diff --git a/Notebooks/Continuum_Mechanics/constitutive_relations.ipynb b/Notebooks/Continuum_Mechanics/constitutive_relations.ipynb deleted file mode 100644 index e61d937d5..000000000 --- a/Notebooks/Continuum_Mechanics/constitutive_relations.ipynb +++ /dev/null @@ -1,521 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# constitutive : The Constitutive Library\n", - "\n", - "This notebook provide the functions that define the different stiffness tensors considering a Voigt notation" - ], - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "%matplotlib inline\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import simcoon as sim\n", - "import os" - ], - "outputs": [], - "execution_count": 4, - "metadata": { - "execution": { - "iopub.status.busy": "2022-01-30T22:16:20.210Z", - "iopub.execute_input": "2022-01-30T22:16:20.217Z", - "iopub.status.idle": "2022-01-30T22:16:20.779Z", - "shell.execute_reply": "2022-01-30T22:16:20.771Z" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "## L_iso\n", - "\n", - "Provides the elastic stiffness tensor for an isotropic material.\n", - "The two first arguments are a couple of elastic properties. The third argument specifies which couple has been provided and the nature and order of coefficients.\n", - "Exhaustive list of possible third argument :\n", - "\u2018Enu\u2019,\u2019nuE,\u2019Kmu\u2019,\u2019muK\u2019, \u2018KG\u2019, \u2018GK\u2019, \u2018lambdamu\u2019, \u2018mulambda\u2019, \u2018lambdaG\u2019, \u2018Glambda\u2019.\n", - "Return a numpy ndarray.\n", - "Example :" - ], - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "E = 70000.0\n", - "nu = 0.3\n", - "L = sim.L_iso(E,nu,\"Enu\")\n", - "print(np.array_str(L, precision=2, suppress_small=True))\n", - "\n", - "d = sim.check_symetries(L)\n", - "print(d['umat_type'])\n", - "print(d['props'])\n", - "\n", - "x = sim.L_iso_props(L)\n", - "print(x)\n", - "\n" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "[[94230.77 40384.62 40384.62 0. 0. 0. ]\n", - " [40384.62 94230.77 40384.62 0. 0. 0. ]\n", - " [40384.62 40384.62 94230.77 0. 0. 0. ]\n", - " [ 0. 0. 0. 26923.08 0. 0. ]\n", - " [ 0. 0. 0. 0. 26923.08 0. ]\n", - " [ 0. 0. 0. 0. 0. 26923.08]]\n", - "ELISO\n", - "[7.e+04 3.e-01]\n", - "[7.e+04 3.e-01]\n" - ] - } - ], - "execution_count": 5, - "metadata": { - "execution": { - "iopub.status.busy": "2022-01-30T22:16:21.673Z", - "iopub.execute_input": "2022-01-30T22:16:21.680Z", - "iopub.status.idle": "2022-01-30T22:16:21.693Z", - "shell.execute_reply": "2022-01-30T22:16:21.697Z" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "## M_iso\n", - "\n", - "Provides the elastic compliance tensor for an isotropic material.\n", - "The two first arguments are a couple of elastic properties. The third argument specify which couple has been provided and the nature and order of coefficients.\n", - "Exhaustive list of possible third argument :\n", - "\u2018Enu\u2019,\u2019nuE,\u2019Kmu\u2019,\u2019muK\u2019, \u2018KG\u2019, \u2018GK\u2019, \u2018lambdamu\u2019, \u2018mulambda\u2019, \u2018lambdaG\u2019, \u2018Glambda\u2019." - ], - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "E = 70000.0\n", - "nu = 0.3\n", - "M = sim.M_iso(E,nu,\"Enu\")\n", - "print(np.array_str(M, suppress_small=True))\n", - "\n", - "L_inv = np.linalg.inv(M)\n", - "d = sim.check_symetries(L_inv)\n", - "print(d['umat_type'])\n", - "print(d['props'])\n", - "\n", - "x = sim.M_iso_props(M)\n", - "print(x)\n" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "scrolled": true, - "execution": { - "iopub.status.busy": "2022-01-29T14:52:11.807Z", - "iopub.execute_input": "2022-01-29T14:52:11.811Z", - "iopub.status.idle": "2022-01-29T14:52:11.821Z", - "shell.execute_reply": "2022-01-29T14:52:11.966Z" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "## L_cubic\n", - "\n", - "Provides the elastic stiffness tensor for a cubic material. Arguments are the stiffness coefficients C11, C12 and C44, or the elastic constants E, nu, G\n", - "Exhaustive list of possible third argument : \u2018Cii\u2019,\u2019EnuG, the by-default argument is 'Cii'" - ], - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "E = 70000.0\n", - "nu = 0.3\n", - "G = 23000.0\n", - "L = sim.L_cubic(E,nu,G,\"EnuG\")\n", - "print(np.array_str(L, precision=2, suppress_small=True))\n", - "\n", - "d = sim.check_symetries(L)\n", - "print(d['umat_type'])\n", - "print(d['props'])\n", - "\n", - "x = sim.L_cubic_props(L)\n", - "print(x)\n", - "\n", - "C11 = np.random.uniform(10000., 100000.)\n", - "C12 = np.random.uniform(10000., 100000.)\n", - "C44 = np.random.uniform(10000., 100000.)\n", - "Liso = sim.L_cubic(C11,C12,C44, \"Cii\")\n" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "[[94230.77 40384.62 40384.62 0. 0. 0. ]\n", - " [40384.62 94230.77 40384.62 0. 0. 0. ]\n", - " [40384.62 40384.62 94230.77 0. 0. 0. ]\n", - " [ 0. 0. 0. 23000. 0. 0. ]\n", - " [ 0. 0. 0. 0. 23000. 0. ]\n", - " [ 0. 0. 0. 0. 0. 23000. ]]\n", - "ELCUB\n", - "[7.0e+04 3.0e-01 2.3e+04]\n", - "[7.0e+04 3.0e-01 2.3e+04]\n" - ] - } - ], - "execution_count": 7, - "metadata": { - "execution": { - "iopub.status.busy": "2022-01-30T21:32:38.578Z", - "iopub.execute_input": "2022-01-30T21:32:38.589Z", - "iopub.status.idle": "2022-01-30T21:32:38.607Z", - "shell.execute_reply": "2022-01-30T21:32:38.613Z" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "## M_cubic\n", - "\n", - "Provides the elastic compliance tensor for a cubic material. Arguments are the stiffness coefficients C11, C12 and C44, or the elastic constants E, nu, G\n", - "Exhaustive list of possible third argument : \u2018Cii\u2019,\u2019EnuG, the by-default argument is 'Cii'" - ], - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "E = 70000.0\n", - "nu = 0.3\n", - "G = 23000.0\n", - "M = sim.M_cubic(E,nu,G,\"EnuG\")\n", - "print(np.array_str(M, suppress_small=True))\n", - "\n", - "L = np.linalg.inv(M)\n", - "d = sim.check_symetries(L)\n", - "print(d['umat_type'])\n", - "print(d['props'])\n", - "\n", - "x = sim.L_cubic_props(L)\n", - "print(x)" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "execution": { - "iopub.status.busy": "2022-01-29T14:52:11.847Z", - "iopub.execute_input": "2022-01-29T14:52:11.851Z", - "iopub.status.idle": "2022-01-29T14:52:11.860Z", - "shell.execute_reply": "2022-01-29T14:52:11.974Z" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "## L_isotrans\n", - "\n", - "Provides the elastic stiffness tensor for an isotropic transverse material.\n", - "Arguments are longitudinal Young modulus EL, transverse young modulus, Poisson\u2019s ratio for loading along the longitudinal axis nuTL, Poisson\u2019s ratio for loading along the transverse axis nuTT, shear modulus GLT and the axis of symmetry." - ], - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "EL = 70000.0\n", - "ET = 20000.0\n", - "nuTL = 0.08\n", - "nuTT = 0.3\n", - "GLT = 12000.0\n", - "axis = 3\n", - "L = sim.L_isotrans(EL,ET,nuTL,nuTT,GLT,axis)\n", - "print(np.array_str(L, precision=2, suppress_small=True))\n", - "\n", - "d = sim.check_symetries(L)\n", - "print(d['umat_type'])\n", - "print(d['axis'])\n", - "print(np.array_str(d['props'], precision=2, suppress_small=True))\n", - "\n", - "x = sim.L_isotrans_props(L,axis)\n", - "print(np.array_str(x, precision=2))" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "execution": { - "iopub.status.busy": "2022-01-29T14:52:11.866Z", - "iopub.execute_input": "2022-01-29T14:52:11.870Z", - "iopub.status.idle": "2022-01-29T14:52:11.880Z", - "shell.execute_reply": "2022-01-29T14:52:11.977Z" - } - } - }, - { - "cell_type": "markdown", - "source": [ - " bp::def(\"L_iso\", L_iso);\n", - " bp::def(\"M_iso\", M_iso);\n", - " bp::def(\"L_cubic\", L_cubic);\n", - " bp::def(\"M_cubic\", M_cubic);\n", - " bp::def(\"L_ortho\", L_ortho);\n", - " bp::def(\"M_ortho\", M_ortho);\n", - " bp::def(\"L_isotrans\", L_isotrans);\n", - " bp::def(\"M_isotrans\", M_isotrans);\n", - " \n", - " bp::def(\"check_symetries\", check_symetries);\n", - " bp::def(\"L_iso_props\", L_iso_props);\n", - " bp::def(\"M_iso_props\", M_iso_props);\n", - " bp::def(\"L_isotrans_props\", L_isotrans_props);\n", - " bp::def(\"M_isotrans_props\", M_isotrans_props);\n", - " bp::def(\"L_cubic_props\", L_cubic_props);\n", - " bp::def(\"M_cubic_props\", M_cubic_props);\n", - " bp::def(\"L_ortho_props\", L_ortho_props);\n", - " bp::def(\"M_ortho_props\", M_ortho_props);\n", - " bp::def(\"M_aniso_props\", M_aniso_props);" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## M_isotrans\n", - "\n", - "Provides the elastic compliance tensor for an isotropic transverse material.\n", - "Arguments are longitudinal Young modulus EL, transverse young modulus, Poisson\u2019s ratio for loading along the longitudinal axis nuTL, Poisson\u2019s ratio for loading along the transverse axis nuTT, shear modulus GLT and the axis of symmetry." - ], - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "EL = 70000.0\n", - "ET = 20000.0\n", - "nuTL = 0.08\n", - "nuTT = 0.3\n", - "GLT = 12000.0\n", - "axis = 3\n", - "M = sim.M_isotrans(EL,ET,nuTL,nuTT,GLT,axis)\n", - "print(np.array_str(M, suppress_small=True))\n", - "\n", - "x = sim.M_isotrans_props(M,axis)\n", - "print(np.array_str(x, precision=2, suppress_small=True))" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "execution": { - "iopub.status.busy": "2022-01-29T14:52:11.885Z", - "iopub.execute_input": "2022-01-29T14:52:11.890Z", - "iopub.status.idle": "2022-01-29T14:52:11.899Z", - "shell.execute_reply": "2022-01-29T14:52:11.981Z" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "## L_ortho\n", - "\n", - "Provides the elastic stiffness tensor for an orthotropic material.\n", - "Arguments are either (convention 'EnuG'):\n", - "\n", - "1. The Young modulus of axis 1 $E_1$,\n", - "2. The Young modulus of axis 2 $E_2$,\n", - "3. The Young modulus of axis 3 $E_3$,\n", - "4. The Poisson ratio of direction 1 with respect to 2 $\\nu_{12}$,\n", - "5. The Poisson ratio of direction 1 with respect to 3 $\\nu_{13}$,\n", - "6. The Poisson ratio of direction 2 with respect to 3 $\\nu_{13}$,\n", - "7. The shear modulus of direction 1 with respect to 2 $G_{12}$,\n", - "8. The shear modulus of direction 1 with respect to 3 $G_{13}$,\n", - "9. The shear modulus of direction 2 with respect to 3 $G_{23}$,\n", - "10. The axial coefficient of thermal expansion in direction 1 $\\alpha_1$,\n", - "11. The axial coefficient of thermal expansion in direction 1 $\\alpha_2$,\n", - "12. The axial coefficient of thermal expansion in direction 1 $\\alpha_3$,\n", - "\n", - "or the list of Cii (C11, C12, C13, C22, C23, C33, C44, C55, C66) (convention 'Cii')" - ], - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "E_1 = 4500.0\n", - "E_2 = 2300.0\n", - "E_3 = 2700.0\n", - "nu_12 = 0.06\n", - "nu_13 = 0.08\n", - "nu_23 = 0.3\n", - "G_12 = 2200.0\n", - "G_13 = 2100.0\n", - "G_23 = 2400.0\n", - "\n", - "L = sim.L_ortho(E_1,E_2,E_3,nu_12,nu_13,nu_23,G_12,G_13,G_23,'EnuG')\n", - "print(np.array_str(L, precision=2, suppress_small=True))\n", - "\n", - "d = sim.check_symetries(L)\n", - "print(d['umat_type'])\n", - "print(d['axis'])\n", - "print(np.array_str(d['props'], precision=2, suppress_small=True))\n", - "\n", - "x = sim.L_ortho_props(L)\n", - "print(np.array_str(x, precision=2, suppress_small=True))" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "execution": { - "iopub.status.busy": "2022-01-29T14:52:11.906Z", - "iopub.execute_input": "2022-01-29T14:52:11.911Z", - "iopub.status.idle": "2022-01-29T14:52:11.923Z", - "shell.execute_reply": "2022-01-29T14:52:11.984Z" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "## M_ortho\n", - "\n", - "Provides the elastic compliance tensor for an orthotropic material.\n", - "Arguments are either (convention 'EnuG'):\n", - "\n", - "1. The Young modulus of axis 1 $E_1$,\n", - "2. The Young modulus of axis 2 $E_2$,\n", - "3. The Young modulus of axis 3 $E_3$,\n", - "4. The Poisson ratio of direction 1 with respect to 2 $\\nu_{12}$,\n", - "5. The Poisson ratio of direction 1 with respect to 3 $\\nu_{13}$,\n", - "6. The Poisson ratio of direction 2 with respect to 3 $\\nu_{13}$,\n", - "7. The shear modulus of direction 1 with respect to 2 $G_{12}$,\n", - "8. The shear modulus of direction 1 with respect to 3 $G_{13}$,\n", - "9. The shear modulus of direction 2 with respect to 3 $G_{23}$,\n", - "10. The axial coefficient of thermal expansion in direction 1 $\\alpha_1$,\n", - "11. The axial coefficient of thermal expansion in direction 1 $\\alpha_2$,\n", - "12. The axial coefficient of thermal expansion in direction 1 $\\alpha_3$,\n", - "\n", - "or the list of Cii (C11, C12, C13, C22, C23, C33, C44, C55, C66) (convention 'Cii')" - ], - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "E_1 = 4500.0\n", - "E_2 = 2300.0\n", - "E_3 = 2700.0\n", - "nu_12 = 0.06\n", - "nu_13 = 0.08\n", - "nu_23 = 0.3\n", - "G_12 = 2200.0\n", - "G_13 = 2100.0\n", - "G_23 = 2400.0\n", - "\n", - "M = sim.M_ortho(E_1,E_2,E_3,nu_12,nu_13,nu_23,G_12,G_13,G_23,'EnuG')\n", - "print(np.array_str(M, suppress_small=True))\n", - "\n", - "x = sim.M_ortho_props(M)\n", - "print(np.array_str(x, precision=4, suppress_small=True))" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "execution": { - "iopub.status.busy": "2022-01-29T14:52:11.929Z", - "iopub.execute_input": "2022-01-29T14:52:11.933Z", - "iopub.status.idle": "2022-01-29T14:52:11.943Z", - "shell.execute_reply": "2022-01-29T14:52:11.987Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "L = sim.L_iso(70000.0, 0.3,\"Enu\");\n", - "Eel = np.random.uniform(0., 1., 6);\n", - "ndi = 3\n", - "sigma = sim.el_pred(L, Eel, ndi)\n" - ], - "outputs": [], - "execution_count": 6, - "metadata": { - "execution": { - "iopub.status.busy": "2022-01-30T22:53:01.061Z", - "iopub.execute_input": "2022-01-30T22:53:01.071Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "E = 70000.0\n", - "nu = 0.3\n", - "L = sim.L_iso(E,nu,\"Enu\")" - ], - "outputs": [], - "execution_count": 9, - "metadata": { - "execution": { - "iopub.status.busy": "2022-01-31T09:10:23.908Z", - "iopub.execute_input": "2022-01-31T09:10:23.915Z", - "iopub.status.idle": "2022-01-31T09:10:23.927Z", - "shell.execute_reply": "2022-01-31T09:10:23.935Z" - } - } - }, - { - "cell_type": "code", - "source": [], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "name": "python", - "version": "3.8.12", - "mimetype": "text/x-python", - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "pygments_lexer": "ipython3", - "nbconvert_exporter": "python", - "file_extension": ".py" - }, - "nteract": { - "version": "0.28.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/Notebooks/Umats/Mechanical/Elasticity/ELISO.ipynb b/Notebooks/Umats/Mechanical/Elasticity/ELISO.ipynb deleted file mode 100644 index 215fbc7c7..000000000 --- a/Notebooks/Umats/Mechanical/Elasticity/ELISO.ipynb +++ /dev/null @@ -1,168 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# Isotropic linear elasticity" - ], - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "%matplotlib inline\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import simcoon as sim\n", - "import os\n", - "dir = os.path.dirname(os.path.realpath('__file__'))" - ], - "outputs": [], - "execution_count": 1, - "metadata": { - "collapsed": false, - "execution": { - "iopub.status.busy": "2021-08-31T16:06:28.050Z", - "iopub.execute_input": "2021-08-31T16:06:28.055Z", - "iopub.status.idle": "2021-08-31T16:06:28.336Z", - "shell.execute_reply": "2021-08-31T16:06:28.330Z" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "In thermoelastic isotropic materials three parameters are required: \n", - " \n", - "1. The Young modulus $E$,\n", - "2. The Poisson ratio $\\nu$,\n", - "3. The coefficient of thermal expansion $\\alpha$.\n", - "\n", - "The elastic stiffness tensor and the thermal expansion coefficients tensor are written in the Voigt notation formalism as\n", - "\n", - "$$\\boldsymbol{L}=\\left(\\begin{matrix} L_{1111} & L_{1122} & L_{1122} & 0 & 0 & 0 \\\\ L_{1122} & L_{1111} & L_{1122} & 0 & 0 & 0 \\\\ L_{1122} & L_{1122} & L_{1111} & 0 & 0 & 0 \\\\ 0 & 0 & 0 & L_{1212} & 0 & 0 \\\\ 0 & 0 & 0 & 0 & L_{1212} & 0 \\\\ 0 & 0 & 0 & 0 & 0 & L_{1212} \\end{matrix}\\right), \\quad \\boldsymbol{\\alpha}=\\left(\\begin{matrix} \\alpha & 0 & 0 \\\\ 0 & \\alpha & 0 \\\\ 0 & 0 & \\alpha \\end{matrix}\\right),$$\n", - "\n", - "with \n", - "$$L_{1111}=\\frac{E(1-\\nu)}{(1+\\nu)(1-2\\nu)}, \\quad L_{1122}=\\frac{E\\nu}{(1+\\nu)(1-2\\nu)}, \\quad L_{1212}=\\frac{E}{2(1+\\nu)}.$$\n", - "\n", - "Details on the the elastic stiffness tensor of isotropic media can be found in Lai et al 2010. The tangent stiffness tensor in this case is $\\boldsymbol{L}^t=\\boldsymbol{L}$. Moreover, the increment of the elastic strain is given by\n", - "\n", - "$$\\Delta\\varepsilon^{\\textrm{el}}_{ij}=\\Delta\\varepsilon^{\\textrm{tot}}_{ij}-\\alpha\\Delta T\\delta_{ij},$$\n", - "\n", - "where $\\delta_{ij}$ implies the Kronecker delta operator. In the 1D case only one component of stress is computed, through the relation \n", - "\n", - "$$\\sigma^{\\textrm{fin}}_{11}=\\sigma^{\\textrm{init}}_{11}+E\\Delta\\varepsilon^{\\textrm{el}}_{11}.$$\n", - "\n", - "In the plane stress case only three components of stress are computed, through the relations \n", - "\n", - "$$\\left(\\begin{matrix} \\sigma^{\\textrm{fin}}_{11} \\\\ \\sigma^{\\textrm{fin}}_{22} \\\\ \\sigma^{\\textrm{fin}}_{12} \\end{matrix}\\right) =\\left(\\begin{matrix} \\sigma^{\\textrm{init}}_{11} \\\\ \\sigma^{\\textrm{init}}_{22} \\\\ \\sigma^{\\textrm{init}}_{12} \\end{matrix}\\right)+\\frac{E}{1-\\nu^2} \\left(\\begin{matrix} 1 & \\nu & 0 \\\\ \\nu & 1 & 0 \\\\ 0 & 0 & \\frac{1-\\nu}{2} \\end{matrix}\\right) \\left(\\begin{matrix} \\Delta\\varepsilon^{\\textrm{el}}_{11} \\\\ \\Delta\\varepsilon^{\\textrm{el}}_{22} \\\\ 2\\Delta\\varepsilon^{\\textrm{el}}_{12} \\end{matrix}\\right).$$\n", - "\n", - "In the generalized plane strain/3D analysis case the stress tensor is computed through the relation\n", - "$$\\sigma^{\\textrm{fin}}_{ij}=\\sigma^{\\textrm{init}}_{ij}+L_{ijkl}~\\Delta\\varepsilon^{\\textrm{el}}_{kl}.$$" - ], - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "umat_name = 'ELISO' #This is the 5 character code for the elastic-isotropic subroutine\n", - "nstatev = 1 #The number of scalar variables required, only the initial temperature is stored here\n", - "\n", - "E = 700000.\n", - "nu = 0.2\n", - "alpha = 1.E-5\n", - "\n", - "psi_rve = 0.\n", - "theta_rve = 0.\n", - "phi_rve = 0.\n", - "solver_type = 0\n", - "corate_type = 2\n", - "\n", - "props = np.array([E, nu, alpha])\n", - "\n", - "path_data = 'data'\n", - "path_results = 'results'\n", - "pathfile = 'path.txt'\n", - "outputfile = 'results_ELISO.txt'\n", - "\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, corate_type, path_data, path_results, pathfile, outputfile)\n", - "\n", - "outputfile_macro = dir + '/' + path_results + '/results_ELISO_global-0.txt'\n", - "\n", - "fig = plt.figure()\n", - "\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(outputfile_macro, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "plt.grid(True)\n", - "\n", - "plt.plot(e11,s11, c='blue')\n", - "plt.xlabel('Strain')\n", - "plt.ylabel('Stress (MPa)')\n", - "\n", - "plt.show()\n" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": "
