx(planestressparam.expected_r_deflect) + assert rradius == pytest.approx(planestressparam.expected_rradius) + + +class ExtendedPlaneStrainParam(NamedTuple): + n_radial_array: Any = None + + nlayers: Any = None + + i_tf_bucking: Any = None + + nu_t: Any = None + + nu_zt: Any = None + + ey_t: Any = None + + ey_z: Any = None + + d_curr: Any = None + + rad: Any = None + + v_force: Any = None + + expected_sigr: Any = None + + expected_sigt: Any = None + + expected_sigz: Any = None + + expected_str_r: Any = None + + expected_str_t: Any = None + + expected_str_z: Any = None + + expected_r_deflect: Any = None + + expected_rradius: Any = None + + +@pytest.mark.parametrize( + "extendedplanestrainparam", + ( + ExtendedPlaneStrainParam( + n_radial_array=100, + nlayers=2, + i_tf_bucking=0, + nu_t=np.array( + np.array((0.34999999999999998, 0.29999999999999999), order="F"), + order="F", + ).transpose(), + nu_zt=np.array( + np.array((0.34948024015688117, 0.29999999999999999), order="F"), + order="F", + ).transpose(), + ey_t=np.array( + np.array((117000000000, 205000000000), order="F"), order="F" + ).transpose(), + ey_z=np.array( + np.array((97843910970.178864, 205000000000.00003), order="F"), + order="F", + ).transpose(), + d_curr=np.array( + np.array((375174117.44492483, 0), order="F"), order="F" + ).transpose(), + rad=np.array( + np.array((0, 0.14708850000000001, 0.15483000000000002), order="F"), + order="F", + ).transpose(), + v_force=147221629.66130698, + expected_sigr=[ + 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+def test_extended_plane_strain(extendedplanestrainparam, monkeypatch): + """ + Automatically generated Regression Unit Test for extended_plane_strain. + + This test was generated using data from tests/regression/scenarios/FNSF/IN.DAT. + + :param extendedplanestrainparam: the data used to mock and assert in this test. + :type extendedplanestrainparam: extendedplanestrainparam + + :param monkeypatch: pytest fixture used to mock module/class variables + :type monkeypatch: _pytest.monkeypatch.monkeypatch + """ + + ( + rradius, + sigr, + sigt, + sigz, + str_r, + str_t, + str_z, + r_deflect, + ) = tfcoil_module.extended_plane_strain( + n_radial_array=extendedplanestrainparam.n_radial_array, + nlayers=extendedplanestrainparam.nlayers, + i_tf_bucking=extendedplanestrainparam.i_tf_bucking, + nu_t=extendedplanestrainparam.nu_t, + nu_zt=extendedplanestrainparam.nu_zt, + ey_t=extendedplanestrainparam.ey_t, + ey_z=extendedplanestrainparam.ey_z, + d_curr=extendedplanestrainparam.d_curr, + rad=extendedplanestrainparam.rad, + v_force=extendedplanestrainparam.v_force, + ) + + # assert sigr == pytest.approx(extendedplanestrainparam.expected_sigr, rel=0.01) + + np.testing.assert_array_almost_equal( + sigr, np.array(extendedplanestrainparam.expected_sigr), decimal=3 + ) + + np.testing.assert_array_almost_equal( + sigt, np.array(extendedplanestrainparam.expected_sigt), decimal=3 + ) + + np.testing.assert_array_almost_equal( + sigz, np.array(extendedplanestrainparam.expected_sigz), decimal=3 + ) + + np.testing.assert_array_almost_equal( + str_r, np.array(extendedplanestrainparam.expected_str_r) + ) + + np.testing.assert_array_almost_equal( + str_t, np.array(extendedplanestrainparam.expected_str_t) + ) + + np.testing.assert_array_almost_equal( + str_z, np.array(extendedplanestrainparam.expected_str_z) + ) + + np.testing.assert_array_almost_equal( + r_deflect, np.array(extendedplanestrainparam.expected_r_deflect) + ) + + np.testing.assert_array_almost_equal( + rradius, np.array(extendedplanestrainparam.expected_rradius) + ) + + +class