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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "execution_count": 2, - "metadata": { - "collapsed": false, - "execution": { - "iopub.status.busy": "2021-08-31T16:06:28.341Z", - "iopub.execute_input": "2021-08-31T16:06:28.346Z", - "iopub.status.idle": "2021-08-31T16:06:28.496Z", - "shell.execute_reply": "2021-08-31T16:06:28.503Z" - } - } - }, - { - "cell_type": "code", - "source": [], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true - } - }, - { - "cell_type": "code", - "source": [], - "outputs": [], - "execution_count": null, - "metadata": {} - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "name": "python", - "version": "3.7.10", - "mimetype": "text/x-python", - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "pygments_lexer": "ipython3", - "nbconvert_exporter": "python", - "file_extension": ".py" - }, - "nteract": { - "version": "0.28.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/Notebooks/Umats/Mechanical/Elasticity/ELITR.ipynb b/Notebooks/Umats/Mechanical/Elasticity/ELITR.ipynb deleted file mode 100644 index 05f3bc01f..000000000 --- a/Notebooks/Umats/Mechanical/Elasticity/ELITR.ipynb +++ /dev/null @@ -1,180 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# Transverse isotropy linear elasticity" - ], - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "%matplotlib inline\n", - "\n", - "import pylab\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import simcoon as sim\n", - "import math\n", - "import os\n", - "dir = os.path.dirname(os.path.realpath('__file__'))" - ], - "outputs": [], - "execution_count": 1, - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "In thermoelastic transversely isotropic materials eight parameters are required: \n", - "\n", - "1. The axis of transverse isotropy (1,2 or 3),\n", - "2. The axial Young modulus $E_L$,\n", - "3. The transverse Young modulus $E_T$,\n", - "4. The axial Poisson ratio $\\nu_{TL}$,\n", - "5. The transverse Poisson ratio $\\nu_{TT}$,\n", - "6. The axial shear modulus $G_{LT}$,\n", - "7. The axial coefficient of thermal expansion $\\alpha_L$,\n", - "8. The transverse coefficient of thermal expansion $\\alpha_T$.\n", - "\n", - "When the axis of transverse isotropy is 1, the elastic stiffness tensor and the thermal expansion coefficients tensor are written in SMART+ formalism as\n", - "\n", - "$$\\boldsymbol{L}=\\left(\\begin{matrix} L_{1111} & L_{1122} & L_{1122} & 0 & 0 & 0 \\\\ L_{1122} & L_{2222} & L_{2233} & 0 & 0 & 0 \\\\ L_{1122} & L_{2233} & L_{2222} & 0 & 0 & 0 \\\\ 0 & 0 & 0 & L_{1212} & 0 & 0 \\\\ 0 & 0 & 0 & 0 & L_{1212} & 0 \\\\ 0 & 0 & 0 & 0 & 0 & L_{2323} \\end{matrix}\\right), \\quad \\boldsymbol{\\alpha}=\\left(\\begin{matrix} \\alpha_L & 0 & 0 \\\\ 0 & \\alpha_T & 0 \\\\ 0 & 0 & \\alpha_T \\end{matrix}\\right),$$\n", - "\n", - "where $$\\begin{array}{c}\\displaystyle{L_{1111}=\\frac{E_L}{\\omega}~(\\nu^2_{TT}-1), \\quad L_{1122}=-\\frac{E_L}{\\omega}~\\nu_{TL}~(1+\\nu_{TT}), \\quad L_{2222}=\\frac{E_L~\\nu^2_{TL}-E_T}{\\omega},} \\\\ \\displaystyle{L_{2233}=-\\frac{E_L~\\nu^2_{TL}+E_T~\\nu_{TT}}{\\omega}, \\quad L_{1212}=G_{LT}, \\quad L_{2323}=\\frac{E_T}{2(1+\\nu_{TT})},}\\\\ \\displaystyle{\\omega=\\frac{1}{E_T}(1+\\nu_{TT})~(2E_L~\\nu^2_{TL}+E_T~(\\nu_{TT}-1)).}\\end{array}$$\n", - "\n", - "Details on the elastic stiffness tensor of transversely isotropic media can be found in Christensen (1979). For axis of transverse isotropy being 2 or 3, the above tensors are properly rotated. The tangent stiffness tensor in this case is $\\boldsymbol{L}^t=\\boldsymbol{L}$. Moreover, the increment of the elastic strain is given by \n", - "\n", - "$$\\Delta\\varepsilon^{\\textrm{el}}_{ij}=\\Delta\\varepsilon^{\\textrm{tot}}_{ij}-\\alpha_{ij}\\Delta T.$$\n", - "\n", - "In the 1D case only one component of stress is computed, through the relation \n", - "\n", - "$$\\sigma^{\\textrm{fin}}_{11}=\\sigma^{\\textrm{init}}_{11}+L_{1111}\\Delta\\varepsilon^{\\textrm{el}}_{11}.$$\n", - "\n", - "In the plane stress case only three components of stress are computed, through the relations \n", - "\n", - "$$\\left(\\begin{matrix} \\sigma^{\\textrm{fin}}_{11} \\\\ \\sigma^{\\textrm{fin}}_{22} \\\\ \\sigma^{\\textrm{fin}}_{12} \\end{matrix}\\right) =\\left(\\begin{matrix} \\sigma^{\\textrm{init}}_{11} \\\\ \\sigma^{\\textrm{init}}_{22} \\\\ \\sigma^{\\textrm{init}}_{12} \\end{matrix}\\right)+\\boldsymbol{K} \\left(\\begin{matrix} \\Delta\\varepsilon^{\\textrm{el}}_{11} \\\\ \\Delta\\varepsilon^{\\textrm{el}}_{22} \\\\ 2\\Delta\\varepsilon^{\\textrm{el}}_{12} \\end{matrix}\\right),$$\n", - "\n", - "with $$\\boldsymbol{K}=\\left(\\begin{matrix} \\displaystyle{L_{1111}-\\frac{L_{1133}L_{3311}}{L_{3333}}} & \\displaystyle{L_{1122}-\\frac{L_{1133}L_{3322}}{L_{3333}}} & \\displaystyle{L_{1112}-\\frac{L_{1133}L_{3312}}{L_{3333}}} \\\\ \\displaystyle{L_{2211}-\\frac{L_{2233}L_{3311}}{L_{3333}}} & \\displaystyle{L_{2222}-\\frac{L_{2233}L_{3322}}{L_{3333}}} & \\displaystyle{L_{2212}-\\frac{L_{2233}L_{3312}}{L_{3333}}} \\\\ \\displaystyle{L_{1211}-\\frac{L_{1233}L_{3311}}{L_{3333}}} & \\displaystyle{L_{1222}-\\frac{L_{1233}L_{3322}}{L_{3333}}} & \\displaystyle{L_{1212}-\\frac{L_{1233}L_{3312}}{L_{3333}}} \\end{matrix}\\right).$$\n", - "\n", - "In the generalized plane strain/3D analysis case the stress tensor is computed through the relation\n", - "\n", - "$$\\sigma^{\\textrm{fin}}_{ij}=\\sigma^{\\textrm{init}}_{ij}+L_{ijkl}~\\Delta\\varepsilon^{\\textrm{el}}_{kl}.$$" - ], - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "umat_name = 'ELIST' #This is the 5 character code for the elastic transversely isotropic subroutine\n", - "nstatev = 1 #The number of scalar variables required, only the initial temperature is stored here\n", - "\n", - "axis = 1\n", - "E_L = 4500\n", - "E_T = 2300\n", - "nu_TL = 0.05\n", - "nu_TT = 0.3\n", - "G_LT = 2700\n", - "alpha_L = 1.E-5\n", - "alpha_T = 2.5E-5\n", - "\n", - "psi_rve = 0.\n", - "theta_rve = 0.\n", - "phi_rve = 0.\n", - "solver_type = 0\n", - "corate_type = 2\n", - "\n", - "props = np.array([axis, E_L, E_T, nu_TL, nu_TT, G_LT, alpha_L, alpha_T])\n", - "path_data = 'data'\n", - "path_results = 'results'\n", - "\n", - "pathfile = '/path_1.txt'\n", - "outputfile_1 = 'results_ELIST_1.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, corate_type, path_data, path_results, pathfile, outputfile_1)\n", - "outputfile_1 = dir + '/' + path_results + '/results_ELIST_1_global-0.txt'\n", - "\n", - "pathfile = '/path_2.txt'\n", - "outputfile_2 = 'results_ELIST_2.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, corate_type, path_data, path_results, pathfile, outputfile_2)\n", - "outputfile_2 = dir + '/' + path_results + '/results_ELIST_2_global-0.txt'\n", - "\n", - "fig = plt.figure()\n", - "pylab.rcParams['figure.figsize'] = (16.0, 5.0) #configure the figure output size\n", - "\n", - "ax = fig.add_subplot(1, 3, 1)\n", - "\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(outputfile_1, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('Strain', size = 15)\n", - "plt.ylabel('Stress (MPa)', size = 15)\n", - "plt.plot(e11,s11, c='black', label='direction 1')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(1, 3, 2)\n", - "\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('Strain', size = 15)\n", - "plt.ylabel('Stress (MPa)')\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(outputfile_2, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "plt.plot(e22,s22, c='red', label='direction 2')\n", - "\n", - "plt.legend(loc=2)\n", - "\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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r+/aFF8I774TNkyAqFEUkMjt37qRZs2YAXH/99Tz99NOBE4mIRODAAahTx7c7doQPPwybJ4FUKIpIJHbt2kWj+BBMnz59ePXVVwMnEhGJwMGDULOmb7dsCf/+d9g8CaZCUUQSbu/evdSPD8FcdNFFzJgxI3AiEZEI5OVB9eq+XaeOnxInzahQFJGE2r9/P3XiQzCdO3dm3rx5YQOJiETBOahaaJbBPXvCZYmQCkURSZiDBw9Sq1YtAFq1asXSpUsDJxIRiYBzUKVQCRWLhcsSMRWKIpIQeXl5VI8PwdSrV48NGzYETiQiEpGiRaJZuCwRU6EoIuUWi8WoWmgIZteuXQHTiIhEKP/uZoDc3LQuEkGFooiUk3OOjIyMguexNB6CEZFKrmVLP18iQE4OFOr70pUKRREplyqFhmBisRiW5t+uRaSS6tgRNm3y7f37oVq1sHkqiApFETlqtWvXLmjn5uaqSBSR9HThhbBihW/v3Qs1aoTNU4FUKIrIUWnevDnZ2dkA5OTkHDL8LCKSNq68Ej76yLd37YJCX5ArAxWKIlJmHTp04IsvvgD8vInVKskQjIhUMgMHwnvv+faOHVCvXtg8AahQFJEy6d69OytXrgT8Ciw1KtEQjIhUIrfcAq+/7ttbtkDDhmHzBJK0haKZXWdmS8wsy8w2m9nLZtasyD5mZvea2X/MLNvMPjSzLqEyi6S73r17s3DhQsBPgVO7kg3BREF9nUgSGjkSnn/etzdsgOOPD5snoKQsFM3su8DrwELgKuBuoAfwnpkVznwPcD8wDugDZAGzzazy/o2KROS6665j5syZAOzcuZN6lXAIJtHU14kkoTFj4LHHfHvNGmjVKmyewKqWvEsQA4Elzrmf5r9gZruBPwEnA5+aWU185znWOTcpvs/fgM+BnwKjKjq0SLoaMmQIb7zxBgBffvklDRo0CJwobaivE0kmjz8Ov/iFb69YAW3bhs2TBJLyjCJQDSi6tMNX8T/z5984H6gHTMvfwTm3F3gH+E7UAUUqixEjRvDCCy8AsHHjRpo2bRo4UVpRXyeSLCZPhjvv9O3Fi+G008LmSRLJWii+BFxoZjeYWT0zaw/8EpjjnFsZ3+cUIA9YU+RnP41vE5FyGj16NOPHjwdg7dq1tGzZMnCitKO+TiQZTJ0KN9/s2wsWwJlnhs2TRJKyUHTOvQf8EJiM/7a9GsgArim023FAlnMur8iPZwK1zax6BUQVSVvjx4/nwQcfBGDFihW0adMmbKA0pL5OJAlMnw4/+IFvf/ABnH9+2DxJJimvUTSzi4HngCeBmUBT4EFgupn1KqbDLO37DgGGADRt2pR58+aV+mezsrLKtH8qSMdjAh1XIsyYMYMJEyYAMHnyZLZt28a2bdsS/jnp+ndVWurrKpaOLfVEfVzHffwxp999NwD/fuQRdlapAhX0e0yZvzPnXNI9gCXA1CKvnQw4oG/8+VAgF8gost9dwN6SPqNr166uLObOnVum/VNBOh6Tczqu8nr55Zdd/P81t3Dhwkg/K9n+roBPnPq6Mu2fSnRsqSfS45o/3znwjzfeiO5zDiPk31lZ+rqkHHrGX3fzr8IvOOdWA9lA/vjXKvwQTdFbkk6JbxORMnrrrbe44YYbAJgzZw7nnXde4ERpT32dSAgffwwXXeTbv/0tXHtt2DxJLFkLxQ3AIVeSmtmpQC38lBDg5x3bDfQvtE9t/BxjMyskpUgamTlzJv369QPg3Xff5eKLLw6cqFJQXydS0ZYtg3PO8e1Jk+CHPwwaJ9kl5TWK+Gt2JpjZF3x93c4D+I7zzwDOuf1m9ihwv5ll4r9Z34EvfieGCC2SqubNm0fv3r0BmDZtGldccUXgRJWG+jqRirRqFZx+um8/+ij85Cdh86SAZC0UnwJygFuBW/Dzin0E/Nz5+cPyPYrvLH8ONAQ+AS5xzm2t2LgiqWvRokUFZw+nTJlC//79S/gJSSD1dSIVZf16OPVU3x41CuI3sciRJWWhGL/Q8tn4o6T9Ho4/RKSMli5dyrnnngvA008/zaBBgwInqlzU14lUkC++gG99y7dvvx0eeihsnhSSrNcoikjEVq1aRZcuXQAYN24cQ4cODZxIRCQC27ZB8+a+feON8MQTYfOkGBWKIpXQunXrODU+BHP//fczcuTIwIlERCKQmQn5y45ecw289FLYPClIhaJIJbN58+aCVVaGDx/OmDFjAicSEYlAVhY0aODbvXrBH/4QNk+KUqEoUols27aNFi1aADB48OCC1VdERNJKdjbUrevb3brBX/4SNk8KU6EoUklkZmbSND4E079/f1588cXAiUREIpCTA7Vr+3a7dvCPf4TNk+JUKIpUAnv27KFBfAjm0ksvZdq0aYETiYhEIDcXatTw7UaN4LPPwuZJAyoURdJcdnY29erVA+Dss89m1qxZgROJiEQgFoNq1Xy7ShXYvj1snjShQlEkjeXk5FA7PgRz8skns2jRosCJREQi4BxkZHz9PDc3XJY0o0JRJE3l5uZSIz4E06RJE1atWhU4kYhIBJzzZxDzxWJgFi5PmlGhKJKGYrEY1eJDMNWqVWPrVq30JiJpKn+4GSAvT0VigqlQFEkzzjkyCg3BHDhwIGAaEZEINW7si0OAgwcPPbMoCaHfqEgacc5RpVBHGYvFMH27FpF01K4d7Njh2wcOQNWqYfOkKRWKImmk8JnEvLw8FYkikp7OOgvWrvXtffugevWwedKYCkWRNNGgQQOcc4C/kaWKhmBEJB1dcgl88olv794NtWqFzZPm9C+JSBpo06YNmZmZgL8msfCZRRGRtHHNNTB7tm9nZn69TJ9ERoWiSIrr2rUr69atA2Dfvn1U1xCMiKSjG2+Et9/27W3boH79sHkqCRWKIimsZ8+eLFmyBPDL9NXSEIyIpKPbb4cpU3x70yZ/t7NUCBWKIimqb9++zJkzB4DMzEzq1KkTOJGISARGjYKnnvLt9euhefOweSoZ3UsukoIGDRrE9OnTAdi2bRv1NQQjImmo1WuvwQsv+CerVkHr1kHzVEY6oyiSYoYNG8bLL78MwObNm2msIRgRSUeTJvGt/CJx6VI4+eSweSopFYoiKeS+++5j0qRJAKxfv55mzZoFTiQiEoHf/haGDfPtRYugc+eweSoxDT2LpIipU6fym9/8BoDVq1fTWkMwIpKOpk2DwYMB+OcTT3DG2WcHDlS5qVAUSQETJ04sKBKXLVtG+/btAycSEYnAu+/CgAG+PXMmu2rWDJtHSlcomlkV4DKgF3A2cDxQE/gv8BmwAHjbObcxopwildZLL73EbbfdBsDHH39Mp06dAicSEYnABx9Anz6+/fbbcPnlMG9e0EhSwjWKZlbXzB4ANgF/BC4G1sbbvwP+CtQC7gHWmdn/mtmF0UYWqTzeeOMNfvSjHwHwxBNPcNZZZwVOJCISgYULoVcv3371Vfje98LmkQIlnVH8HFgC3AXMcM7tOdyOZnYGcC3wtpmNds5NSlhKkUpoxowZXHfddQC8//771KhRI3AiEZEILFkC3bv79vPPw/XXh80jhyjprudLnXOXOOemHqlIBHDO/dM593PgRGB2whKKVEKzZ8/mqquuAmD69OlcdtllgROJiERgxQro2tW3x4+HIUPC5pFvOOIZRefc4rK+oXNuH7DqqBOJVHILFizgkksuAfydzldffXXgRCIiEVi7Fjp29O3Ro+GOO8LmkWId1V3P8ZtbvnErUrxIFJGjtHjxYi644AIAJk+ezMCBAwMnEhGJwMaN0K6db991FzzwQNg8clilnnDbvLvNbC1wENhTzENEjtLy5cvp1q0bABMmTOCmm24KnEhEJAJffgknnujbN98Mv/pV2DxyRGVZmeU2/N3NLwIGPAyMwU+P8zmgCwtEjtKaNWsKpr156KGHGD58eOBEIiIR2LkTTjjBtwcOhOeeC5tHSlSWQvEm4BdAfun/R+fcaKAD/prEdgnOJlIpbNiwoWAC7bvvvptRo0YFTiQiEoFdu6BRI9++4gqYOjVsHimVshSKJwH/cs7l4Yee6wM452LAM8CgxMcTSW9btmwpWIpv6NChPProo2EDiYhEYe9eqF/fty+4wK/AIimhLIXiTqBOvL0ROKPQtuPwE2+LSCnt2LGDZs2aAXD99dfz9NNPB04kIhKBAwegTrx86NgR/vrXsHmkTMpy1/MC4Czgz8BrwINm1gDIAX4CfJD4eCLpadeuXTRu3BiAPn368OqrrwZOJCISgYMHIX+95hYt4N//DptHyqwsheKDQPN4+xH80PMP8WcS/wIMS2QwkXS1d+9e6seHYC666CJmzJgROJGISATy8qB6dd+uUwf+85+weeSolLpQdM6tBlbH2weA2+MPESml/fv3Uyc+BNO5c2fmacF7EUlHzkHVQiXGHs2gl6pKvEbRzI43szvNbKKZ3WdmnSsimJlVNbN7zGyNmR0ws01mNqHIPmZm95rZf8ws28w+NLMuFZFPpKwOHjxIrVr+Ut5WrVqxdOnSwIkkGaivk7TjHFQpVF7EYuGySLkd8YyimZ0BzAHqAduBBsAvzOxG51zU97VPAb4NjMZPv9MSOK3IPvcA9wN3xfe5A5htZh2dc19GnE+k1PLy8qgeH4KpV68eGzZsCJxISsPMagJXAhcCzYBsYDnwnnNuRYI+Zgrq6ySdFC0SzcJlkXIraeh5LLAOuMo5t8nM6gK/AR4HIisUzexyYABwunNu5WH2qYnvPMc65ybFX/sbfvLvnwKajE6SQiwWo2qhIZhdu3YFTCOlZWaj8UXiPGARsA2/dGl74NF4H3Snc25ZOT5DfZ2kl/y7mwFyc1UkpoGSCsUzgCHOuU0Azrk9ZjYC2GBmLZ1zUV2ZOhiYc7iOM+58/JnOafkvOOf2mtk7wHdQ5ylJwDlHRkZGwfOYhmBSycfOuV8cZtvjZtYEaFXOz1BfJ+mjZUs/XyJATg4U6vskdZV0jWJjYEuR176I/9ko8XEKnAN8ZmaTzGy3me0zs7fNrFmhfU4B8oA1RX720/g2keCqFBqCicVimL5dpwzn3HslbN/mnPuknB+jvk7SQ6dOsGmTb2dnQ7VqYfNIwphz7vAbzWLA/wCFO8OqwFfABcC/Cu/vnNuXkFBmB/DzMy7FT8VTF7904JfAuc45Z2b3AXc55+oX+dkfAy8ANZxzOUW2DSG+JnXTpk27/v73vy91pqysrIK7VdNFOh4TJM9xXX755Rw4cACA2bNnH3Jm8Wgky3ElUrId08UXX7zYOdet8Gtm1hi4G3/dYM38151z3y7v56mvq1g6tmh0ue026sfnR/xw5kxiNWuW8BOlp7+zaBTX1x2Wc+6wDyCG/yZb9FHs60d6r7I88B1nFtCw0Gs9AAf0jD+/D/iqmJ/9cXy/6kf6jK5du7qymDt3bpn2TwXpeEzOJcdxNWvWzMX/O3Q5OTkJec9kOK5ES7ZjAj5x3+xT/hf4Ef4M3kXAS8C4ovsdzUN9XcXSsUXgiiuc8/c5O/fVVwl/e/2dRaO4vu5wj5KuUbyxVNVm4mUC65xzOwu99hG+Uz0NvwpMJlDHzDKcX38633HAPlfkG7ZIRenQoQNffOGv0Ni/fz/VNAST6ho65140s9udc/OB+Wb2jwS9t/o6SV0DB8J78Ss0duyAY48Nm0ciccRC0Tn3u4oKUsSnFBriKcTwZzPBTxGRAbQlPhF43CnxbSIVrnv37qxc6e9L2Lt3LzVq1AicSBLgYPzPLWZ2Bf467QYJem/1dZKabrkFXn/dt7dsgYYNw+aRyJQ44XYg7wKdzKzwDTM9gGr4a3kAFgK7gf75O5hZbaAPMLOCcooU6N27NwsXLgT8FDi1a9cOnEgS5JdmdixwJzACP0XYzxL03urrJPWMHAnPP+/bGzbA8ceHzSORKmnC7ZfK8mbOucHli1NgMnAb8I6Z5V/gPQ6Y7Zz7KP5Z+83sUeB+M8vk60loqwATE5RDpFSuu+46Zs70/2bv3LmTevXqBU4kCfR359wuYBdwcYLfW32dpJYxY+Cxx3x7zRpoVd4ZoiTZlXSN4g+BPfjhjpLm9Tj87dNl5JzbbWbfBp4Cfo+/XudPfPNb/KP4zvLnQEP83dmXOOe2JiqLSEmGDBnCG2/Z2hQAAAAgAElEQVS8AcCXX35JgwaJGpWUkMysD/7GlVwzywOudc4tTORnqK+TlPL44/CL+NSiy5dD27Zh80iFKKlQnIO/y68O8AbwunPus8hTAc65tUDvEvZxwMPxh0iFGzFiBC+88AIAGzdupGnTpoETSQI9DFzonFtlZufgp625KNEfor5OUsLkyXDnnb69eDF06BA2j1SYI16j6JzrBTQHngEuAVaa2RIzu8vMWlZEQJFkNXr0aMaPHw/A2rVradlS/0ukmVzn3CoA59wi/LCwSOUzdSrcfLNvL1gAZ54ZNo9UqJLOKOKc2wZMAiaZ2Yn4dUmvA8aa2SJggnPuD9HGFEku48eP58EHHwRgxYoVtGnTJmwgiUITM7vjcM+dc48HyCRSsaZPhx/8wLdnz4bzzw+bRypcme56ds5tcM79CugFTMAvPzUwimAiyeq5555jxIgRACxZsoTTTjstcCKJyAv4s4j5j6LPRdLbrFnQt69vv/MO9OwZNo8EUeIZxXxmdgxwNf5s4iX4ucR+DYSaa1Gkwr3yyivceuutACxcuJAzzjgjcCKJinNudOgMIsF8+CFcfrlvv/EGXHll2DwSTEnT49QArsAXh1fg13h+E3jYOff36OOJJI+33nqLG264AYA5c+Zw3nnnBU4kUTKzp4603Tl3W0VlEalQH38MF8Xv23rpJbj22rB5JKiSzihuxU+L8w7+2sR5xFcLiE/4egjn3L4E5xNJCjNnzqRfv34AvPvuu1x8caKn05MkdAuwHJiGH0EpaYowkdS3bBmcc45vT5oEN4ZayVeSRUmFYv6swQOB75fi/TLKF0ck+cybN4/evf3sJdOmTeOKK64InEgqyAn41VAGALn4KcL+4Jz7KmgqkaisWgWnn+7bY8fCT34SNo8khZIKRX2VkEpt0aJFBWcPp0yZQv/+/Uv4CUkXzrmdwHPAc2bWAn8Jzkozu9s590rYdCIJtn49nHqqb993H9xzT9g8kjSOWCg653SjilRaS5cu5dxzzwXg6aefZtCgQYETSQhmdiZ+ROUS/NrKi8MmEkmwL76Ab33Lt2+/HX75y7B5JKmU+q5nkcpk1apVdOnSBYBx48YxdOjQwImkopnZGPxNfJ/il9f7uXMuN2wqkQTbvh2aN/ftH/4QnngiaBxJPkecR9HMPjCzXqV9MzNrYmZjzGxY+aOJhLFu3TpOjQ/B3H///YwcOTJwIglkFFAfOB0YCywxs2Vm9m8zWxY2mkgCZGZCkya+fc018Nvfhs0jSamkM4rvAVPNbD/wNrAQfxfgDuAAvhM9CegKfAe/Duos4M6oAotEafPmzQWrrAwfPpwxY8YETiQBnRQ6gEhksrKgQQPf7tUL/qAF1qR4JV2j+LiZPY+/6/kG4Cd8885mA7bgC8m7nHP/iiKoSNS2bdtGixYtABg8eDATJkwInEgC2+icc0fawcyspH1Ekk52NtSNLy7UrRv85S9h80hSK81az3vxS1e9EJ878XTgeKAm8F9gtXPu8yhDikQtMzOTpk2bAtCvXz9efPHFwIkkCcw1s7eAPznnNua/aGbVgQuAQcBcYEqYeCJHIScHasenQW7bFv7xj7B5JOmV6WaW+ITaf4soi0gQe/bsoUF8CObSSy/lzTffDJxIksTlwGDgdTM7Cb8yVU38qMr/Ak845/4ZMJ9I2eTmQo0avt2oEaxZEzaPpATd9SyVWnZ2NvXq+Xnlzz77bGbNmhU4kSQL59x+4BngGTOrBjQCsjXhtqSkWAyqVfPtKlX83c4ipaBCUSqtnJwcaseHYNq3b8+iRYsCJ5Jk5Zw7iL8WWyT1OAcZhW4vyNUsT1J6R5weRyRd5ebmUiM+BNOkSRNWr14dOJGISASc82cQ88ViYFq2XEpPhaJUOrFYjGrxIZiqVauydevWwIlERCKSP9wMkJenIlHKTIWiVCrOOTIKDcHk5OQETCOpwMyOMbMq8XZ7M/tu/JpFkeTWuLEvDgEOHjz0zKJIKZX6vxozu9DMrir0vJGZvWZm/zKz8eo4Jdk556hSqKOMxWKYvl1LyT4EappZc/zdzv8PTYkjya5dO9ixw7cPHICquiVBjk5Zvl78CuhY6PmTQE/g78APgdGJiyWSeIWLxLy8PBWJUloWnxqsL/CMc64/0CFwJpHDO+ssWLvWt/ftg+rVw+aRlFaWQvFkYDFAfOLt7wG3O+duAUYCAxIfTyQx8udJBH8jSxUNwUjpmZmdB1yPX9YUvrlClUhyuOQS+OQT3969G2rVCptHUl5Z/rWsDuyPt7vjp9bJ7zQ/A05IYC6RhGnTpg2ZmZkAHDhw4JBrFEVKYTjwc2C6c26FmX0LvyKLSHK55hqYPdu3//vfr5fpEymHsly0sAq/UsE8/Dfrvznn9sS3NcMv5yeSVLp27cq6desA2LdvH9U1BCNl5JybD8wHiN/UssM5d1vYVCKHOnncOHj/ff9k2zY47riwgSRtlOWM4hjgZ2a2HRgIPFpo2+WAlrKSpNKzZ0+WLFkC+GX6amkIRo5C/Ka9emZ2DLAcWGlmd4XOJVLg9ts5Ib9I3LTJ3+0skiClLhSdczOAU4FbgI7OuZmFNv8NeDjB2USOWt++fZkzZw4AmZmZ1KlTJ3AiSWGnOed2A1cDM4GT8Hc+i4Q3ahQ89ZRvr1sHzZuHzSNpp0z3yzvn1gHrinl9csISiZTTo48+WrBm87Zt26hfv37gRJLiqsWn/7oamOScO2hmLnQoEcaNg4f9OZpFv/sd55x0UuBAko7KMo/iNWb2o0LPTzKzhWb2lZm9ZWb611iCGzZsWEGRuHnzZhprCEbK73ngc+AY4EMzOxHYHTSRyKRJcM89vr10KdmtWoXNI2mrLNcojgLqFXo+EWiEv1bxTDT0LIHdd999TJo0CYD169fTrFmzwIkkHTjnnnLONXfO9XbeBuDi0LmkEvvtb2HYMN9etAg6dw6bR9JaWQrFbwH/BjCzY4FLgZ855x4F7gP6JD6eSOmMHTuWRx55BICXX36Z1q1bhw0kacPMmprZi2Y2M/78NGBQ4FhSWU2bBoMH+/b8+XD22WHzSNor66zD+dflXATkAfEJm9gEaIxPgpg4cSL33nsvAMuWLaNly5aBE0mamQLMwk8DBn7e2OHB0kjl9e67MCC+tsXMmdCjR9g8UimUpVBcClwfnyLix8Bc59yB+LZWwLZEhxMpyUsvvcRtt/kp7T7++GM6deoUOJGkoUbOuWlADMA5l4v/oixScT74APrEB+7efhsuvzxsHqk0ynLX873AO/ghlyzgkkLbrgYWJTCXSIneeOMNfvQjf3/V/PnzOeusswInkjS118waEh9RMbNzgV1hI0mlsnAh9Orl26++Ct/7Xtg8UqmUulB0zn1kZq2A9sD/Oee+KrT5JWBtosOJHM6MGTO47rrrAHj//ffpoSEYic4dwAygjZktwF9m0y9sJKk0liyB7t19+/nn4frrw+aRSqes8yjuARab1wzY5pzLdc79OZp4It80e/ZsrrrqKgCmT5/OZZddFjiRpKv4kn018ddlnwwYsNo5dzBoMKkcVqyArl19e/x4GDIkbB6plMp0M4uZ9TazRcB+YCPQOf76ZDP7QQT5MLPmZpZlZs7M6hR63czsXjP7j5llm9mHZtYligySPBYsWMAll/irHqZOncrVV18dOJGkM+dcDHg6/oV4hXNueVRFovo6OcTatdCxo2+PHg133BE2j1RaZZlw+wb88MsqYEiRn10D/Ki4n0uAx/DXRBZ1D3A/MA4/NU8WMNvMjo8ohwS2ePFiLrjgAgAmT57MwIEDAyeSSuKD+IIDFvHnqK8Tb+NGaNfOt0eMgAceCJtHKrWynFG8D3jMOTcIeLXIthXAaQlLFWdmPYDLgV8Xeb0mvvMc65yb5JybDfTHX2z+00TnkPCWL19Ot27dAJgwYQI33XRT4ERSidwMvAkcMLPdZrbHzBK6Mov6Oinw5Zdw4om+ffPN8NhjYfNIpVeWQvFE4C+H2bafQ1dtKTczy8Cv/jIG2FFk8/nxz5uW/4Jzbi/+ruzvJDKHhLdmzZqCaW8eeughhg/XFHZScZxzdZ1zVZxz1Z1z9eLPE9bfqa+TAjt3wgkn+Pb3vw/PPRc2jwhlKxT/A5xxmG3dSPxdz7cANYCni9l2Cn4eszVFXv80vk3SxIYNG2jfvj0AI0eOZNSoUYETSWVjZh+U5rVyUF8nsHs3NGrk21dcAa+9FjaPSFxZ7np+EfiFmW0F/hh/zcysJzAS/204IeJzlj0E/MA5d7CYS4OOA7Kcc0Unvc0EaptZdedcTqLySBhbtmwpWIrv1ltvZdy4cWEDSaUSH/atDTQys+PwdzyDP8PXPEGfob5OYO9eOPZY3+7e3a/AIpIkylIojgNaAr/j61UJFgIZwPPOuacSmOth4O+JnnbHzIbgb8ShadOmzJs3r9Q/m5WVVab9U0EyH9OuXbsK7mju1asX1157bamzJvNxlUc6HleSH9PN+KX6mgGL+bpQ3A1MStBnqK+rQMl4bJaTw0XxKb6yTjqJT375SziKjMl4bImQrscFKXRszrkyPYA2+A7oXvyQSfuyvkcJ798ByAHOBerHH0PxF283B2rFn+cCGUV+9i5gb2k+p2vXrq4s5s6dW6b9U0GyHtNXX33l4n/frk+fPmX++WQ9rvJKx+NKtmMCPnHf7JOGFX0tEQ/1dRUv6Y4tJ8c58I8WLcr1Vkl3bAmSrsflXNhjK66vO9yjVGcU40MwE4EXnXN/B/6vzBVp6bUDqgF/K2bbJvwQ+Gv4M5ltgdWFtp+Cn75HUtTevXupX78+AD169GDGjBmBE0llZWZnAf9xzk2MP78BuAbYADzonPtvOT9CfV1llpcH1av79jHHwH/+EzaPyGGU6mYW59x+4Dr8CgVR+wi4uMgj/+K03vi5xhbih3/65/+QmdXGzzE2swIySgT2799PnTp+nuHOnTszf/78wImkknsef8Yvf/qaR4GX8es8T07A+6uvq6ycg6qFztNkFTd9pkhyKMs1inPwHdm8aKJ4zrkdRT/DzFrHm391zmXFX3sUuN/MMvHfrO/AF74To8wn0Th48CC1atUCoFWrVixdujRwIhEyCp01HABMds69BbxlZv8q75urr6uknIMqhc7RxGLhsoiUQlkKxaeB35jZMcCfga34a2kKOOdWJjBbSR7Fd5Y/BxoCnwCXOOe2VmAGSYC8vDyqx4dg6tWrx4YNGwInEgEgw8yqOudygZ7Ebw6JK0vfWV7q69JJ0SIx8gV/RMqnLJ3d+/E/74g/CheJFn+ekaBch3DOTQGmFHnN4e8YfDiKz5SKEYvFqFpoCGbXrl0B04gc4nVgvpntALKBvwKYWVv88HPCqa9Lc3XqfN3OzVWRKCmhLIXitylyBlGkPJxzZGR8/d0ipiEYSSLOuYfjE2ufAPxvvGADf3ZvWLhkkpJatvTzJQLk5EBGJOdVRBKu1IWic25ehDmkEqpSaAgmFotRzGTDIkHFZ3ko+tpnIbJICuvUCTZt8u3sbKhWLWwekTIo9RJ+ZpZnZmcfZltXMyu6coDIYdWuXbugnZubqyJRRNLThRfC8uW+nZUFNSti8hCRxCnLWs9H+pe8Gn5SWJESNWvWjOzsbABycnIOGX4WEUkbV14JH33k21995edLFEkxRxx6NrNWQOtCL50Rn3y7sJrAIGB9YqNJOurQoQNbtmwB/LyJ1TQEIyLpaOBAeO89396x4+u1nEVSTEnXKN4I/IL4cmrAs4fZLxv4cQJzSRo6//zzWbnSz6C0d+9eatSoETiRiEgEbrkFXn/dt7dsgYYNw+YRKYeSCsVngD/gh52XAdfH/ywsB9jonDuQ+HiSLnr37s3f/uZXKtu1a9ch1yiKiKSNkSPh+ed9e8MGOP74sHlEyumIhaJzbjuwHcDMTgK2OOdyKiKYpI/rrruOmTP9amM7d+6kXr16gROJiERgzBh47DHfXrMGWrUKm0ckAcoyPU7BchnxtUZ/hF+Y/kvg5cLbRfINGTKEN954A4Avv/ySBg0aBE4kIhKBxx+HX/zCt5cvh7Ztw+YRSZCSbmYZD/RxzrUv9Fpd4B9AOyATOBa408zO1vxiUtiIESN44YUXANi4cSNNmzYNnEhEJAKTJ8Odd/r24sXQoUPYPCIJVNL0OBcDrxZ5bQTQHrjJOdcIaAZ8Dtyf8HSSskaPHs348eMBWLt2LS1btgycSEQkAlOnws03+/aCBXDmmWHziCRYSYVia2BxkdeuAVY6516CgusYxwPdE55OUtL48eN58MEHAVixYgVt2rQJG0hEJArTp8MPfuDbs2fD+eeHzSMSgZIKxarA/vwnZtYAOBWYU2S/zwHd2iU899xzjBgxAoAlS5Zw2mmnBU4kIhKBWbOgb1/fnjEDevYMm0ckIiUVip8B/1Po+ZXxP2cV2a8J8N8EZZIU9corr3DrrbcCsHDhQs4444zAiUREIvDhh3D55b79+99Dnz5h84hEqKS7nicBL5jZscBW4Db8Ciz/W2S/S4HliY8nqeKtt97ihhtuAOCDDz7gvPPOC5xIRCQCH38MF13k2y+9BAMGhM0jErGS5lGcYmYnAD8B6gNLgJ845w7m72NmjYGrgNFRBpXkNXPmTPr16wfAu+++y7e//e3AiUREIrBsGZxzjm9PnAg33hg2j0gFKHEeRefcWGDsEbZvR9cnVlrz5s2jd+/eAEybNo0rrrgicCIRkQisXg2nn+7bY8fCT38aNo9IBSnpGkWRw1q0aBEXX3wxAFOmTKF///6BE4mIRGD9ejjlFN++7z64556weUQqkApFOSpLly7l3HPPBeDpp59m0KBBgROJiETgiy/gW9/y7dtug1/+MmwekQqmQlHKbNWqVXTp0gWAcePGMXTo0MCJREQisH07NG/u2z/8ITz5ZNA4IiGoUJQyWbduHaeeeioA999/PyNHjgycSEQkApmZ0KSJb/ftC7/9bdg8IoGoUJRS27x5c8EqK8OHD2fMmDGBE4mIRCArCxo08O1eveCtt8LmEQlIhaKUyrZt22jRogUAgwcPZsKECYETiYhEIDsb6tb17W7d4C9/CZtHJDAVilKizMxMmjZtCkC/fv148cUXAycSEYlATg7Uru3bbdvCP/4RNo9IElChKEe0Z88eGsSHYC699FLefPPNwIlERCKQmws1avh2o0awZk3YPCJJQoWiHFZ2djb16tUD4Oyzz2bWrKJLfIuIpIFYDKpV8+0qVfzdziICqFCUw8jJyaF2fAimffv2LFq0KHAiEZEIOAcZGV8/z80Nl0UkCalQlG/Izc2lRnwIpkmTJqxevTpwIhGRCDjnzyDmi8XALFwekSSkQlEOEYvFqBYfgqlatSpbt24NnEhEJCL5w80AeXkqEkWKoUJRCjjnyCg0BJOTkxMwjYhIhBo39sUhwMGDh55ZFJEC+j9DAF8kVinUUcZiMUzfrkUkHbVrBzt2+PaBA1C1atg8IklMhaIAHFIk5uXlqUgUkfR01lmwdq1v79sH1auHzSOS5PQ1SgrmSQR/I0sVDcGISBrqPGIELF7sn+zeDbVqhQ0kkgJUEVRybdq0ITMzE4ADBw4cco2iiEjauOYaGuQXif/979fL9InIEalQrMSGDBnCunXrANi3bx/VNQQjIunoxhvh7bd9e+tWOO64sHlEUogKxUqqZ8+erIkvUbVnzx5qaQhGRNLR8OEwZQoAC6dNgyZNwuYRSTEqFCuh733ve8yZMweAzMxM6tSpEziRiEgERo2CJ5/07XXryGncOGwekRSUlIWimfU3sxlmttnMssxssZl9v5j9bjKzNWa2P75PzxB5U8mgQYP44x//CMD06dOpX79+4EQilZf6ugiNGwcPP+zbn34KJ50UNo9IikrKQhG4A8gCfgZ8F5gLvGZmw/J3iHemzwEvA98BVgDvmlnHio+bGoYNG8bLL78MwObNm1UkioSnvi4KTz8N99zj20uXwimnhM0jksKSdXqcPs65HYWezzGzZvhOdWL8tQeB3znnHgIws/nAGcA9wA8qMGtKuPfee5k0aRIA69evp1mzZnz22WeBU4lUeurrEm3KFPjpT33773+Hzp2DxhFJdUl5RrFIx5nvn0AzADP7FtAemFboZ2LAm/hv3FLI2LFjGTt2LACrV6+mdevWYQOJCKC+LuGmTfN3OAPMmwfnnBM0jkg6SMpC8TDOA/JPgeWPI6wqss+nQAMz0xXLcRMnTuTee+8FYOnSpbRv3z5wIhEpgfq6o/HuuzBggG/PnAkXXRQ2j0iaSNah50PEL9y+Ghgcfyl/EqyviuyaWWj79mLeZwgwBKBp06bMmzev1BmysrLKtH8yeO+99/j1r38NwLPPPst///vfQ44hFY+pNHRcqSMdj6k81NcdnfqLF9NlxAgAlo8ezY6aNf0ZxSJS8dhKK12PLV2PC1Lo2JxzSf0AWgNbgemFXrsecED9Ivv2ir/evqT37dq1qyuLuXPnlmn/0F5//XUX/124+fPnF7tPqh1Taem4UkeyHRPwiVNfV6b9g1uwwDnwj1deOeKuKXdsZZCux5aux+Vc2GMrS1+X1EPPZtYAmAlswHeY+fK/TR9b5EeOK7K9UpoxYwbf/76fYeP999+nR48egROJyJGorztKS5ZA9+6+/fzz8APd2yOSaElbKJpZbeBdoDpwpXNuX6HN+dfrFJ3z4BTgv865bwzFVBazZ8/mqquuAvw8iZdddlngRCJyJOrrjtKKFdC1q2+PHw9DhoTNI5KmkrJQNLOq+Lv62gGXO+e2Fd7unFuHv9i7f6GfqRJ/PrMCoyaVBQsWcMkllwAwdepUrr766sCJRORI1NcdpbVroWN8GsnRo+GOO8LmEUljyXozyzNAb+B2oKGZNSy07Z/OuQP4ucVeNbPPgQXAIHxnO7BioyaHxYsXc8EFFwAwefJkBg6slL8GkVSjvq6sNm6Edu18e8QIeOCBsHlE0lyyFoqXxv98sphtJwGfO+deN7M6wN3A/fjVCq50zi2voIxJY/ny5XTr1g2ACRMmcNNNNwVOJCKlpL6uLL78Ek480bdvvhkeeyxsHpFKICkLRedc61Lu9wLwQrRpktuaNWvo1KkTAA899BDDhw8PnEhESkt9XRns3AknnODb3/8+PPdc2DwilURSXqMopbNhw4aCCbRHjhzJqFGjAicSEYnA7t3QqJFvX3EFvPZa2DwilYgKxRS1ZcuWgqX4br31VsaNGxc2kIhIFPbuhWPjswN17+5XYBGRCqNCMQXt2LGDZs2aAXD99dfzzDPPBE4kIhKBAwegTh3f7tgRPvoobB6RSkiFYorZtWsXjRv75V379OnDq6++GjiRiEgEDh6EmjV9u0UL+Pe/w+YRqaRUKKaQvXv3Ur9+fQB69OjBjBkzAicSEYlAXh5Ur+7bxxwD//lP2DwilZgKxRSxf/9+6sSHYDp37sz8+fMDJxIRiYBzULXQhBxZWeGyiIgKxVRw8OBBatWqBUCrVq1YunRp4EQiIhFwDqoU+mcpFguXRUQAFYpJLy8vj+rxIZh69eqxYcOGwIlERCJStEg0C5dFRAAVikktFotRtdAQzK5duwKmERGJUP7dzQC5uSoSRZKECsUk5ZwjIyOj4HlMQzAikq5atvTzJQLk5EChvk9EwlKhmKSqFBqCicVimL5di0g66tQJNm3y7exsqFYtbB4ROYQKxSRUu3btgnZubq6KRBFJTxdeCMuX+3ZW1tfzJopI0lChmGSaNWtGdnY2ADk5OYcMP4uIpI0rr/x6pZWvvvLzJYpI0lGhmEQ6dOjAli1bAD9vYjUNwYhIOrr+enjvPd/esePrtZxFJOmoUEwS559/PitXrgT8Ciw1atQInEhEJAK33AKvvebbW7ZAw4Zh84jIEalQTAK9e/fmb3/7G+CnwCl8jaKISNoYORKef963N2yA448Pm0dESqRCMbDvf//7zJw5E4CdO3dSr169wIlERCLw0EPw2GO+vWYNtGoVNo+IlIoKxYCGDBnC73//ewC+/PJLGjRoEDiRiEgEHn8cHnjAt5cvh7Ztw+YRkVJToRjIiBEjeOGFFwDYuHEjTZs2DZxIRCQCkyfDnXf69iefQIcOYfOISJmoUAxg9OjRjB8/HoC1a9fSsmXLwIlERCIwdSrcfLNvf/QRdO0aNo+IlJkKxQr261//mgcffBCAFStW0KZNm7CBRESiMH06/OAHvv2Xv0D37mHziMhRUaFYgZ577jnuuusuAJYsWcJpp50WOJGISARmzYK+fX17xgzo1StsHhE5aioUK8grr7zCrbfeCsDChQs544wzAicSEYnAhx/C5Zf79u9/D336hM0jIuWiQrECvPXWW9xwww0AfPDBB5x33nmBE4mIRODjj+Gii3z7pZdgwICweUSk3FQoRmzmzJn069cPgHfffZdvf/vbgROJiERg2TI45xzfnjgRbrwxbB4RSQgVihGaN28evXv3BmDatGlcccUVgROJiERg9Wo4/XTfHjsWfvrTsHlEJGFUKEbk73//OxdffDEAU6ZMoX///oETiYhEYP16OOUU377vPrjnnrB5RCShVChGYOnSpQXXIT799NMMGjQocCIRkQh88QV861u+fdtt8Mtfhs0jIgmnQjHBVq1aRZcuXQAYN24cQ4cODZxIRCQC27dD8+a+/cMfwpNPBo0jItFQoZhA69at49RTTwXg/vvvZ+TIkYETiYhEIDMTmjTx7b594be/DZtHRCKjQjFBNm/eXLDKyvDhwxkzZkzgRCIiEcjKggYNfLtnT3jrrbB5RCRSKhQTYNu2bbRo0QKAwYMHM2HChMCJREQikJ0Ndev6dteuMHt22DwiEjkViuWUmZlJ06ZNAejXrx8vvvhi4EQiIhHIyYHatX27bVv45JOweUSkQqhQLIc9e/bQID4Ec+mll/Lmm28GTiQiEoHcXMjdmyEAAA3WSURBVKhRw7cbNoQ1a8LmEZEKo0LxKGVnZ1OvXj0Azj77bGbNmhU4kYhIBGIxqFbNt81gx46weUSkQqlQPAo5OTnUjg/BtG/fnkWLFgVOJCISAecgI+Pr53l54bKISBApXSia2Wlm9oGZ7TOzL8xsjJlllPyTRy83N5ca8SGYJk2asHr16ig/TkQkSF+Hc1Cl0D8RsZg/oygilUrV0AGOlpkdB8wGVgJXAW2A8fjid1QUnxmLxagWH4KpWrUqW7dujeJjREQKhOjrgK+Hm8GfSVSRKFIppWyhyP9v7/6D5SrrO46/P00EQtEkKtIKSKqRptQZaxMs+ItLGoORToMYi9ZWmalSMqQ/HDp1nA4zIbZ/lBZ0nNSBzGQS66iRCkOlSAMaLyCV1og/ohBrLJSppuHHXPKDQAI33/7xnNts1nPv3d27Z/c8J5/XzM7uPnvOs8/3Pud873P2nH0WrgTmAJdGxD7gbkkvAdZKuq4o65uIYFbLKZjDhw/3s3ozs8kMNNcBcOqpR08zP//8sZ8smtlxJee9fwWwtS1JbiEl1Av6+UYRwYYNG/7/+ZEjR5CPrs1sMAaW6wBOv+WWo19YOXQIZuf8eYKZzVTOA8VFwM7Wgoh4DDhYvNY369atY8uWLaxevZrx8XEPEs1skAaW69iwgdeuXw+XXJLmTTzhhL5Wb2b5yflQcT7wdEn5WPHaz5F0BXAFwGmnncbo6Oi0b7Jr1y6uvfZali1bxqpVq7j33nt7b3HNHDhwoKO/QW4cVz6aGFMFBpLrTnjiCc5bs4YnFy/m4dWrifvv773FNdXk7a2psTU1LsgntpwHil2LiA3ABoAlS5bEyMjItOuMjIywcOFCxsfHWbp0acUtHKzR0VE6+RvkxnHlo4kx1UEvuQ6A009n5/79XLB8eXWNG6Imb29Nja2pcUE+seV86nkMmFtSPr94rW9GRkaO+SKLmdkADSzX8aY3cWTiF1jMzMh7oLiTtutzJJ0JnEzb9TxmZhlzrjOzocl5oHgncJGkF7eUXQY8C9wznCaZmfWdc52ZDU3OA8UbgUPArZKWFRdvrwVuqGReMTOz4XCuM7OhyfbLLBExJum3gfXA7aRvBX6ClEDNzBrBuc7MhinbgSJARDwENOuryGZmbZzrzGxYcj71bGZmZmYV8kDRzMzMzEp5oGhmZmZmpTxQNDMzM7NSHiiamZmZWSlFxLDbMBSSngD+u4tVXg48WVFzhqWJMYHjykndYjorIk4ddiP6ybnuGI4tP02NC4YbW8e57rgdKHZL0vaIWDLsdvRTE2MCx5WTJsaUuyb3iWPLT1Pjgnxi86lnMzMzMyvlgaKZmZmZlfJAsXMbht2ACjQxJnBcOWliTLlrcp84tvw0NS7IJDZfo2hmZmZmpfyJopmZmZmVauxAUdI5kr4m6aCkn0laJ2lWB+vNlbRJ0pikvZI+J+llJcutlLRD0nOSHpJ0Wa915RSXpAWSouS2pa5xSXq7pC9IerRo69pe68opptz6StIsSR+VdJ+kp4rbXZLOLanrREnXS3pc0jOS7pC0YCZxNcmw80SVhr1fVakO+1ZVKu63a4vtcZ+k/ZK2N2WbbFt+ZbFdbu9vBNOIiMbdgPnAz4CvAm8HrgSeAf66g3W3Ao8A7wbeBfwncF/bMm8BXgA+BVwI/B1wBFjebV25xQUsAAK4Gjiv5bawxnFdD/wQ2FjUu7bXunKKKbe+Ak4BxoAbgHcCK4A7gEPA4ra6bgKeAj5QLPfvwI+Bk3qNrSm3OuSJjGPrKFfkFls3+1ZusRXLfKLIcyuA5aRr/wJYlXtsLcueBPwX8L/A9kFtlxHR2IHix4qd4iUtZX8JHGwtK1nv/GLjeltL2RuLsmVtnbutbd2vAN/otq4M41pQrPc7GfXXL7Q8fpLyQVVf+6smMWXVV8AsYH7beicAjwKbWsrOIA1UPtBSdjpwGPhQv2LN9VaHPJFxbNPuVznG1um+lWNsU6x7P/DlpsQGXAPcB2xmwAPFpp56XgFsjYh9LWVbgDnABdOstyci7p0oiIj/II34V0A65UU6ir65bd0twPmS5nZaVw/qEFcVKourKDvSYRv62V91iKkKlcUVEeMRMda6UkQcJn3C88qW4uXF/a0ty/0U+Aa971tN0tQ8MdHGJu5XUI99qyqV9tskniINhqtWeWySXkUafP5ZvxrdjaYOFBcBO1sLIuIx0gh/UTfrFR5uWe81wItKlnuY9Pc8u4u6ulWHuCZskjQuabekGyTN6SyEztrXx7h6bsMM6iqtbwgxTci2r4qByW+STsm01vU/EXGgm7qOI3XKE/1Wp/2q3+qwb1VlILFJmi1pnqT3kw4ob+y5xZ0bRGzXAzdHxIMzaGfPZg/jTQdgPvB0SflY8Vov6726ZRlKlhtre72TurpVh7gOAf8A3AXsA0aAj5L+gaycog1TqTKufrShl/6qQ0xN6Ku/Al4KrO9DG44XdcgTVanDflWVOuxbVak8NknnAd8snr4ArImI27pvatcqjU3SUtKgt+qDsEk1daBoFYmI3cCalqJRSXuAT0t6fUR8b0hNsza595Wki0n/zK6OiB8Nuz1mTdHQfWsHcC4wD7gYWC9pX0R8YbjN6p2k2aQvjf1NROwZVjuaeup5DCi7VmY+R498e11v4r59ufltr/fahqnUIa4yXyruF0+xzFSqjKvqNvS7viq2m1ZZ9FUxbccXgRsj4pN9asPxoq55oh/qul/1Qx32rapUHltEPBMR2yPiqxHxEeCzwN/22N5uVBnbh4tlNhen1OeRrrucVTx/Ue/N7lxTB4o7aTvHL+lM4GTKrwmYdL1C67UEPwGeL1luEWmKiInrPTqpq1t1iKtMtN13q8q4em7DDOoqrW8IMZWpfV9JOps0dcfXgD+dpK4zJf3idHUdp+qaJ/qhrvtVP9Rh36rKMPrtQVKeqPrMaZWx/Spploc9pMHjGPA+4DeKxwOZK7KpA8U7gYskvbil7DLgWeCeadb7JUlvmSiQtIR0vcCdABFxCPg68J62dS8DvhkRezutqwd1iKvMquL+250EMUn7Komryzb0s7/qEFOZWveVpF8mTb/yE+B9ETFeUtddxf27WtZ7JfBW+vM3yl1d80Q/1HW/6oc67FtVGUa/vZn0pbcXemtyx6qMbT1ploHW21bSAdmFwN19imFqg5yLZ1A30ke3u4s/4jLgCuAAbRNgAruAjW1lW0mTWl4KXAL8iMknnP0k6QsC1zH5hNtT1pVbXMBa0jewLi3asI60Q9xS47jOIg2QVpG+1HFz8XhFVf1Vh5hy6yvSdBLfJV3gfTHHThL+hra6biLNc/eHwDuAB/CE24Pa9jrKf5nG1lGuyC22bvatDGM7i/QJ6YeBpcDvAptIZ02uzDm2Sd5vM55wu2+ddw6wjfSPcTfwcWBW2zKPApvbyuYVG9nTRaL4PPDykvovAX5A+mbpTuC9Jct0VFdOcQHvBbYDe0kTHO8iDUBOrGtcwOVF0mi/PVplfw07ptz6iqMThHfSVyeSfmXiCdKvIHwF+JWZxNWkW5XbXrHctPkvx9g6zRW5xdbNvpVhbHNJ1yM+AjxH+uWSbcA7m7BNlrzXZgY8UFTxxmZmZmZmx2jqNYpmZmZmNkMeKJqZmZlZKQ8UzczMzKyUB4pmZmZmVsoDRTMzMzMr5YGimZmZmZXyQNGyI+lySd+WtF/SmKTvSLqh5fVXSForaUGf33dU0pemX9LMbOac66wOPI+iZUXSx0iTmV5H+imxk4DFwB9ExMJimdcBO4ALI2K0j+99DvB8RPy4X3WamZVxrrO68EDRsiLpp8BtEXFVW7mi2Ji7SZ6S5kTEs1W118ysF851Vhc+9Wy5mUf6iaZjtCTOBaTECfB1SSFp4rWR4vlFkr4s6QDpR9eRdLWkb0naK2mPpNslLWx9j/bTMcUpnyclvUHSA5IOFqeG3lpF4GZ2XHGus1rwQNFy8yDwJ5I+KOllJa/vBt5fPL4KOL+4tdoIfI/04/Ebi7IzSIl0JenH5WcB/yZp7jTtORn4DHAT8G7Sb9/eKunkboIyM2vjXGe1MHvYDTDr0lXAbaQfRg9JDwO3AH8fEfsi4pCk7xfLPhQRD5TU8U8RcU1rQUR8ZOKxpFnA3cDjpGT6j1O0Zw7w5xGxrVh3N/Ad4G3Av/YQn5kZONdZTfgTRctKRHwf+DXSEfKnAQHXANslndJhNXe0F0g6T9Ldkp4CXgAOAqcAZ09T12FgtOX5Q8X9GR22xczs5zjXWV14oGjZiYhDEXF7RKyJiHOADwGvBf6owyr2tD6R9CrgLlIi/mPgzcC5pKPsk6apa39EHGlp2+Hi4XTrmZlNybnO6sCnni17EbFR0nXAok5XaXv+DtL1Nysj4hkASbOBl/avlWZmM+NcZ8PgTxQtK5JeUVJ2KjCXo0fP3R7pzgGOkE7DTPg9fCBlZkPiXGd14Y3DcrND0j+TTp88DpwF/AXpOpvPFMs8BjwLfFDSXtLEsdunqHMb6Zt/myRtBH69qPPpakIwM5uWc53Vgj9RtNysAxYAnyIl0I8DPwTeGBGPAETEc6RpHxYD9wDfmqrCiNgBXA78FvAvwO8D7wH2VhGAmVkHnOusFvzLLGZmZmZWyp8ompmZmVkpDxTNzMzMrJQHimZmZmZWygNFMzMzMyvlgaKZmZmZlfJA0czMzMxKeaBoZmZmZqU8UDQzMzOzUh4ompmZmVmp/wM7QQkD+c9Q5QAAAABJRU5ErkJggg==\n", 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" - }, - "metadata": {} - } - ], - "execution_count": 3, - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "source": [], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.14" - }, - "nteract": { - "version": "0.28.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/Notebooks/Umats/Mechanical/Elasticity/ELORT.ipynb b/Notebooks/Umats/Mechanical/Elasticity/ELORT.ipynb deleted file mode 100644 index f15bcc484..000000000 --- a/Notebooks/Umats/Mechanical/Elasticity/ELORT.ipynb +++ /dev/null @@ -1,220 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# Orthotropic linear elasticity" - ], - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "%matplotlib inline\n", - "\n", - "import pylab\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import simcoon as sim\n", - "import math\n", - "import os\n", - "dir = os.path.dirname(os.path.realpath('__file__'))" - ], - "outputs": [], - "execution_count": 1, - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "In thermoelastic orthotropic materials twelve parameters are required: \n", - "\n", - "1. The Young modulus of axis 1 $E_1$,\n", - "2. The Young modulus of axis 2 $E_2$,\n", - "3. The Young modulus of axis 3 $E_3$,\n", - "4. The Poisson ratio of direction 1 with respect to 2 $\\nu_{12}$,\n", - "5. The Poisson ratio of direction 1 with respect to 3 $\\nu_{13}$,\n", - "6. The Poisson ratio of direction 2 with respect to 3 $\\nu_{13}$,\n", - "7. The shear modulus of direction 1 with respect to 2 $G_{12}$,\n", - "8. The shear modulus of direction 1 with respect to 3 $G_{13}$,\n", - "9. The shear modulus of direction 2 with respect to 3 $G_{23}$,\n", - "10. The axial coefficient of thermal expansion in direction 1 $\\alpha_1$,\n", - "11. The axial coefficient of thermal expansion in direction 1 $\\alpha_2$,\n", - "12. The axial coefficient of thermal expansion in direction 1 $\\alpha_3$,\n", - "\n", - "The elastic stiffness tensor and the thermal expansion coefficients tensor are written in SMART+ formalism as\n", - "\n", - "$$\\boldsymbol{L}=\\left(\\begin{matrix} L_{1111} & L_{1122} & L_{1133} & 0 & 0 & 0 \\\\ L_{1122} & L_{2222} & L_{2233} & 0 & 0 & 0 \\\\ L_{1133} & L_{2233} & L_{3333} & 0 & 0 & 0 \\\\ 0 & 0 & 0 & L_{1212} & 0 & 0 \\\\ 0 & 0 & 0 & 0 & L_{1313} & 0 \\\\ 0 & 0 & 0 & 0 & 0 & L_{2323} \\end{matrix}\\right), \\quad \\boldsymbol{\\alpha}=\\left(\\begin{matrix} \\alpha_L & 0 & 0 \\\\ 0 & \\alpha_T & 0 \\\\ 0 & 0 & \\alpha_T \\end{matrix}\\right),$$\n", - "\n", - "where $$\\begin{array}{c}\\displaystyle{L_{1111}=E_1~(1-\\nu_{23}\\nu_{32})~\\Delta, \\quad L_{1122}=E_1~(\\nu_{21}+\\nu_{31}\\nu_{23} \\Delta, L_{1133} = E_1~(\\nu_{31}+\\nu_{21}\\nu_{32}} \\\\\n", - "\\displaystyle{L_{2222}= E_2~(1-\\nu_{13}\\nu_{31})~\\Delta, \\quad L_{2233}= E_2~(\\nu_{yz}+\\nu_{zx}\\nu_{yz} \\Delta, \\quad L_{2233} = E_3 (1-\\nu_{12}\\nu_{21})~\\Delta} \\\\\n", - "\\displaystyle{\\quad L_{1212}= G_{12}, \\quad \\quad L_{1313}= G_{13}, \\quad \\quad L_{2323}= G_{23},}\\\\\n", - "\\displaystyle{\\Delta= \\frac{1}{1-\\nu_{12}\\nu_{21}-\\nu_{23}\\nu_{32}-\\nu_{31}\\nu_{13}-2\\nu_{21}\\nu_{32}\\nu_{13}}}\\end{array}$$\n", - "\n", - "Also, all the Poisson's ratio are defined as:\n", - "$$\\begin{array}{c} \\nu_{21}= \\nu_{12} \\frac{E_2}{E_1} \\\\\n", - "\\nu_{32}= \\nu_{23}\\frac{E_3}{E_2} \\\\\n", - "\\nu_{31}= \\nu_{13}\\frac{E_3}{E_1} \\end{array}$$ \n", - "\n", - "\n", - "Details on the elastic stiffness tensor of transversely isotropic media can be found in Christensen (1979). For axis of transverse isotropy being 2 or 3, the above tensors are properly rotated. The tangent stiffness tensor in this case is $\\boldsymbol{L}^t=\\boldsymbol{L}$. Moreover, the increment of the elastic strain is given by \n", - "\n", - "$$\\Delta\\varepsilon^{\\textrm{el}}_{ij}=\\Delta\\varepsilon^{\\textrm{tot}}_{ij}-\\alpha_{ij}\\Delta T.