EyoungParallelParam(NamedTuple): + eyoung_j_1: Any = None + + eyoung_j_2: Any = None + + a_1: Any = None + + a_2: Any = None + + poisson_j_perp_1: Any = None + + poisson_j_perp_2: Any = None + + expected_eyoung_j_3: Any = None + + expected_a_3: Any = None + + expected_poisson_j_perp_3: Any = None + + +@pytest.mark.parametrize( + "eyoungparallelparam", + ( + EyoungParallelParam( + eyoung_j_1=0, + eyoung_j_2=0, + a_1=0.010000000000000002, + a_2=0, + poisson_j_perp_1=0.30000001192092896, + poisson_j_perp_2=0, + expected_eyoung_j_3=0, + expected_a_3=0.010000000000000002, + expected_poisson_j_perp_3=0.30000001192092896, + ), + EyoungParallelParam( + eyoung_j_1=0, + eyoung_j_2=0, + a_1=0.020661087836601012, + a_2=0.010000000000000002, + poisson_j_perp_1=0.30000001192092896, + poisson_j_perp_2=0.30000001192092896, + expected_eyoung_j_3=0, + expected_a_3=0.030661087836601014, + expected_poisson_j_perp_3=0.30000001192092896, + ), + ), +) +def test_eyoung_parallel(eyoungparallelparam, monkeypatch): + """ + Automatically generated Regression Unit Test for eyoung_parallel. + + This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. + + :param eyoungparallelparam: the data used to mock and assert in this test. + :type eyoungparallelparam: eyoungparallelparam + + :param monkeypatch: pytest fixture used to mock module/class variables + :type monkeypatch: _pytest.monkeypatch.monkeypatch + """ + + eyoung_j_3, a_3, poisson_j_perp_3 = tfcoil_module.eyoung_parallel( + eyoung_j_1=eyoungparallelparam.eyoung_j_1, + eyoung_j_2=eyoungparallelparam.eyoung_j_2, + a_1=eyoungparallelparam.a_1, + a_2=eyoungparallelparam.a_2, + poisson_j_perp_1=eyoungparallelparam.poisson_j_perp_1, + poisson_j_perp_2=eyoungparallelparam.poisson_j_perp_2, + ) + + assert eyoung_j_3 == pytest.approx(eyoungparallelparam.expected_eyoung_j_3) + + assert a_3 == pytest.approx(eyoungparallelparam.expected_a_3) + + assert poisson_j_perp_3 == pytest.approx( + eyoungparallelparam.expected_poisson_j_perp_3 + ) + + +class EyoungTNestedSquaresParam(NamedTuple): + n: Any = None + + eyoung_j_in: Any = None + + l_in: Any = None + + poisson_j_perp_in: Any = None + + expected_eyoung_j_out: Any = None + + expected_l_out: Any = None + + expected_poisson_j_perp_out: Any = None + + expected_eyoung_stiffest: Any = None + + +class EyoungSeriesParam(NamedTuple): + eyoung_j_1: Any = None + + eyoung_j_2: Any = None + + l_1: Any = None + + l_2: Any = None + + poisson_j_perp_1: Any = None + + poisson_j_perp_2: Any = None + + expected_eyoung_j_3: Any = None + + expected_l_3: Any = None + + expected_poisson_j_perp_3: Any = None + + +class EyoungParallelArrayParam(NamedTuple): + n: Any = None + + eyoung_j_in: Any = None + + a_in: Any = None + + poisson_j_perp_in: Any = None + + expected_eyoung_j_out: Any = None + + expected_a_out: Any = None + + expected_poisson_j_perp_out: Any = None + + +@pytest.mark.parametrize( + "eyoungtnestedsquaresparam", + ( + EyoungTNestedSquaresParam( + n=4, + eyoung_j_in=np.array( + np.array((0, 0, 205000000000, 20000000000, 0), order="F"), order="F" + ).transpose(), + l_in=np.array( + np.array( + ( + 0.010000000000000002, + 0.020661087836601012, + 0.016, + 0.0041799999999999997, + 0, + ), + order="F", + ), + order="F", + ).transpose(), + poisson_j_perp_in=np.array( + np.array( + ( + 0.29999999999999999, + 0.30000001192092896, + 0.29999999999999999, + 0.34000000000000002, + 0, + ), + order="F", + ), + order="F", + ).transpose(), + expected_eyoung_j_out=38289891367.115105, + expected_l_out=0.050841087836601018, + expected_poisson_j_perp_out=0.30931445806415137, + expected_eyoung_stiffest=116443733140.5881, + ), + EyoungTNestedSquaresParam( + n=4, + eyoung_j_in=np.array( + np.array((0, 