$$\n", - "\n", - "In the 1D case only one component of stress is computed, through the relation \n", - "\n", - "$$\\sigma^{\\textrm{fin}}_{11}=\\sigma^{\\textrm{init}}_{11}+L_{1111}\\Delta\\varepsilon^{\\textrm{el}}_{11}.$$\n", - "\n", - "In the plane stress case only three components of stress are computed, through the relations \n", - "\n", - "$$\\left(\\begin{matrix} \\sigma^{\\textrm{fin}}_{11} \\\\ \\sigma^{\\textrm{fin}}_{22} \\\\ \\sigma^{\\textrm{fin}}_{12} \\end{matrix}\\right) =\\left(\\begin{matrix} \\sigma^{\\textrm{init}}_{11} \\\\ \\sigma^{\\textrm{init}}_{22} \\\\ \\sigma^{\\textrm{init}}_{12} \\end{matrix}\\right)+\\boldsymbol{K} \\left(\\begin{matrix} \\Delta\\varepsilon^{\\textrm{el}}_{11} \\\\ \\Delta\\varepsilon^{\\textrm{el}}_{22} \\\\ 2\\Delta\\varepsilon^{\\textrm{el}}_{12} \\end{matrix}\\right),$$\n", - "\n", - "with $$\\boldsymbol{K}=\\left(\\begin{matrix} \\displaystyle{L_{1111}-\\frac{L_{1133}L_{3311}}{L_{3333}}} & \\displaystyle{L_{1122}-\\frac{L_{1133}L_{3322}}{L_{3333}}} & \\displaystyle{L_{1112}-\\frac{L_{1133}L_{3312}}{L_{3333}}} \\\\ \\displaystyle{L_{2211}-\\frac{L_{2233}L_{3311}}{L_{3333}}} & \\displaystyle{L_{2222}-\\frac{L_{2233}L_{3322}}{L_{3333}}} & \\displaystyle{L_{2212}-\\frac{L_{2233}L_{3312}}{L_{3333}}} \\\\ \\displaystyle{L_{1211}-\\frac{L_{1233}L_{3311}}{L_{3333}}} & \\displaystyle{L_{1222}-\\frac{L_{1233}L_{3322}}{L_{3333}}} & \\displaystyle{L_{1212}-\\frac{L_{1233}L_{3312}}{L_{3333}}} \\end{matrix}\\right).$$\n", - "\n", - "In the generalized plane strain/3D analysis case the stress tensor is computed through the relation\n", - "\n", - "$$\\sigma^{\\textrm{fin}}_{ij}=\\sigma^{\\textrm{init}}_{ij}+L_{ijkl}~\\Delta\\varepsilon^{\\textrm{el}}_{kl}.$$" - ], - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "umat_name = 'ELORT' #This is the 5 character code for the elastic transversely isotropic subroutine\n", - "nstatev = 1 #The number of scalar variables required, only the initial temperature is stored here\n", - "\n", - "E_1 = 4500\n", - "E_2 = 2300\n", - "E_3 = 2700\n", - "nu_12 = 0.06\n", - "nu_13 = 0.08\n", - "nu_23 = 0.3\n", - "G_12 = 2200\n", - "G_13 = 2100\n", - "G_23 = 2400\n", - "alpha_1 = 1.0E-5\n", - "alpha_2 = 2.5E-5\n", - "alpha_3 = 2.2E-5\n", - "\n", - "psi_rve = 0.\n", - "theta_rve = 0.\n", - "phi_rve = 0.\n", - "solver_type = 0\n", - "corate_type = 2\n", - "\n", - "props = np.array([E_1, E_2, E_3, nu_12, nu_13, nu_23, G_12, G_13, G_23, alpha_1, alpha_2, alpha_3])\n", - "path_data = 'data'\n", - "path_results = 'results'\n", - "\n", - "pathfile = 'path_1.txt'\n", - "outputfile_1 = 'results_ELORT_1.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, corate_type, path_data, path_results, pathfile, outputfile_1)\n", - "outputfile_1 = dir + '/' + path_results + '/results_ELORT_1_global-0.txt'\n", - "\n", - "pathfile = 'path_2.txt'\n", - "outputfile_2 = 'results_ELORT_2.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, corate_type, path_data, path_results, pathfile, outputfile_2)\n", - "outputfile_2 = dir + '/' + path_results + '/results_ELORT_2_global-0.txt'\n", - "\n", - "pathfile = 'path_3.txt'\n", - "outputfile_3 = 'results_ELORT_3.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, corate_type, path_data, path_results, pathfile, outputfile_3)\n", - "outputfile_3 = dir + '/' + path_results + '/results_ELORT_3_global-0.txt'\n", - "\n", - "\n", - "fig = plt.figure()\n", - "pylab.rcParams['figure.figsize'] = (24.0, 5.0) #configure the figure output size\n", - "\n", - "ax = fig.add_subplot(1, 3, 1)\n", - "\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(outputfile_1, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('Strain', size = 15)\n", - "plt.ylabel('Stress (MPa)', size = 15)\n", - "plt.plot(e11,s11, c='black', label='direction 1')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(1, 3, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('Strain', size = 15)\n", - "#plt.ylabel('Stress (MPa)')\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(outputfile_2, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "plt.plot(e22,s22, c='black', label='direction 2')\n", - "\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(1, 3, 3)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('Strain', size = 15)\n", - "#plt.ylabel('Stress (MPa)')\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(outputfile_3, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "plt.plot(e33,s33, c='black', label='direction 3')\n", - "plt.legend(loc=2)\n", - "\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", 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" - }, - "metadata": {} - } - ], - "execution_count": 3, - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "source": [], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true - } - }, - { - "cell_type": "code", - "source": [], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.14" - }, - "nteract": { - "version": "0.28.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/Notebooks/Umats/Mechanical/Elasticity/data/path.txt b/Notebooks/Umats/Mechanical/Elasticity/data/path.txt deleted file mode 100644 index 0e6267b72..000000000 --- a/Notebooks/Umats/Mechanical/Elasticity/data/path.txt +++ /dev/null @@ -1,59 +0,0 @@ -#Initial_temperature -290 -#Number_of_blocks -1 - -#Block -1 -#Loading_type -1 -#Control_type(NLGEOM) -1 -#Repeat -1 -#Steps -2 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1. -#Dn_inc 0.01 -#time -1 -#Consigne -E 0.02 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 290 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1 -#Dn_inc 0.01 -#time -1 -#Consigne -E 0 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 290 - -#Mode -3 -#File -path_inc.txt -#Consigne -S -0 0 -0 0 0 -#T_is_set -Q - - - - - diff --git a/Notebooks/Umats/Mechanical/Elasticity/data/path_1.txt b/Notebooks/Umats/Mechanical/Elasticity/data/path_1.txt deleted file mode 100644 index b4e8b5f57..000000000 --- a/Notebooks/Umats/Mechanical/Elasticity/data/path_1.txt +++ /dev/null @@ -1,46 +0,0 @@ -#Initial_temperature -290 -#Number_of_blocks -1 - -#Block -1 -#Loading_type -1 -#Control_type(NLGEOM) -1 -#Repeat -1 -#Steps -2 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1. -#Dn_inc 0.01 -#time -1 -#Consigne -S 100 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 290 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1 -#Dn_inc 0.01 -#time -1 -#Consigne -S 0 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 290 - - - diff --git a/Notebooks/Umats/Mechanical/Elasticity/data/path_2.txt b/Notebooks/Umats/Mechanical/Elasticity/data/path_2.txt deleted file mode 100644 index 2c71fe3d1..000000000 --- a/Notebooks/Umats/Mechanical/Elasticity/data/path_2.txt +++ /dev/null @@ -1,45 +0,0 @@ -#Initial_temperature -290 -#Number_of_blocks -1 - -#Block -1 -#Loading_type -1 -#Control_type(NLGEOM) -1 -#Repeat -1 -#Steps -2 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1. -#Dn_inc 0.01 -#time -1 -#Consigne -S 0 -S 0 S 100 -S 0 S 0 S 0 -#Consigne_T -T 290 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1 -#Dn_inc 0.01 -#time -1 -#Consigne -S 0 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 290 - - diff --git a/Notebooks/Umats/Mechanical/Elasticity/data/path_3.txt b/Notebooks/Umats/Mechanical/Elasticity/data/path_3.txt deleted file mode 100644 index 0a0b3524e..000000000 --- a/Notebooks/Umats/Mechanical/Elasticity/data/path_3.txt +++ /dev/null @@ -1,45 +0,0 @@ -#Initial_temperature -290 -#Number_of_blocks -1 - -#Block -1 -#Loading_type -1 -#Control_type(NLGEOM) -1 -#Repeat -1 -#Steps -2 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1. -#Dn_inc 0.01 -#time -1 -#Consigne -S 0 -S 0 S 0 -S 0 S 0 S 100 -#Consigne_T -T 290 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1 -#Dn_inc 0.01 -#time -1 -#Consigne -S 0 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 290 - - diff --git a/Notebooks/Umats/Mechanical/Plasticity/Loop_kin/EPKCP.ipynb b/Notebooks/Umats/Mechanical/Plasticity/Loop_kin/EPKCP.ipynb deleted file mode 100644 index 1e96e14cf..000000000 --- a/Notebooks/Umats/Mechanical/Plasticity/Loop_kin/EPKCP.ipynb +++ /dev/null @@ -1,340 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plasticity with isotropic hardening" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import pylab\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import rc\n", - "import simcoon as sim\n", - "import os\n", - "from IPython.display import HTML\n", - "dir = os.path.dirname(os.path.realpath('__file__'))\n", - "\n", - "plt.rc('text', usetex=True)\n", - "plt.rc('font', family='serif')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The elastic-plastic (isotropic with kinematical hardening) constitutive law implemented in SMART+ is a rate independent, isotropic, von Mises type material with power-law isotropic hardening. \n", - "Eight parameters are required for the thermomechanical version: \n", - "1. The density $\\rho$\n", - "2. The specific heat $c_p$\n", - "3. The Young modulus $E$,\n", - "4. The Poisson ratio $\\nu$,\n", - "5. The coefficient of thermal expansion $\\alpha$,\n", - "6. The von Mises equivalent yield stress limit $\\sigma_{Y}$,\n", - "7. The hardening parameter $k$,\n", - "8. The hardening exponent $m$.\n", - "9. The kinematic hardening parameter $k_X$\n", - "\n", - "The constitutive law is given by the set of equations \n", - "\n", - "$$\\begin{matrix} {\\sigma}_{ij}=L_{ijkl}\\left({\\varepsilon}^{\\textrm{tot}}_{kl}-\\alpha_{kl}\\left(T-T^{\\textrm{ref}}\\right)-{\\varepsilon}^{\\textrm{p}}_{kl}\\right),\\\\\n", - "\\dot{\\varepsilon}^{\\textrm{p}}_{ij}=\\dot{p}\\Lambda_{ij}, \\quad \\Lambda_{ij}=\\frac{3}{2}\\frac{\\sigma'_{ij}}{\\lvert \\mathbf{\\sigma}\\rvert}, \\quad \\sigma'_{ij}=\\sigma_{ij}-\\frac{1}{3}\\sigma_{kk}\\delta_{ij}, \\quad \\lvert q \\rvert = \\sqrt{\\frac{3}{2}q'_{kl}q'_{kl}},\\\\ \\Phi=\\lvert \\mathbf{\\sigma}-\\mathbf{X} \\rvert -\\sigma_{Y}-kp^m\\leq 0, \\quad \\dot{p}\\geq0,~~~ \\dot{p}~\\Phi=0, \\end{matrix}$$\n", - "\n", - "where ${\\varepsilon}^{\\textrm{p}}_{ij}$ is the plastic strain tensor, $p$ is the plastic multiplier, $\\sigma'_{ij}$ is the deviatoric part of the stress and $\\overline{\\sigma}$ is the von Mises equivalent stress (Lemaitre and Chaboche, 2002). Moreover, $T^{\\textrm{ref}}$ is a reference temperature (usually the temperature at the beginning of the analysis).\n", - "\n", - "In SMART+ the elastoplastic material constitutive law is implemented using a *return mapping algorithm*, with use of the *convex cutting plane* algorithm (Simo and Hughes, 1998). The updated stress is provided for 1D, plane stress, and generalized plane strain/3D analysis according to the forms of elastic isotropic materials.\n", - "The updated work, and internal heat production $r$ are determined with the algorithm presented in the *simcoon* documentation.\n", - "\n", - "As a start we should input the name of the UMAT as well as the list of parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The folder for the results, results, is not present and has been created\n" - ] - } - ], - "source": [ - "umat_name = 'EPKCP' #This is the 5 character code for the elastic-plastic subroutine\n", - "nstatev = 14 #The number of scalar variables required, only the initial temperature is stored here\n", - "\n", - "E = 113800\n", - "nu = 0.342\n", - "alpha = 0.86E-5\n", - "sigma_Y = 500\n", - "H = 1000\n", - "beta = 0.7\n", - "kX = 5000\n", - "solver_type = 0\n", - "corate_type = 3\n", - "\n", - "##local orientation\n", - "psi_rve = 0.\n", - "theta_rve = 0.\n", - "phi_rve = 0.\n", - "\n", - "#Define the properties\n", - "props = np.array([E, nu, alpha, sigma_Y, H, beta, kX])\n", - "path_data = 'data'\n", - "path_results = 'results'\n", - "\n", - "#Run the simulation\n", - "pathfile = 'path.txt'\n", - "outputfile = 'results_EPICP.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, corate_type, path_data, path_results, pathfile, outputfile)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#prepare the load\n", - "fig = plt.figure()\n", - "outputfile_global = 'results_EPICP_global-0.txt'\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global = path + outputfile_global\n", - "\n", - "#Get the data\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(P_global, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time, T, Q, r = np.loadtxt(P_global, usecols=(4,5,6,7), unpack=True)\n", - "Wm, Wm_r, Wm_ir, Wm_d = np.loadtxt(P_global, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(1, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11,s11, c='black', label='direction 1')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(1, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "plt.plot(time,Wm, c='black', label=r'$W_m$')\n", - "plt.plot(time,Wm_r, c='green', label=r'$W_m^r$')\n", - "plt.plot(time,Wm_ir, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time,Wm_d, c='red', label=r'$W_m^d$')\n", - "plt.legend(loc=2)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test now the increments " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "#Run the simulations - 1 increment\n", - "pathfile = 'path_1.txt'\n", - "outputfile = 'results_EPICP_1.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, corate_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1 = 'results_EPICP_1_global-0.txt'\n", - "\n", - "#Run the simulations - 10 increments\n", - "pathfile = 'path_10.txt'\n", - "outputfile = 'results_EPICP_10.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, corate_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_10 = 'results_EPICP_10_global-0.txt'\n", - "\n", - "#Run the simulations - 100 increments\n", - "pathfile = 'path_100.txt'\n", - "outputfile = 'results_EPICP_100.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, corate_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_100 = 'results_EPICP_100_global-0.txt'\n", - "\n", - "#Run the simulations - 1000 increments\n", - "pathfile = 'path_1000.txt'\n", - "outputfile = 'results_EPICP_1000.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, corate_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1000 = 'results_EPICP_1000_global-0.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global_1 = path + outputfile_global_1\n", - "P_global_10 = path + outputfile_global_10\n", - "P_global_100 = path + outputfile_global_100\n", - "P_global_1000 = path + outputfile_global_1000\n", - "\n", - "#Get the data\n", - "e11_1, e22_1, e33_1, e12_1, e13_1, e23_1, s11_1, s22_1, s33_1, s12_1, s13_1, s23_1 = np.loadtxt(P_global_1, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1, T_1, Q_1, r_1 = np.loadtxt(P_global_1, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1, Wm_r_1, Wm_ir_1, Wm_d_1 = np.loadtxt(P_global_1, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "e11_10, e22_10, e33_10, e12_10, e13_10, e23_10, s11_10, s22_10, s33_10, s12_10, s13_10, s23_10 = np.loadtxt(P_global_10, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_10, T_10, Q_10, r_10 = np.loadtxt(P_global_10, usecols=(4,5,6,7), unpack=True)\n", - "Wm_10, Wm_r_10, Wm_ir_10, Wm_d_10 = np.loadtxt(P_global_10, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "e11_100, e22_100, e33_100, e12_100, e13_100, e23_100, s11_100, s22_100, s33_100, s12_100, s13_100, s23_100 = np.loadtxt(P_global_100, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_100, T_100, Q_100, r_100 = np.loadtxt(P_global_100, usecols=(4,5,6,7), unpack=True)\n", - "Wm_100, Wm_r_100, Wm_ir_100, Wm_d_100 = np.loadtxt(P_global_100, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "e11_1000, e22_1000, e33_1000, e12_1000, e13_1000, e23_1000, s11_1000, s22_1000, s33_1000, s12_1000, s13_1000, s23_1000 = np.loadtxt(P_global_1000, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1000, T_1000, Q_1000, r_1000 = np.loadtxt(P_global_1000, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1000, Wm_r_1000, Wm_ir_1000, Wm_d_1000 = np.loadtxt(P_global_1000, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "fig = plt.figure()\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(1, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11_1,s11_1, linestyle='None', marker='D', color='black', markersize=10, label='1 increment')\n", - "plt.plot(e11_10,s11_10, linestyle='None', marker='o', color='black', markersize=10, label='10 increment')\n", - "plt.plot(e11_100,s11_100, linestyle='None', marker='x', color='black', markersize=10, label='100 increments')\n", - "plt.plot(e11_1000,s11_1000, c='black', label='1000 increments')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(1, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "#1 increment\n", - "plt.plot(time_1,Wm_1, linestyle='None', marker='D', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_1,Wm_r_1, linestyle='None', marker='D', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_1,Wm_ir_1, linestyle='None', marker='D', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_1,Wm_d_1, linestyle='None', marker='D', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#10 increment\n", - "plt.plot(time_10,Wm_10, linestyle='None', marker='o', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_10,Wm_r_10, linestyle='None', marker='o', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_10,Wm_ir_10, linestyle='None', marker='o', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_10,Wm_d_10, linestyle='None', marker='o', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#100 increments\n", - "plt.plot(time_100,Wm_100, linestyle='None', marker='x', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_100,Wm_r_100, linestyle='None', marker='x', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_100,Wm_ir_100, linestyle='None', marker='x', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_100,Wm_d_100, linestyle='None', marker='x', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#1000 increments\n", - "plt.plot(time_1000,Wm_1000, c='black', label=r'$W_m$')\n", - "plt.plot(time_1000,Wm_r_1000, c='green', label=r'$W_m^r$')\n", - "plt.plot(time_1000,Wm_ir_1000, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time_1000,Wm_d_1000, c='red', label=r'$W_m^d$')\n", - "##\n", - "plt.legend(loc=2)\n", - "\n", - "plt.show()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "simcoon-scratch", - "language": "python", - "name": "simcoon-scratch" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/Notebooks/Umats/Mechanical/Plasticity/Loop_kin/data/output.dat b/Notebooks/Umats/Mechanical/Plasticity/Loop_kin/data/output.dat deleted file mode 100755 index cc0b9ba19..000000000 --- a/Notebooks/Umats/Mechanical/Plasticity/Loop_kin/data/output.dat +++ /dev/null @@ -1,16 +0,0 @@ -#Output_values -strain_type 0 -nb_strain 6 -0 1 2 3 4 5 -stress_type 4 -nb_stress 6 -0 1 2 3 4 5 - -Rotation_type 0 -Tangent_type 0 -T 1 - -Number_of_wanted_internal_variables 0 - -#Block #type_1_N_2_T #every -1 1 1 \ No newline at end of file diff --git a/Notebooks/Umats/Mechanical/Plasticity/Loop_kin/data/path.txt b/Notebooks/Umats/Mechanical/Plasticity/Loop_kin/data/path.txt deleted file mode 100644 index 71f39f2f5..000000000 --- a/Notebooks/Umats/Mechanical/Plasticity/Loop_kin/data/path.txt +++ /dev/null @@ -1,85 +0,0 @@ -#Initial_temperature -293.15 -#Number_of_blocks -1 - -#Block -1 -#Loading_type -1 -#Control_type(NLGEOM) -1 -#Repeat -1 -#Steps -5 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1. -#Dn_inc 0.001 -#time -300 -#Consigne -S 1000 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 293.15 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1. -#Dn_inc 0.001 -#time -300 -#Consigne -S -1100 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 293.15 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1. -#Dn_inc 0.001 -#time -300 -#Consigne -S 1200 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 293.15 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1. -#Dn_inc 0.001 -#time -300 -#Consigne -S -1300 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 293.15 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1. -#Dn_inc 0.001 -#time -300 -#Consigne -S 1500 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 293.15 \ No newline at end of file diff --git a/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/EPICP.ipynb b/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/EPICP.ipynb deleted file mode 100644 index 3b2b0abdf..000000000 --- a/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/EPICP.ipynb +++ /dev/null @@ -1,348 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plasticity with isotropic hardening" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import pylab\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import rc\n", - "import simcoon as sim\n", - "import os\n", - "from IPython.display import HTML\n", - "dir = os.path.dirname(os.path.realpath('__file__'))\n", - "\n", - "plt.rc('text', usetex=True)\n", - "plt.rc('font', family='serif')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The elastic-plastic (isotropic hardening) constitutive law implemented in SMART+ is a rate independent, isotropic, von Mises type material with power-law isotropic hardening. \n", - "Eight parameters are required for the thermomechanical version: \n", - "1. The density $\\rho$\n", - "2. The specific heat $c_p$\n", - "3. The Young modulus $E$,\n", - "4. The Poisson ratio $\\nu$,\n", - "5. The coefficient of thermal expansion $\\alpha$,\n", - "6. The von Mises equivalent yield stress limit $\\sigma_{Y}$,\n", - "7. The hardening parameter $k$,\n", - "8. The hardening exponent $m$.\n", - "\n", - "The constitutive law is given by the set of equations \n", - "\n", - "$$\\begin{matrix} {\\sigma}_{ij}=L_{ijkl}\\left({\\varepsilon}^{\\textrm{tot}}_{kl}-\\alpha_{kl}\\left(T-T^{\\textrm{ref}}\\right)-{\\varepsilon}^{\\textrm{p}}_{kl}\\right),\\\\\n", - "\\dot{\\varepsilon}^{\\textrm{p}}_{ij}=\\dot{p}\\Lambda_{ij}, \\quad \\Lambda_{ij}=\\frac{3}{2}\\frac{\\sigma'_{ij}}{\\overline{\\sigma}}, \\quad \\sigma'_{ij}=\\sigma_{ij}-\\frac{1}{3}\\sigma_{kk}\\delta_{ij}, \\quad \\overline{\\sigma}=\\sqrt{\\frac{3}{2}\\sigma'_{kl}\\sigma'_{kl}},\\\\ \\Phi=\\overline{\\sigma}-\\sigma_{Y}-kp^m\\leq 0, \\quad \\dot{p}\\geq0,~~~ \\dot{p}~\\Phi=0, \\end{matrix}$$\n", - "\n", - "where ${\\varepsilon}^{\\textrm{p}}_{ij}$ is the plastic strain tensor, $p$ is the plastic multiplier, $\\sigma'_{ij}$ is the deviatoric part of the stress and $\\overline{\\sigma}$ is the von Mises equivalent stress (Lemaitre and Chaboche, 2002). Moreover, $T^{\\textrm{ref}}$ is a reference temperature (usually the temperature at the beginning of the analysis).\n", - "\n", - "In SMART+ the elastoplastic material constitutive law is implemented using a *return mapping algorithm*, with use of the *convex cutting plane* algorithm (Simo and Hughes, 1998). The updated stress is provided for 1D, plane stress, and generalized plane strain/3D analysis according to the forms of elastic isotropic materials.\n", - "The updated work, and internal heat production $r$ are determined with the algorithm presented in the *simcoon* documentation.\n", - "\n", - "As a start we should input the name of the UMAT as well as the list of parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The folder for the results, results, is not present and has been created\n" - ] - } - ], - "source": [ - "umat_name = 'EPICP' #This is the 5 character code for the elastic-plastic subroutine\n", - "nstatev = 8 #The number of scalar variables required, only the initial temperature is stored here\n", - "\n", - "E = 113800\n", - "nu = 0.342\n", - "alpha = 0.86E-5\n", - "sigma_Y = 600\n", - "H = 1600\n", - "beta = 0.25\n", - "\n", - "psi_rve = 0.\n", - "theta_rve = 0.\n", - "phi_rve = 0.\n", - "solver_type = 0\n", - "corate_type = 3\n", - "\n", - "#Define the properties\n", - "props = np.array([E, nu, alpha, sigma_Y, H, beta])\n", - "path_data = 'data'\n", - "path_results = 'results'\n", - "\n", - "#Run the simulation\n", - "pathfile = 'path.txt'\n", - "outputfile = 'results_EPICP.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, corate_type, path_data, path_results, pathfile, outputfile)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#prepare the load\n", - "fig = plt.figure()\n", - "outputfile_global = 'results_EPICP_global-0.txt'\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global = path + outputfile_global\n", - "\n", - "#Get the data\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(P_global, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time, T, Q, r = np.loadtxt(P_global, usecols=(4,5,6,7), unpack=True)\n", - "Wm, Wm_r, Wm_ir, Wm_d = np.loadtxt(P_global, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(1, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11,s11, c='black', label='direction 1')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(1, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "plt.plot(time,Wm, c='black', label=r'$W_m$')\n", - "plt.plot(time,Wm_r, c='green', label=r'$W_m^r$')\n", - "plt.plot(time,Wm_ir, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time,Wm_d, c='red', label=r'$W_m^d$')\n", - "plt.legend(loc=2)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test now the increments " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "#Run the simulations - 1 increment\n", - "pathfile = 'path_1.txt'\n", - "outputfile = 'results_EPICP_1.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, corate_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1 = 'results_EPICP_1_global-0.txt'\n", - "\n", - "#Run the simulations - 10 increments\n", - "pathfile = 'path_10.txt'\n", - "outputfile = 'results_EPICP_10.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, corate_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_10 = 'results_EPICP_10_global-0.txt'\n", - "\n", - "#Run the simulations - 100 increments\n", - "pathfile = 'path_100.txt'\n", - "outputfile = 'results_EPICP_100.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, corate_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_100 = 'results_EPICP_100_global-0.txt'\n", - "\n", - "#Run the simulations - 1000 increments\n", - "pathfile = 'path_1000.txt'\n", - "outputfile = 'results_EPICP_1000.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, corate_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1000 = 'results_EPICP_1000_global-0.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global_1 = path + outputfile_global_1\n", - "P_global_10 = path + outputfile_global_10\n", - "P_global_100 = path + outputfile_global_100\n", - "P_global_1000 = path + outputfile_global_1000\n", - "\n", - "#Get the data\n", - "e11_1, e22_1, e33_1, e12_1, e13_1, e23_1, s11_1, s22_1, s33_1, s12_1, s13_1, s23_1 = np.loadtxt(P_global_1, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1, T_1, Q_1, r_1 = np.loadtxt(P_global_1, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1, Wm_r_1, Wm_ir_1, Wm_d_1 = np.loadtxt(P_global_1, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "e11_10, e22_10, e33_10, e12_10, e13_10, e23_10, s11_10, s22_10, s33_10, s12_10, s13_10, s23_10 = np.loadtxt(P_global_10, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_10, T_10, Q_10, r_10 = np.loadtxt(P_global_10, usecols=(4,5,6,7), unpack=True)\n", - "Wm_10, Wm_r_10, Wm_ir_10, Wm_d_10 = np.loadtxt(P_global_10, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "e11_100, e22_100, e33_100, e12_100, e13_100, e23_100, s11_100, s22_100, s33_100, s12_100, s13_100, s23_100 = np.loadtxt(P_global_100, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_100, T_100, Q_100, r_100 = np.loadtxt(P_global_100, usecols=(4,5,6,7), unpack=True)\n", - "Wm_100, Wm_r_100, Wm_ir_100, Wm_d_100 = np.loadtxt(P_global_100, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "e11_1000, e22_1000, e33_1000, e12_1000, e13_1000, e23_1000, s11_1000, s22_1000, s33_1000, s12_1000, s13_1000, s23_1000 = np.loadtxt(P_global_1000, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1000, T_1000, Q_1000, r_1000 = np.loadtxt(P_global_1000, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1000, Wm_r_1000, Wm_ir_1000, Wm_d_1000 = np.loadtxt(P_global_1000, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "fig = plt.figure()\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(1, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11_1,s11_1, linestyle='None', marker='D', color='black', markersize=10, label='1 increment')\n", - "plt.plot(e11_10,s11_10, linestyle='None', marker='o', color='black', markersize=10, label='10 increment')\n", - "plt.plot(e11_100,s11_100, linestyle='None', marker='x', color='black', markersize=10, label='100 increments')\n", - "plt.plot(e11_1000,s11_1000, c='black', label='1000 increments')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(1, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "#1 increment\n", - "plt.plot(time_1,Wm_1, linestyle='None', marker='D', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_1,Wm_r_1, linestyle='None', marker='D', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_1,Wm_ir_1, linestyle='None', marker='D', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_1,Wm_d_1, linestyle='None', marker='D', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#10 increment\n", - "plt.plot(time_10,Wm_10, linestyle='None', marker='o', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_10,Wm_r_10, linestyle='None', marker='o', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_10,Wm_ir_10, linestyle='None', marker='o', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_10,Wm_d_10, linestyle='None', marker='o', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#100 increments\n", - "plt.plot(time_100,Wm_100, linestyle='None', marker='x', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_100,Wm_r_100, linestyle='None', marker='x', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_100,Wm_ir_100, linestyle='None', marker='x', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_100,Wm_d_100, linestyle='None', marker='x', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#1000 increments\n", - "plt.plot(time_1000,Wm_1000, c='black', label=r'$W_m$')\n", - "plt.plot(time_1000,Wm_r_1000, c='green', label=r'$W_m^r$')\n", - "plt.plot(time_1000,Wm_ir_1000, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time_1000,Wm_d_1000, c='red', label=r'$W_m^d$')\n", - "##\n", - "plt.legend(loc=2)\n", - "\n", - "plt.show()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernel_info": { - "name": "python2" - }, - "kernelspec": { - "display_name": "simcoon-scratch", - "language": "python", - "name": "simcoon-scratch" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - }, - "nteract": { - "version": "0.15.0" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/data/output.dat b/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/data/output.dat deleted file mode 100755 index cc0b9ba19..000000000 --- a/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/data/output.dat +++ /dev/null @@ -1,16 +0,0 @@ -#Output_values -strain_type 0 -nb_strain 6 -0 1 2 3 4 5 -stress_type 4 -nb_stress 6 -0 1 2 3 4 5 - -Rotation_type 0 -Tangent_type 0 -T 1 - -Number_of_wanted_internal_variables 0 - -#Block #type_1_N_2_T #every -1 1 1 \ No newline at end of file diff --git a/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/data/path.txt b/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/data/path.txt deleted file mode 100644 index ce359e609..000000000 --- a/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/data/path.txt +++ /dev/null @@ -1,85 +0,0 @@ -#Initial_temperature -293.15 -#Number_of_blocks -1 - -#Block -1 -#Loading_type -1 -#Control_type(NLGEOM) -1 -#Repeat -1 -#Steps -5 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1. -#Dn_inc 0.001 -#time -300 -#Consigne -S 1000 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 293.15 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1. -#Dn_inc 0.001 -#time -300 -#Consigne -S -1100 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 293.15 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1. -#Dn_inc 0.001 -#time -300 -#Consigne -S 1200 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 293.15 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1. -#Dn_inc 0.001 -#time -300 -#Consigne -S -1300 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 293.15 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1. -#Dn_inc 0.001 -#time -300 -#Consigne -S 1500 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 293.15 diff --git a/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/data/path_1.txt b/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/data/path_1.txt deleted file mode 100644 index fd9d8a0a9..000000000 --- a/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/data/path_1.txt +++ /dev/null @@ -1,29 +0,0 @@ -#Initial_temperature -293.15 -#Number_of_blocks -1 - -#Block -1 -#Loading_type -1 -#Control_type(NLGEOM) -1 -#Repeat -1 -#Steps -1 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1. -#Dn_inc 1 -#time -300 -#Consigne -S 1000 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 293.15 \ No newline at end of file diff --git a/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/data/path_10.txt b/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/data/path_10.txt deleted file mode 100644 index beab7b094..000000000 --- a/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/data/path_10.txt +++ /dev/null @@ -1,29 +0,0 @@ -#Initial_temperature -293.15 -#Number_of_blocks -1 - -#Block -1 -#Loading_type -1 -#Control_type(NLGEOM) -1 -#Repeat -1 -#Steps -1 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1. -#Dn_inc 0.1 -#time -300 -#Consigne -S 1000 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 293.15 \ No newline at end of file diff --git a/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/data/path_100.txt b/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/data/path_100.txt deleted file mode 100644 index 6962debd4..000000000 --- a/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/data/path_100.txt +++ /dev/null @@ -1,29 +0,0 @@ -#Initial_temperature -293.15 -#Number_of_blocks -1 - -#Block -1 -#Loading_type -1 -#Control_type(NLGEOM) -1 -#Repeat -1 -#Steps -1 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1. -#Dn_inc 0.01 -#time -300 -#Consigne -S 1000 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 293.15 \ No newline at end of file diff --git a/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/data/path_1000.txt b/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/data/path_1000.txt deleted file mode 100644 index 5e632550d..000000000 --- a/Notebooks/Umats/Mechanical/Plasticity/Loops_iso/data/path_1000.txt +++ /dev/null @@ -1,29 +0,0 @@ -#Initial_temperature -293.15 -#Number_of_blocks -1 - -#Block -1 -#Loading_type -1 -#Control_type(NLGEOM) -1 -#Repeat -1 -#Steps -1 - -#Mode -1 -#Dn_init 1. -#Dn_mini 1. -#Dn_inc 0.001 -#time -300 -#Consigne -S 1000 -S 0 S 0 -S 0 S 0 S 0 -#Consigne_T -T 293.15 \ No newline at end of file diff --git a/Notebooks/Umats/Mechanical/Prony_N/Prony_N.ipynb b/Notebooks/Umats/Mechanical/Prony_N/Prony_N.ipynb deleted file mode 100644 index fea981d32..000000000 --- a/Notebooks/Umats/Mechanical/Prony_N/Prony_N.ipynb +++ /dev/null @@ -1,314 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Viscoelasticity : Generalized Maxwell model" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import pylab\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import rc\n", - "import simcoon as sim\n", - "import os\n", - "from IPython.display import HTML\n", - "dir = os.path.dirname(os.path.realpath('__file__'))\n", - "\n", - "plt.rc('text', usetex=True)\n", - "plt.rc('font', family='serif')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Poynting-Thomson constitutive law implemented in SMART+ is a rate dependent, isotropic, Zener-type linear viscoelastic model that consider thermal strains\n", - "\n", - "Nine parameters are required for the thermomechanical version: \n", - "\n", - "1. The Thermoelastic Young's modulus $E_0$,\n", - "2. The Thermoelastic Poisson's ratio $\\nu_0$,\n", - "3. The Thermoelastic coefficient of thermal expansion $\\alpha_0$,\n", - "4. The Viscoelastic Young's modulus of Zener branch $E_1$,\n", - "5. The Viscoelastic Poisson's ratio of Zener branch $\\nu_1$,\n", - "6. The Viscoelastic Bulk viscosity of Zener branch $\\eta_B$\n", - "7. The Viscoelastic shear viscosity of Zener branch $\\eta_s$\n", - "\n", - "In 'smartplus' the viscoelastic material constitutive law is implemented using a *fast scalar updating method*. The updated stress is provided for 1D, plane stress, and generalized plane strain/3D analysis according to the forms of elastic isotropic materials.