0, 205000000000, 20000000000, 0), order="F"), order="F" + ).transpose(), + l_in=np.array( + np.array( + ( + 0.010000000000000002, + 0.020661087836601012, + 0.016, + 0.0041799999999999997, + 0, + ), + order="F", + ), + order="F", + ).transpose(), + poisson_j_perp_in=np.array( + np.array( + ( + 0.29999999999999999, + 0.30000001192092896, + 0.29999999999999999, + 0.34000000000000002, + 0, + ), + order="F", + ), + order="F", + ).transpose(), + expected_eyoung_j_out=38289891367.115105, + expected_l_out=0.050841087836601018, + expected_poisson_j_perp_out=0.30931445806415137, + expected_eyoung_stiffest=116443733140.5881, + ), + ), +) +def test_eyoung_t_nested_squares(eyoungtnestedsquaresparam, monkeypatch): + """ + Automatically generated Regression Unit Test for eyoung_t_nested_squares. + + This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. + + :param eyoungtnestedsquaresparam: the data used to mock and assert in this test. + :type eyoungtnestedsquaresparam: eyoungtnestedsquaresparam + + :param monkeypatch: pytest fixture used to mock module/class variables + :type monkeypatch: _pytest.monkeypatch.monkeypatch + """ + + ( + eyoung_j_out, + l_out, + poisson_j_perp_out, + eyoung_stiffest, + ) = tfcoil_module.eyoung_t_nested_squares( + n=eyoungtnestedsquaresparam.n, + eyoung_j_in=eyoungtnestedsquaresparam.eyoung_j_in, + l_in=eyoungtnestedsquaresparam.l_in, + poisson_j_perp_in=eyoungtnestedsquaresparam.poisson_j_perp_in, + ) + + # assert eyoung_j_out == pytest.approx( + # eyoungtnestedsquaresparam.expected_eyoung_j_out + # ) + + # assert l_out == pytest.approx(eyoungtnestedsquaresparam.expected_l_out) + + # assert poisson_j_perp_out == pytest.approx( + # eyoungtnestedsquaresparam.expected_poisson_j_perp_out + # ) + + assert eyoung_stiffest == pytest.approx( + eyoungtnestedsquaresparam.expected_eyoung_stiffest + ) + + +@pytest.mark.parametrize( + "eyoungseriesparam", + ( + EyoungSeriesParam( + eyoung_j_1=0, + eyoung_j_2=117000000000, + l_1=0.003949573550844649, + l_2=0.016711514285756363, + poisson_j_perp_1=0.30000001192092896, + poisson_j_perp_2=0.34999999999999998, + expected_eyoung_j_3=0, + expected_l_3=0.020661087836601012, + expected_poisson_j_perp_3=0.30000001192092896, + ), + EyoungSeriesParam( + eyoung_j_1=0, + eyoung_j_2=0, + l_1=0.020661087836601012, + l_2=0.010000000000000002, + poisson_j_perp_1=0.30000001192092896, + poisson_j_perp_2=0.29999999999999999, + expected_eyoung_j_3=0, + expected_l_3=0.030661087836601014, + expected_poisson_j_perp_3=0.30000001192092896, + ), + ), +) +def test_eyoung_series(eyoungseriesparam, monkeypatch): + """ + Automatically generated Regression Unit Test for eyoung_series. + + This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. + + :param eyoungseriesparam: the data used to mock and assert in this test. + :type eyoungseriesparam: eyoungseriesparam + + :param monkeypatch: pytest fixture used to mock module/class variables + :type monkeypatch: _pytest.monkeypatch.monkeypatch + """ + + eyoung_j_3, l_3, poisson_j_perp_3 = tfcoil_module.eyoung_series( + eyoung_j_1=eyoungseriesparam.eyoung_j_1, + eyoung_j_2=eyoungseriesparam.eyoung_j_2, + l_1=eyoungseriesparam.l_1, + l_2=eyoungseriesparam.l_2, + poisson_j_perp_1=eyoungseriesparam.poisson_j_perp_1, + poisson_j_perp_2=eyoungseriesparam.poisson_j_perp_2, + ) + + assert eyoung_j_3 == pytest.approx(eyoungseriesparam.expected_eyoung_j_3) + + assert l_3 == pytest.approx(eyoungseriesparam.expected_l_3) + + assert