\n", - "The updated work, and internal heat production $r$ are determined with the algorithm presented in the *simcoon* documentation.\n", - "\n", - "As a start we should input the name of the UMAT as well as the list of parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "umat_name = 'PRONK' #This is the 5 character code for the elastic-plastic subroutine\n", - "\n", - "E_0 = 9400\n", - "nu_0 = 0.4\n", - "alpha = 0.\n", - "n_prony = 5\n", - "#Get the material data\n", - "mat_file = dir + '/data/Prony_raw.dat'\n", - "E_i, nu_i, etaB_i, etaS_i = np.loadtxt(mat_file, usecols=(0,1,2,3), unpack=True)\n", - "\n", - "#nstatev depends on the number of branches\n", - "nstatev = 8+7*n_prony #The number of scalar variables required, only the initial temperature is stored here\n", - "\n", - "##local orientation\n", - "psi_rve = 0.\n", - "theta_rve = 0.\n", - "phi_rve = 0.\n", - "solver_type = 0\n", - "\n", - "#Define the properties\n", - "props = np.array([E_0, nu_0, alpha, n_prony])\n", - "\n", - "for i in range(0,n_prony):\n", - " props = np.append(props, E_i[i])\n", - " props = np.append(props,nu_i[i])\n", - " props = np.append(props,etaB_i[i])\n", - " props = np.append(props,etaS_i[i]) \n", - "\n", - "path_data = 'data'\n", - "path_results = 'results'\n", - "\n", - "#Run the simulation\n", - "pathfile = 'path.txt'\n", - "outputfile = 'results_PRONY.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#prepare the load\n", - "fig = plt.figure()\n", - "outputfile_global = 'results_PRONY_global-0.txt'\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global = path + outputfile_global\n", - "\n", - "#Get the data\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(P_global, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time, T, Q, r = np.loadtxt(P_global, usecols=(4,5,6,7), unpack=True)\n", - "Wm, Wm_r, Wm_ir, Wm_d = np.loadtxt(P_global, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(1, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11,s11, c='black', label='direction 1')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(1, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "plt.plot(time,Wm, c='black', label=r'$W_m$')\n", - "plt.plot(time,Wm_r, c='green', label=r'$W_m^r$')\n", - "plt.plot(time,Wm_ir, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time,Wm_d, c='red', label=r'$W_m^d$')\n", - "plt.legend(loc=2)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test now the increments " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "#Run the simulations - 1 increment\n", - "pathfile = 'path_1.txt'\n", - "outputfile = 'results_PRONK_1.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1 = 'results_PRONK_1_global-0.txt'\n", - "\n", - "#Run the simulations - 10 increments\n", - "pathfile = 'path_10.txt'\n", - "outputfile = 'results_PRONK_10.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_10 = 'results_PRONK_10_global-0.txt'\n", - "\n", - "#Run the simulations - 100 increments\n", - "pathfile = 'path_100.txt'\n", - "outputfile = 'results_PRONK_100.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_100 = 'results_PRONK_100_global-0.txt'\n", - "\n", - "#Run the simulations - 1000 increments\n", - "pathfile = 'path_1000.txt'\n", - "outputfile = 'results_PRONK_1000.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1000 = 'results_PRONK_1000_global-0.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global_1 = path + outputfile_global_1\n", - "P_global_10 = path + outputfile_global_10\n", - "P_global_100 = path + outputfile_global_100\n", - "P_global_1000 = path + outputfile_global_1000\n", - "\n", - "#Get the data\n", - "e11_1, e22_1, e33_1, e12_1, e13_1, e23_1, s11_1, s22_1, s33_1, s12_1, s13_1, s23_1 = np.loadtxt(P_global_1, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1, T_1, Q_1, r_1 = np.loadtxt(P_global_1, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1, Wm_r_1, Wm_ir_1, Wm_d_1 = np.loadtxt(P_global_1, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "e11_10, e22_10, e33_10, e12_10, e13_10, e23_10, s11_10, s22_10, s33_10, s12_10, s13_10, s23_10 = np.loadtxt(P_global_10, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_10, T_10, Q_10, r_10 = np.loadtxt(P_global_10, usecols=(4,5,6,7), unpack=True)\n", - "Wm_10, Wm_r_10, Wm_ir_10, Wm_d_10 = np.loadtxt(P_global_10, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "e11_100, e22_100, e33_100, e12_100, e13_100, e23_100, s11_100, s22_100, s33_100, s12_100, s13_100, s23_100 = np.loadtxt(P_global_100, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_100, T_100, Q_100, r_100 = np.loadtxt(P_global_100, usecols=(4,5,6,7), unpack=True)\n", - "Wm_100, Wm_r_100, Wm_ir_100, Wm_d_100 = np.loadtxt(P_global_100, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "e11_1000, e22_1000, e33_1000, e12_1000, e13_1000, e23_1000, s11_1000, s22_1000, s33_1000, s12_1000, s13_1000, s23_1000 = np.loadtxt(P_global_1000, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1000, T_1000, Q_1000, r_1000 = np.loadtxt(P_global_1000, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1000, Wm_r_1000, Wm_ir_1000, Wm_d_1000 = np.loadtxt(P_global_1000, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "fig = plt.figure()\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(1, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11_1,s11_1, linestyle='None', marker='D', color='black', markersize=10, label='1 increment')\n", - "plt.plot(e11_10,s11_10, linestyle='None', marker='o', color='black', markersize=10, label='10 increment')\n", - "plt.plot(e11_100,s11_100, linestyle='None', marker='x', color='black', markersize=10, label='100 increments')\n", - "plt.plot(e11_1000,s11_1000, c='black', label='1000 increments')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(1, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "#1 increment\n", - "plt.plot(time_1,Wm_1, linestyle='None', marker='D', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_1,Wm_r_1, linestyle='None', marker='D', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_1,Wm_ir_1, linestyle='None', marker='D', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_1,Wm_d_1, linestyle='None', marker='D', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#10 increment\n", - "plt.plot(time_10,Wm_10, linestyle='None', marker='o', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_10,Wm_r_10, linestyle='None', marker='o', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_10,Wm_ir_10, linestyle='None', marker='o', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_10,Wm_d_10, linestyle='None', marker='o', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#100 increments\n", - "plt.plot(time_100,Wm_100, linestyle='None', marker='x', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_100,Wm_r_100, linestyle='None', marker='x', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_100,Wm_ir_100, linestyle='None', marker='x', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_100,Wm_d_100, linestyle='None', marker='x', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#1000 increments\n", - "plt.plot(time_1000,Wm_1000, c='black', label=r'$W_m$')\n", - "plt.plot(time_1000,Wm_r_1000, c='green', label=r'$W_m^r$')\n", - "plt.plot(time_1000,Wm_ir_1000, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time_1000,Wm_d_1000, c='red', label=r'$W_m^d$')\n", - "##\n", - "plt.legend(loc=2)\n", - "\n", - "plt.show()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Notebooks/Umats/Mechanical/SMA_TR/SMA_TR.ipynb b/Notebooks/Umats/Mechanical/SMA_TR/SMA_TR.ipynb deleted file mode 100644 index da38208d8..000000000 --- a/Notebooks/Umats/Mechanical/SMA_TR/SMA_TR.ipynb +++ /dev/null @@ -1,376 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SMA model" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import pylab\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import rc\n", - "import simcoon as sim\n", - "import os\n", - "from IPython.display import HTML\n", - "dir = os.path.dirname(os.path.realpath('__file__'))\n", - "\n", - "plt.rc('text', usetex=True)\n", - "plt.rc('font', family='serif')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The SMA (transformation-only) constitutive law implemented in SMART+ is a rate independent description of phase transformation where the two transformations, i.e. austenite $\\rightarrow$ martensite, and martensite $\\rightarrow$ are considered as independant mechanisms.\n", - "\n", - "27 parameters are required for the thermomechanical version: \n", - "1. props[0] : density\n", - "2. props[1] : specific heat capacity\n", - "3. props[2] : flagT: 0 transformation temperatures linearly extrapolated; 1 : smooth temperatures\n", - "4. props[3] : EA: Young's modulus of Austenite\n", - "5. props[4] : EM: Young's modulus of Martensite\n", - "6. props[5] : nuA : Poisson's ratio of Austenite\n", - "7. props[6] : nuM : Poisson's ratio of Martensite\n", - "8. props[7] : alphaA_iso : CTE of Austenite\n", - "9. props[8] : alphaM_iso : CTE of Martensite\n", - "10. props[9] : Hmin : Minimal transformation strain magnitude\n", - "11. props[10] : Hmax : Maximal transformation strain magnitude\n", - "12. props[11] : k1 : Exponential evolution of transformation strain magnitude\n", - "13. props[12] : sigmacrit : Critical stress for change of transformation strain magnitude\n", - "14. props[13]: C_A : Slope of martesnite -> austenite parameter\n", - "15. props[14]: C_M : Slope of austenite -> martensite parameter\n", - "16. props[15]: Ms0 : Martensite start at zero stress\n", - "17. props[16]: Mf0 : Martensite finish at zero stress\n", - "18. props[17]: As0 : Austenite start at zero stress\n", - "19. props[18]: Af0 : Austenite finish at zero stress\n", - "20. props[19]: n1 : Martensite start smooth exponent\n", - "21. props[20]: n2 : Martensite finish smooth exponent\n", - "22. props[21]: n3 : Austenite start smooth exponent\n", - "23. props[22]: n4 : Austenite finish smooth exponent\n", - "\n", - "24. props[23]: c_lambda : penalty function exponent start point\n", - "25. props[24]: p0_lambda : penalty function exponent limit penalty value\n", - "26. props[25]: n_lambda : penalty function power law exponent\n", - "27. props[26]: alpha_lambda : penalty function power law parameter\n", - "\n", - "In SMART+ the SMA material constitutive law is implemented using a *return mapping algorithm*, with use of the *convex cutting plane* algorithm (Simo and Hughes, 1998). The updated stress is provided for 1D, plane stress, and generalized plane strain/3D analysis according to the forms of elastic isotropic materials.\n", - "The updated work, and internal heat production $r$ are determined with the algorithm presented in the *simcoon* documentation.\n", - "\n", - "As a start we should input the name of the UMAT as well as the list of parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "solver(): incompatible function arguments. The following argument types are supported:\n 1. (arg0: str, arg1: numpy.ndarray[numpy.float64], arg2: int, arg3: float, arg4: float, arg5: float, arg6: int, arg7: int, arg8: str, arg9: str, arg10: str, arg11: str) -> None\n\nInvoked with: 'SMAUT', array([0.0000e+00, 6.7538e+04, 6.7538e+04, 3.4900e-01, 3.4900e-01,\n 1.0000e-06, 1.0000e-06, 0.0000e+00, 4.1800e-02, 2.1000e-02,\n 0.0000e+00, 1.0000e+01, 1.0000e+01, 2.5000e+02, 2.3000e+02,\n 2.6000e+02, 2.8000e+02, 2.0000e-01, 2.0000e-01, 2.0000e-01,\n 2.0000e-01, 3.0000e+02, 1.4000e+00, 2.0000e+00, 1.0000e-06,\n 1.0000e-03, 1.0000e+00, 1.0000e+08]), 50, 0.0, 0.0, 0.0, 0, 'data', 'results', 'path.txt', 'results_SMA.txt'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 50\u001b[39m\n\u001b[32m 48\u001b[39m pathfile = \u001b[33m'\u001b[39m\u001b[33mpath.txt\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m 49\u001b[39m outputfile = \u001b[33m'\u001b[39m\u001b[33mresults_SMA.txt\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m50\u001b[39m \u001b[43msim\u001b[49m\u001b[43m.\u001b[49m\u001b[43msolver\u001b[49m\u001b[43m(\u001b[49m\u001b[43mumat_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprops\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnstatev\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpsi_rve\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtheta_rve\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mphi_rve\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msolver_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath_results\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpathfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutputfile\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[31mTypeError\u001b[39m: solver(): incompatible function arguments. The following argument types are supported:\n 1. (arg0: str, arg1: numpy.ndarray[numpy.float64], arg2: int, arg3: float, arg4: float, arg5: float, arg6: int, arg7: int, arg8: str, arg9: str, arg10: str, arg11: str) -> None\n\nInvoked with: 'SMAUT', array([0.0000e+00, 6.7538e+04, 6.7538e+04, 3.4900e-01, 3.4900e-01,\n 1.0000e-06, 1.0000e-06, 0.0000e+00, 4.1800e-02, 2.1000e-02,\n 0.0000e+00, 1.0000e+01, 1.0000e+01, 2.5000e+02, 2.3000e+02,\n 2.6000e+02, 2.8000e+02, 2.0000e-01, 2.0000e-01, 2.0000e-01,\n 2.0000e-01, 3.0000e+02, 1.4000e+00, 2.0000e+00, 1.0000e-06,\n 1.0000e-03, 1.0000e+00, 1.0000e+08]), 50, 0.0, 0.0, 0.0, 0, 'data', 'results', 'path.txt', 'results_SMA.txt'" - ] - } - ], - "source": [ - "umat_name = 'SMAUT' #This is the 5 character code for the elastic-plastic subroutine\n", - "nstatev = 50 #The number of scalar variables required, only the initial temperature is stored here\n", - "\n", - "flagT = 0\n", - "E_A = 67538\n", - "E_M = 67538\n", - "nu_A = 0.349\n", - "nu_M = 0.349\n", - "alphaA = 1.E-6\n", - "alphaM = 1.E-6\n", - "Hmin = 0.\n", - "Hmax = 0.0418\n", - "k1 = 0.021\n", - "sigmacrit = 0.0\n", - "C_A = 10\n", - "C_M = 10\n", - "Ms0 = 250\n", - "Mf0 = 230\n", - "As0 = 260\n", - "Af0 = 280\n", - "n1 = 0.2\n", - "n2 = 0.2\n", - "n3 = 0.2\n", - "n4 = 0.2\n", - "sigmacaliber = 300\n", - "b_prager = 1.4\n", - "n_prager = 2.\n", - "c_lambda = 1.E-6\n", - "p0_lambda = 1.E-3\n", - "n_lambda = 1.0\n", - "alpha_lambda = 1.E8\n", - "\n", - "##local orientation\n", - "psi_rve = 0.\n", - "theta_rve = 0.\n", - "phi_rve = 0.\n", - "solver_type = 0\n", - "corate_type = 3\n", - "\n", - "#Define the properties\n", - "#Define the properties\n", - "props = np.array([flagT, E_A, E_M, nu_A, nu_M, alphaA, alphaM, Hmin, Hmax, k1, \\\n", - " sigmacrit, C_A, C_M, Ms0, Mf0, As0, Af0, n1, n2, n3, n4, sigmacaliber, b_prager, n_prager, \\\n", - " c_lambda, p0_lambda, n_lambda, alpha_lambda])\n", - "path_data = 'data'\n", - "path_results = 'results'\n", - "\n", - "#Run the simulation\n", - "pathfile = 'path.txt'\n", - "outputfile = 'results_SMA.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, corate_type, path_data, path_results, pathfile, outputfile)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "/Users/ychemisky/scratch/simcoon/Notebooks/Umats/Mechanical/SMA_TR/results/results_SMA_global-0.txt not found.", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 6\u001b[39m P_global = path + outputfile_global\n\u001b[32m 8\u001b[39m \u001b[38;5;66;03m#Get the data\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mloadtxt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mP_global\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43musecols\u001b[49m\u001b[43m=\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m8\u001b[39;49m\u001b[43m,\u001b[49m\u001b[32;43m9\u001b[39;49m\u001b[43m,\u001b[49m\u001b[32;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[32;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[32;43m12\u001b[39;49m\u001b[43m,\u001b[49m\u001b[32;43m13\u001b[39;49m\u001b[43m,\u001b[49m\u001b[32;43m14\u001b[39;49m\u001b[43m,\u001b[49m\u001b[32;43m15\u001b[39;49m\u001b[43m,\u001b[49m\u001b[32;43m16\u001b[39;49m\u001b[43m,\u001b[49m\u001b[32;43m17\u001b[39;49m\u001b[43m,\u001b[49m\u001b[32;43m18\u001b[39;49m\u001b[43m,\u001b[49m\u001b[32;43m19\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munpack\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 10\u001b[39m time, T, Q, r = np.loadtxt(P_global, usecols=(\u001b[32m4\u001b[39m,\u001b[32m5\u001b[39m,\u001b[32m6\u001b[39m,\u001b[32m7\u001b[39m), unpack=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m 11\u001b[39m Wm, Wm_r, Wm_ir, Wm_d = np.loadtxt(P_global, usecols=(\u001b[32m20\u001b[39m,\u001b[32m21\u001b[39m,\u001b[32m22\u001b[39m,\u001b[32m23\u001b[39m), unpack=\u001b[38;5;28;01mTrue\u001b[39;00m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/3MAH/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py:1395\u001b[39m, in \u001b[36mloadtxt\u001b[39m\u001b[34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin, encoding, max_rows, quotechar, like)\u001b[39m\n\u001b[32m 1392\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(delimiter, \u001b[38;5;28mbytes\u001b[39m):\n\u001b[32m 1393\u001b[39m delimiter = delimiter.decode(\u001b[33m'\u001b[39m\u001b[33mlatin1\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m1395\u001b[39m arr = \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcomment\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcomment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdelimiter\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdelimiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1396\u001b[39m \u001b[43m \u001b[49m\u001b[43mconverters\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconverters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskiplines\u001b[49m\u001b[43m=\u001b[49m\u001b[43mskiprows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43musecols\u001b[49m\u001b[43m=\u001b[49m\u001b[43musecols\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1397\u001b[39m \u001b[43m \u001b[49m\u001b[43munpack\u001b[49m\u001b[43m=\u001b[49m\u001b[43munpack\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mndmin\u001b[49m\u001b[43m=\u001b[49m\u001b[43mndmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1398\u001b[39m \u001b[43m \u001b[49m\u001b[43mmax_rows\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmax_rows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquote\u001b[49m\u001b[43m=\u001b[49m\u001b[43mquotechar\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1400\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m arr\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/3MAH/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py:1022\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(fname, delimiter, comment, quote, imaginary_unit, usecols, skiplines, max_rows, converters, ndmin, unpack, dtype, encoding)\u001b[39m\n\u001b[32m 1020\u001b[39m fname = os.fspath(fname)\n\u001b[32m 1021\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fname, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m-> \u001b[39m\u001b[32m1022\u001b[39m fh = \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlib\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_datasource\u001b[49m\u001b[43m.\u001b[49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mrt\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1023\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m encoding \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 1024\u001b[39m encoding = \u001b[38;5;28mgetattr\u001b[39m(fh, \u001b[33m'\u001b[39m\u001b[33mencoding\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mlatin1\u001b[39m\u001b[33m'\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/3MAH/lib/python3.12/site-packages/numpy/lib/_datasource.py:192\u001b[39m, in \u001b[36mopen\u001b[39m\u001b[34m(path, mode, destpath, encoding, newline)\u001b[39m\n\u001b[32m 155\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 156\u001b[39m \u001b[33;03mOpen `path` with `mode` and return the file object.\u001b[39;00m\n\u001b[32m 157\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 188\u001b[39m \n\u001b[32m 189\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 191\u001b[39m ds = DataSource(destpath)\n\u001b[32m--> \u001b[39m\u001b[32m192\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mds\u001b[49m\u001b[43m.\u001b[49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/3MAH/lib/python3.12/site-packages/numpy/lib/_datasource.py:529\u001b[39m, in \u001b[36mDataSource.open\u001b[39m\u001b[34m(self, path, mode, encoding, newline)\u001b[39m\n\u001b[32m 526\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _file_openers[ext](found, mode=mode,\n\u001b[32m 527\u001b[39m encoding=encoding, newline=newline)\n\u001b[32m 528\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m529\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m not found.\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[31mFileNotFoundError\u001b[39m: /Users/ychemisky/scratch/simcoon/Notebooks/Umats/Mechanical/SMA_TR/results/results_SMA_global-0.txt not found." - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#prepare the load\n", - "fig = plt.figure()\n", - "outputfile_global = 'results_SMA_global-0.txt'\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global = path + outputfile_global\n", - "\n", - "#Get the data\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(P_global, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time, T, Q, r = np.loadtxt(P_global, usecols=(4,5,6,7), unpack=True)\n", - "Wm, Wm_r, Wm_ir, Wm_d = np.loadtxt(P_global, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(1, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11,s11, c='black', label='direction 1')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(1, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "plt.plot(time,Wm, c='black', label=r'$W_m$')\n", - "plt.plot(time,Wm_r, c='green', label=r'$W_m^r$')\n", - "plt.plot(time,Wm_ir, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time,Wm_d, c='red', label=r'$W_m^d$')\n", - "plt.legend(loc=2)\n", - "\n", - "plt.savefig('example.pdf', format='pdf')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test now the increments " - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "#Run the simulations - 1 increment\n", - "pathfile = 'path_1.txt'\n", - "outputfile = 'results_SMA_1.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1 = 'results_SMA_1_global-0.txt'\n", - "\n", - "#Run the simulations - 10 increments\n", - "pathfile = 'path_20.txt'\n", - "outputfile = 'results_SMA_20.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_20 = 'results_SMA_20_global-0.txt'\n", - "\n", - "#Run the simulations - 100 increments\n", - "pathfile = 'path_100.txt'\n", - "outputfile = 'results_SMA_100.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_100 = 'results_SMA_100_global-0.txt'\n", - "\n", - "#Run the simulations - 1000 increments\n", - "pathfile = 'path_1000.txt'\n", - "outputfile = 'results_SMA_1000.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1000 = 'results_SMA_1000_global-0.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global_1 = path + outputfile_global_1\n", - "P_global_20 = path + outputfile_global_20\n", - "P_global_100 = path + outputfile_global_100\n", - "P_global_1000 = path + outputfile_global_1000\n", - "\n", - "#Get the data\n", - "e11_1, e22_1, e33_1, e12_1, e13_1, e23_1, s11_1, s22_1, s33_1, s12_1, s13_1, s23_1 = np.loadtxt(P_global_1, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1, T_1, Q_1, r_1 = np.loadtxt(P_global_1, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1, Wm_r_1, Wm_ir_1, Wm_d_1 = np.loadtxt(P_global_1, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "e11_10, e22_10, e33_10, e12_10, e13_10, e23_10, s11_10, s22_10, s33_10, s12_10, s13_10, s23_10 = np.loadtxt(P_global_10, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_10, T_10, Q_10, r_10 = np.loadtxt(P_global_20, usecols=(4,5,6,7), unpack=True)\n", - "Wm_10, Wm_r_10, Wm_ir_10, Wm_d_10 = np.loadtxt(P_global_20, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "e11_100, e22_100, e33_100, e12_100, e13_100, e23_100, s11_100, s22_100, s33_100, s12_100, s13_100, s23_100 = np.loadtxt(P_global_100, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_100, T_100, Q_100, r_100 = np.loadtxt(P_global_100, usecols=(4,5,6,7), unpack=True)\n", - "Wm_100, Wm_r_100, Wm_ir_100, Wm_d_100 = np.loadtxt(P_global_100, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "e11_1000, e22_1000, e33_1000, e12_1000, e13_1000, e23_1000, s11_1000, s22_1000, s33_1000, s12_1000, s13_1000, s23_1000 = np.loadtxt(P_global_1000, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1000, T_1000, Q_1000, r_1000 = np.loadtxt(P_global_1000, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1000, Wm_r_1000, Wm_ir_1000, Wm_d_1000 = np.loadtxt(P_global_1000, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "fig = plt.figure()\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(1, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11_1,s11_1, linestyle='None', marker='D', color='black', markersize=10, label='1 increment')\n", - "plt.plot(e11_10,s11_10, linestyle='None', marker='o', color='black', markersize=10, label='10 increment')\n", - "plt.plot(e11_100,s11_100, linestyle='None', marker='x', color='black', markersize=10, label='100 increments')\n", - "plt.plot(e11_1000,s11_1000, c='black', label='1000 increments')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(1, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "#1 increment\n", - "plt.plot(time_1,Wm_1, linestyle='None', marker='D', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_1,Wm_r_1, linestyle='None', marker='D', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_1,Wm_ir_1, linestyle='None', marker='D', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_1,Wm_d_1, linestyle='None', marker='D', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#10 increment\n", - "plt.plot(time_10,Wm_10, linestyle='None', marker='o', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_10,Wm_r_10, linestyle='None', marker='o', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_10,Wm_ir_10, linestyle='None', marker='o', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_10,Wm_d_10, linestyle='None', marker='o', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#100 increments\n", - "plt.plot(time_100,Wm_100, linestyle='None', marker='x', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_100,Wm_r_100, linestyle='None', marker='x', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_100,Wm_ir_100, linestyle='None', marker='x', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_100,Wm_d_100, linestyle='None', marker='x', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#1000 increments\n", - "plt.plot(time_1000,Wm_1000, c='black', label=r'$W_m$')\n", - "plt.plot(time_1000,Wm_r_1000, c='green', label=r'$W_m^r$')\n", - "plt.plot(time_1000,Wm_ir_1000, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time_1000,Wm_d_1000, c='red', label=r'$W_m^d$')\n", - "##\n", - "plt.legend(loc=2)\n", - "\n", - "plt.savefig('example2.pdf', format='pdf')\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "3MAH", - "language": "python", - "name": "3mah" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/Notebooks/Umats/Mechanical/Zener/Zener.ipynb b/Notebooks/Umats/Mechanical/Zener/Zener.ipynb deleted file mode 100644 index b6fd9ccbd..000000000 --- a/Notebooks/Umats/Mechanical/Zener/Zener.ipynb +++ /dev/null @@ -1,313 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Viscoelasticity : Poynting-Thomson rheological model" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import pylab\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import rc\n", - "import simcoon as sim\n", - "import os\n", - "from IPython.display import HTML\n", - "dir = os.path.dirname(os.path.realpath('__file__'))\n", - "\n", - "plt.rc('text', usetex=True)\n", - "plt.rc('font', family='serif')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Poynting-Thomson constitutive law implemented in SMART+ is a rate dependent, isotropic, Zener-type linear viscoelastic model that consider thermal strains\n", - "\n", - "Nine parameters are required for the thermomechanical version: \n", - "1. The Thermoelastic Young's modulus $E_0$,\n", - "2. The Thermoelastic Poisson's ratio $\\nu_0$,\n", - "3. The Thermoelastic coefficient of thermal expansion $\\alpha_0$,\n", - "4. The Viscoelastic Young's modulus of Zener branch $E_1$,\n", - "5. The Viscoelastic Poisson's ratio of Zener branch $\\nu_1$,\n", - "6. The Viscoelastic Bulk viscosity of Zener branch $\\eta_B$\n", - "7. The Viscoelastic shear viscosity of Zener branch $\\eta_s$\n", - "\n", - "In 'smartplus' the viscoelastic material constitutive law is implemented using a *fast scalar updating method*. The updated stress is provided for 1D, plane stress, and generalized plane strain/3D analysis according to the forms of elastic isotropic materials.\n", - "The updated work, and internal heat production $r$ are determined with the algorithm presented in the *simcoon* documentation.\n", - "\n", - "As a start we should input the name of the UMAT as well as the list of parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "umat_name = 'ZENER' #This is the 5 character code for the elastic-plastic subroutine\n", - "nstatev = 8 #The number of scalar variables required, only the initial temperature is stored here\n", - "\n", - "E_0 = 3000\n", - "nu_0 = 0.4\n", - "alpha = 0.\n", - "E_1 = 100.\n", - "nu_1 = 0.3\n", - "eta_S = 4000.0\n", - "eta_B = eta_S/4.0\n", - "\n", - "\n", - "##local orientation\n", - "psi_rve = 0.\n", - "theta_rve = 0.\n", - "phi_rve = 0.\n", - "solver_type = 0\n", - "\n", - "#Define the properties\n", - "props = np.array([E_0, nu_0, alpha, E_1, nu_1, eta_B, eta_S])\n", - "path_data = 'data'\n", - "path_results = 'results'\n", - "\n", - "#Run the simulation\n", - "pathfile = 'path.txt'\n", - "outputfile = 'results_ZENER.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#prepare the load\n", - "fig = plt.figure()\n", - "outputfile_global = 'results_ZENER_global-0.txt'\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global = path + outputfile_global\n", - "\n", - "#Get the data\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(P_global, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time, T, Q, r = np.loadtxt(P_global, usecols=(4,5,6,7), unpack=True)\n", - "Wm, Wm_r, Wm_ir, Wm_d = np.loadtxt(P_global, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(1, 1, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11,s11, c='black', label='direction 1')\n", - "plt.legend(loc=2)\n", - "\n", - "#ax = fig.add_subplot(1, 2, 2)\n", - "#plt.grid(True)\n", - "#plt.tick_params(axis='both', which='major', labelsize=15)\n", - "#plt.xlabel('time (s)', size = 15)\n", - "#plt.ylabel(r'$W_m$',size = 15)\n", - "#plt.plot(time,Wm, c='black', label=r'$W_m$')\n", - "#plt.plot(time,Wm_r, c='green', label=r'$W_m^r$')\n", - "#plt.plot(time,Wm_ir, c='blue', label=r'$W_m^{ir}$')\n", - "#plt.plot(time,Wm_d, c='red', label=r'$W_m^d$')\n", - "#plt.legend(loc=2)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test now the increments " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#Run the simulations - 1 increment\n", - "pathfile = 'path_1.txt'\n", - "outputfile = 'results_ZENER_1.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1 = 'results_ZENER_1_global-0.txt'\n", - "\n", - "#Run the simulations - 10 increments\n", - "pathfile = 'path_10.txt'\n", - "outputfile = 'results_ZENER_10.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_10 = 'results_ZENER_10_global-0.txt'\n", - "\n", - "#Run the simulations - 100 increments\n", - "pathfile = 'path_100.txt'\n", - "outputfile = 'results_ZENER_100.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_100 = 'results_ZENER_100_global-0.txt'\n", - "\n", - "#Run the simulations - 1000 increments\n", - "pathfile = 'path_1000.txt'\n", - "outputfile = 'results_ZENER_1000.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1000 = 'results_ZENER_1000_global-0.