poisson_j_perp_3 == pytest.approx( + eyoungseriesparam.expected_poisson_j_perp_3 + ) + + +@pytest.mark.parametrize( + "eyoungparallelarrayparam", + ( + EyoungParallelArrayParam( + n=5, + eyoung_j_in=np.array( + np.array((205000000000, 20000000000, 117000000000, 0, 0), order="F"), + order="F", + ).transpose(), + a_in=np.array( + np.array( + ( + 0.29370123076207649, + 0.11646247019991701, + 0.13374756938078641, + 0.031609694578447076, + 0.1297552160314831, + ), + order="F", + ), + order="F", + ).transpose(), + poisson_j_perp_in=np.array( + np.array( + ( + 0.29999999999999999, + 0.34000000000000002, + 0.34999999999999998, + 0.30000001192092896, + 0.29999999999999999, + ), + order="F", + ), + order="F", + ).transpose(), + expected_eyoung_j_out=110859361820.72557, + expected_a_out=0.70527618095271016, + expected_poisson_j_perp_out=0.31608714140682664, + ), + EyoungParallelArrayParam( + n=5, + eyoung_j_in=np.array( + np.array((205000000000, 20000000000, 117000000000, 0, 0), order="F"), + order="F", + ).transpose(), + a_in=np.array( + np.array( + ( + 0.29370123076207649, + 0.11646247019991701, + 0.13374756938078641, + 0.031609694578447076, + 0.1297552160314831, + ), + order="F", + ), + order="F", + ).transpose(), + poisson_j_perp_in=np.array( + np.array( + ( + 0.29999999999999999, + 0.34000000000000002, + 0.34999999999999998, + 0.30000001192092896, + 0.29999999999999999, + ), + order="F", + ), + order="F", + ).transpose(), + expected_eyoung_j_out=110859361820.72557, + expected_a_out=0.70527618095271016, + expected_poisson_j_perp_out=0.31608714140682664, + ), + ), +) +def test_eyoung_parallel_array(eyoungparallelarrayparam, monkeypatch): + """ + Automatically generated Regression Unit Test for eyoung_parallel_array. + + This test was generated using data from tracking/baseline_2018/baseline_2018_IN.DAT. + + :param eyoungparallelarrayparam: the data used to mock and assert in this test. + :type eyoungparallelarrayparam: eyoungparallelarrayparam + + :param monkeypatch: pytest fixture used to mock module/class variables + :type monkeypatch: _pytest.monkeypatch.monkeypatch + """ + + eyoung_j_out, a_out, poisson_j_perp_out = tfcoil_module.eyoung_parallel_array( + n=eyoungparallelarrayparam.n, + eyoung_j_in=eyoungparallelarrayparam.eyoung_j_in, + a_in=eyoungparallelarrayparam.a_in, + poisson_j_perp_in=eyoungparallelarrayparam.poisson_j_perp_in, + ) + + assert eyoung_j_out == pytest.approx(eyoungparallelarrayparam.expected_eyoung_j_out) + + assert a_out == pytest.approx(eyoungparallelarrayparam.expected_a_out) + + assert poisson_j_perp_out == pytest.approx( + eyoungparallelarrayparam.expected_poisson_j_perp_out + ) + + +@pytest.mark.parametrize( + "sx, sy, sz, expected", + ( + (0, -3.2e8, 2.4e8, 486621002.42385757), + (-2.8e8, 0, 2.4e8, 450777106.7833858), + ), +) +def test_sigvm(sx, sy, sz, expected): + # could not find an example of a use in PROCESS where + # tx, ty, or tz were anything other than 0 + ret = tfcoil_module.sigvm(sx, sy, sz, 0, 0, 0) + + assert ret == pytest.approx(expected) diff --git a/tests/unit/test_vacuum.py b/tests/unit/test_vacuum.py index 8835588bfb..7b6c79ec9d 100644 --- a/tests/unit/test_vacuum.py +++ b/tests/unit/test_vacuum.py @@ -26,7 +26,7 @@ def test_simple_model(self, monkeypatch, vacuum): :param monkeypatch: Mock fixture :type monkeypatch: object - :param tfcoil: fixture containing an initialised `TFcoil` object + :param tfcoil: fixture containing an initialised `TFCoil` object :type tfcoil: tests.unit.test_tfcoil.tfcoil (functional fixture) """ monkeypatch.setattr(pv, "qfuel", 7.5745668997694112e22)