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global_1 = path + outputfile_global_1\n", - "P_global_10 = path + outputfile_global_10\n", - "P_global_100 = path + outputfile_global_100\n", - "P_global_1000 = path + outputfile_global_1000\n", - "\n", - "#Get the data\n", - "e11_1, e22_1, e33_1, e12_1, e13_1, e23_1, s11_1, s22_1, s33_1, s12_1, s13_1, s23_1 = np.loadtxt(P_global_1, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1, T_1, Q_1, r_1 = np.loadtxt(P_global_1, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1, Wm_r_1, Wm_ir_1, Wm_d_1 = np.loadtxt(P_global_1, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "e11_10, e22_10, e33_10, e12_10, e13_10, e23_10, s11_10, s22_10, s33_10, s12_10, s13_10, s23_10 = np.loadtxt(P_global_10, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_10, T_10, Q_10, r_10 = np.loadtxt(P_global_10, usecols=(4,5,6,7), unpack=True)\n", - "Wm_10, Wm_r_10, Wm_ir_10, Wm_d_10 = np.loadtxt(P_global_10, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "e11_100, e22_100, e33_100, e12_100, e13_100, e23_100, s11_100, s22_100, s33_100, s12_100, s13_100, s23_100 = np.loadtxt(P_global_100, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_100, T_100, Q_100, r_100 = np.loadtxt(P_global_100, usecols=(4,5,6,7), unpack=True)\n", - "Wm_100, Wm_r_100, Wm_ir_100, Wm_d_100 = np.loadtxt(P_global_100, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "e11_1000, e22_1000, e33_1000, e12_1000, e13_1000, e23_1000, s11_1000, s22_1000, s33_1000, s12_1000, s13_1000, s23_1000 = np.loadtxt(P_global_1000, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1000, T_1000, Q_1000, r_1000 = np.loadtxt(P_global_1000, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1000, Wm_r_1000, Wm_ir_1000, Wm_d_1000 = np.loadtxt(P_global_1000, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "fig = plt.figure()\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(1, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11_1,s11_1, linestyle='None', marker='D', color='black', markersize=10, label='1 increment')\n", - "plt.plot(e11_10,s11_10, linestyle='None', marker='o', color='black', markersize=10, label='10 increment')\n", - "plt.plot(e11_100,s11_100, linestyle='None', marker='x', color='black', markersize=10, label='100 increments')\n", - "plt.plot(e11_1000,s11_1000, c='black', label='1000 increments')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(1, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "#1 increment\n", - "plt.plot(time_1,Wm_1, linestyle='None', marker='D', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_1,Wm_r_1, linestyle='None', marker='D', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_1,Wm_ir_1, linestyle='None', marker='D', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_1,Wm_d_1, linestyle='None', marker='D', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#10 increment\n", - "plt.plot(time_10,Wm_10, linestyle='None', marker='o', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_10,Wm_r_10, linestyle='None', marker='o', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_10,Wm_ir_10, linestyle='None', marker='o', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_10,Wm_d_10, linestyle='None', marker='o', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#100 increments\n", - "plt.plot(time_100,Wm_100, linestyle='None', marker='x', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_100,Wm_r_100, linestyle='None', marker='x', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_100,Wm_ir_100, linestyle='None', marker='x', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_100,Wm_d_100, linestyle='None', marker='x', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#1000 increments\n", - "plt.plot(time_1000,Wm_1000, c='black', label=r'$W_m$')\n", - "plt.plot(time_1000,Wm_r_1000, c='green', label=r'$W_m^r$')\n", - "plt.plot(time_1000,Wm_ir_1000, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time_1000,Wm_d_1000, c='red', label=r'$W_m^d$')\n", - "##\n", - "plt.legend(loc=2)\n", - "\n", - "plt.show()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Notebooks/Umats/Mechanical/Zener_N/Zener_N.ipynb b/Notebooks/Umats/Mechanical/Zener_N/Zener_N.ipynb deleted file mode 100644 index 6e043489e..000000000 --- a/Notebooks/Umats/Mechanical/Zener_N/Zener_N.ipynb +++ /dev/null @@ -1,322 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Viscoelasticity : Generalized Zener model" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import pylab\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import rc\n", - "import simcoon as sim\n", - "import os\n", - "from IPython.display import HTML\n", - "dir = os.path.dirname(os.path.realpath('__file__'))\n", - "\n", - "plt.rc('text', usetex=True)\n", - "plt.rc('font', family='serif')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Poynting-Thomson constitutive law implemented in SMART+ is a rate dependent, isotropic, Zener-type linear viscoelastic model that consider thermal strains\n", - "\n", - "Nine parameters are required for the thermomechanical version: \n", - "\n", - "1. The Thermoelastic Young's modulus $E_0$,\n", - "2. The Thermoelastic Poisson's ratio $\\nu_0$,\n", - "3. The Thermoelastic coefficient of thermal expansion $\\alpha_0$,\n", - "4. The Viscoelastic Young's modulus of Zener branch $E_1$,\n", - "5. The Viscoelastic Poisson's ratio of Zener branch $\\nu_1$,\n", - "6. The Viscoelastic Bulk viscosity of Zener branch $\\eta_B$\n", - "7. The Viscoelastic shear viscosity of Zener branch $\\eta_s$\n", - "\n", - "In 'smartplus' the viscoelastic material constitutive law is implemented using a *fast scalar updating method*. The updated stress is provided for 1D, plane stress, and generalized plane strain/3D analysis according to the forms of elastic isotropic materials.\n", - "The updated work, and internal heat production $r$ are determined with the algorithm presented in the *simcoon* documentation.\n", - "\n", - "As a start we should input the name of the UMAT as well as the list of parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "umat_name = 'ZENNK' #This is the 5 character code for the elastic-plastic subroutine\n", - "\n", - "E_0 = 3000\n", - "nu_0 = 0.4\n", - "alpha = 0.\n", - "n_kelvin = 3\n", - "#Get the material data\n", - "mat_file = dir + '/data/Zener_raw.dat'\n", - "E_i, nu_i, etaB_i, etaS_i = np.loadtxt(mat_file, usecols=(0,1,2,3), unpack=True)\n", - "\n", - "#nstatev depends on the number of branches\n", - "nstatev = 8+7*n_kelvin #The number of scalar variables required, only the initial temperature is stored here\n", - "\n", - "##local orientation\n", - "psi_rve = 0.\n", - "theta_rve = 0.\n", - "phi_rve = 0.\n", - "solver_type = 0\n", - "\n", - "#Define the properties\n", - "props = np.array([E_0, nu_0, alpha, n_kelvin])\n", - "\n", - "for i in range(0,n_kelvin):\n", - " props = np.append(props, E_i[i])\n", - " props = np.append(props,nu_i[i])\n", - " props = np.append(props,etaB_i[i])\n", - " props = np.append(props,etaS_i[i]) \n", - "\n", - "path_data = 'data'\n", - "path_results = 'results'\n", - "\n", - "#Run the simulation\n", - "pathfile = 'path.txt'\n", - "outputfile = 'results_ZENER.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#prepare the load\n", - "fig = plt.figure()\n", - "outputfile_global = 'results_ZENER_global-0.txt'\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global = path + outputfile_global\n", - "\n", - "#Get the data\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(P_global, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time, T, Q, r = np.loadtxt(P_global, usecols=(4,5,6,7), unpack=True)\n", - "Wm, Wm_r, Wm_ir, Wm_d = np.loadtxt(P_global, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(1, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11,s11, c='black', label='direction 1')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(1, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "plt.plot(time,Wm, c='black', label=r'$W_m$')\n", - "plt.plot(time,Wm_r, c='green', label=r'$W_m^r$')\n", - "plt.plot(time,Wm_ir, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time,Wm_d, c='red', label=r'$W_m^d$')\n", - "plt.legend(loc=2)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test now the increments " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#Run the simulations - 1 increment\n", - "pathfile = 'path_1.txt'\n", - "outputfile = 'results_EPICP_1.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1 = 'results_EPICP_1_global-0.txt'\n", - "\n", - "#Run the simulations - 10 increments\n", - "pathfile = 'path_10.txt'\n", - "outputfile = 'results_EPICP_10.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_10 = 'results_EPICP_10_global-0.txt'\n", - "\n", - "#Run the simulations - 100 increments\n", - "pathfile = 'path_100.txt'\n", - "outputfile = 'results_EPICP_100.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_100 = 'results_EPICP_100_global-0.txt'\n", - "\n", - "#Run the simulations - 1000 increments\n", - "pathfile = 'path_1000.txt'\n", - "outputfile = 'results_EPICP_1000.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1000 = 'results_EPICP_1000_global-0.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global_1 = path + outputfile_global_1\n", - "P_global_10 = path + outputfile_global_10\n", - "P_global_100 = path + outputfile_global_100\n", - "P_global_1000 = path + outputfile_global_1000\n", - "\n", - "#Get the data\n", - "e11_1, e22_1, e33_1, e12_1, e13_1, e23_1, s11_1, s22_1, s33_1, s12_1, s13_1, s23_1 = np.loadtxt(P_global_1, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1, T_1, Q_1, r_1 = np.loadtxt(P_global_1, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1, Wm_r_1, Wm_ir_1, Wm_d_1 = np.loadtxt(P_global_1, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "e11_10, e22_10, e33_10, e12_10, e13_10, e23_10, s11_10, s22_10, s33_10, s12_10, s13_10, s23_10 = np.loadtxt(P_global_10, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_10, T_10, Q_10, r_10 = np.loadtxt(P_global_10, usecols=(4,5,6,7), unpack=True)\n", - "Wm_10, Wm_r_10, Wm_ir_10, Wm_d_10 = np.loadtxt(P_global_10, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "e11_100, e22_100, e33_100, e12_100, e13_100, e23_100, s11_100, s22_100, s33_100, s12_100, s13_100, s23_100 = np.loadtxt(P_global_100, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_100, T_100, Q_100, r_100 = np.loadtxt(P_global_100, usecols=(4,5,6,7), unpack=True)\n", - "Wm_100, Wm_r_100, Wm_ir_100, Wm_d_100 = np.loadtxt(P_global_100, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "e11_1000, e22_1000, e33_1000, e12_1000, e13_1000, e23_1000, s11_1000, s22_1000, s33_1000, s12_1000, s13_1000, s23_1000 = np.loadtxt(P_global_1000, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1000, T_1000, Q_1000, r_1000 = np.loadtxt(P_global_1000, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1000, Wm_r_1000, Wm_ir_1000, Wm_d_1000 = np.loadtxt(P_global_1000, usecols=(20,21,22,23), unpack=True)\n", - "\n", - "fig = plt.figure()\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(1, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11_1,s11_1, linestyle='None', marker='D', color='black', markersize=10, label='1 increment')\n", - "plt.plot(e11_10,s11_10, linestyle='None', marker='o', color='black', markersize=10, label='10 increment')\n", - "plt.plot(e11_100,s11_100, linestyle='None', marker='x', color='black', markersize=10, label='100 increments')\n", - "plt.plot(e11_1000,s11_1000, c='black', label='1000 increments')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(1, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "#1 increment\n", - "plt.plot(time_1,Wm_1, linestyle='None', marker='D', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_1,Wm_r_1, linestyle='None', marker='D', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_1,Wm_ir_1, linestyle='None', marker='D', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_1,Wm_d_1, linestyle='None', marker='D', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#10 increment\n", - "plt.plot(time_10,Wm_10, linestyle='None', marker='o', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_10,Wm_r_10, linestyle='None', marker='o', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_10,Wm_ir_10, linestyle='None', marker='o', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_10,Wm_d_10, linestyle='None', marker='o', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#100 increments\n", - "plt.plot(time_100,Wm_100, linestyle='None', marker='x', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_100,Wm_r_100, linestyle='None', marker='x', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_100,Wm_ir_100, linestyle='None', marker='x', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_100,Wm_d_100, linestyle='None', marker='x', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#1000 increments\n", - "plt.plot(time_1000,Wm_1000, c='black', label=r'$W_m$')\n", - "plt.plot(time_1000,Wm_r_1000, c='green', label=r'$W_m^r$')\n", - "plt.plot(time_1000,Wm_ir_1000, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time_1000,Wm_d_1000, c='red', label=r'$W_m^d$')\n", - "##\n", - "plt.legend(loc=2)\n", - "\n", - "plt.show()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Notebooks/Umats/Thermomechanical/Elasticity/ELISO.ipynb b/Notebooks/Umats/Thermomechanical/Elasticity/ELISO.ipynb deleted file mode 100644 index 8f85f0a94..000000000 --- a/Notebooks/Umats/Thermomechanical/Elasticity/ELISO.ipynb +++ /dev/null @@ -1,207 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Isotropic linear elasticity" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import pylab\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import rc\n", - "import simcoon as sim\n", - "import os\n", - "from IPython.display import HTML\n", - "dir = os.path.dirname(os.path.realpath('__file__'))\n", - "\n", - "plt.rc('text', usetex=True)\n", - "plt.rc('font', family='serif')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In thermoelastic isotropic materials three parameters are required: \n", - " \n", - "1. The Young modulus $E$,\n", - "2. The Poisson ratio $\\nu$,\n", - "3. The coefficient of thermal expansion $\\alpha$.\n", - "\n", - "The elastic stiffness tensor and the thermal expansion coefficients tensor are written in the Voigt notation formalism as\n", - "\n", - "$$\\boldsymbol{L}=\\left(\\begin{matrix} L_{1111} & L_{1122} & L_{1122} & 0 & 0 & 0 \\\\ L_{1122} & L_{1111} & L_{1122} & 0 & 0 & 0 \\\\ L_{1122} & L_{1122} & L_{1111} & 0 & 0 & 0 \\\\ 0 & 0 & 0 & L_{1212} & 0 & 0 \\\\ 0 & 0 & 0 & 0 & L_{1212} & 0 \\\\ 0 & 0 & 0 & 0 & 0 & L_{1212} \\end{matrix}\\right), \\quad \\boldsymbol{\\alpha}=\\left(\\begin{matrix} \\alpha & 0 & 0 \\\\ 0 & \\alpha & 0 \\\\ 0 & 0 & \\alpha \\end{matrix}\\right),$$\n", - "\n", - "with \n", - "$$L_{1111}=\\frac{E(1-\\nu)}{(1+\\nu)(1-2\\nu)}, \\quad L_{1122}=\\frac{E\\nu}{(1+\\nu)(1-2\\nu)}, \\quad L_{1212}=\\frac{E}{2(1+\\nu)}.$$\n", - "\n", - "Details on the the elastic stiffness tensor of isotropic media can be found in Lai et al 2010. The tangent stiffness tensor in this case is $\\boldsymbol{L}^t=\\boldsymbol{L}$. Moreover, the increment of the elastic strain is given by\n", - "\n", - "$$\\Delta\\varepsilon^{\\textrm{el}}_{ij}=\\Delta\\varepsilon^{\\textrm{tot}}_{ij}-\\alpha\\Delta T\\delta_{ij},$$\n", - "\n", - "where $\\delta_{ij}$ implies the Kronecker delta operator. In the 1D case only one component of stress is computed, through the relation \n", - "\n", - "$$\\sigma^{\\textrm{fin}}_{11}=\\sigma^{\\textrm{init}}_{11}+E\\Delta\\varepsilon^{\\textrm{el}}_{11}.$$\n", - "\n", - "In the plane stress case only three components of stress are computed, through the relations \n", - "\n", - "$$\\left(\\begin{matrix} \\sigma^{\\textrm{fin}}_{11} \\\\ \\sigma^{\\textrm{fin}}_{22} \\\\ \\sigma^{\\textrm{fin}}_{12} \\end{matrix}\\right) =\\left(\\begin{matrix} \\sigma^{\\textrm{init}}_{11} \\\\ \\sigma^{\\textrm{init}}_{22} \\\\ \\sigma^{\\textrm{init}}_{12} \\end{matrix}\\right)+\\frac{E}{1-\\nu^2} \\left(\\begin{matrix} 1 & \\nu & 0 \\\\ \\nu & 1 & 0 \\\\ 0 & 0 & \\frac{1-\\nu}{2} \\end{matrix}\\right) \\left(\\begin{matrix} \\Delta\\varepsilon^{\\textrm{el}}_{11} \\\\ \\Delta\\varepsilon^{\\textrm{el}}_{22} \\\\ 2\\Delta\\varepsilon^{\\textrm{el}}_{12} \\end{matrix}\\right).$$\n", - "\n", - "In the generalized plane strain/3D analysis case the stress tensor is computed through the relation\n", - "$$\\sigma^{\\textrm{fin}}_{ij}=\\sigma^{\\textrm{init}}_{ij}+L_{ijkl}~\\Delta\\varepsilon^{\\textrm{el}}_{kl}.$$" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "umat_name = 'ELISO' #This is the 5 character code for the elastic-isotropic subroutine\n", - "nstatev = 1 #The number of scalar variables required, only the initial temperature is stored here\n", - "\n", - "rho = 4.4\n", - "c_p = 0.656\n", - "E = 70000.\n", - "nu = 0.2\n", - "alpha = 1.E-5\n", - "\n", - "psi_rve = 0.\n", - "theta_rve = 0.\n", - "phi_rve = 0.\n", - "solver_type = 0\n", - "\n", - "props = np.array([rho, c_p, E, nu, alpha])\n", - "\n", - "path_data = 'data'\n", - "path_results = 'results'\n", - "pathfile = 'path.txt'\n", - "outputfile = 'results_ELISO.txt'\n", - "\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "outputfile_macro = dir + '/' + path_results + '/results_ELISO_global-0.txt'\n", - "\n", - "#Get the data\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(outputfile_macro, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time, T, Q, r = np.loadtxt(outputfile_macro, usecols=(4,5,6,7), unpack=True)\n", - "Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt(outputfile_macro, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(2, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11,s11, c='black', label='direction 1')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time t (s)', size = 15)\n", - "plt.ylabel(r'temperature $\\theta$\\,(K)',size = 15)\n", - "plt.plot(time,T, c='black', label='temperature')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 3)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "plt.plot(time,Wm, c='black', label=r'$W_m$')\n", - "plt.plot(time,Wm_r, c='green', label=r'$W_m^r$')\n", - "plt.plot(time,Wm_ir, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time,Wm_d, c='red', label=r'$W_m^d$')\n", - "plt.legend(loc=3)\n", - "\n", - "ax = fig.add_subplot(2, 2, 4)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_t$',size = 15)\n", - "plt.plot(time,Wt, c='black', label=r'$W_t$')\n", - "plt.plot(time,Wt_r, c='green', label=r'$W_t^r$')\n", - "plt.plot(time,Wt_ir, c='blue', label=r'$W_t^{ir}$')\n", - "plt.legend(loc=3)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Notebooks/Umats/Thermomechanical/Elasticity/ELITR.ipynb b/Notebooks/Umats/Thermomechanical/Elasticity/ELITR.ipynb deleted file mode 100644 index 19f58856b..000000000 --- a/Notebooks/Umats/Thermomechanical/Elasticity/ELITR.ipynb +++ /dev/null @@ -1,278 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Transverse isotropy linear elasticity" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import pylab\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import simcoon as sim\n", - "import math\n", - "import os\n", - "dir = os.path.dirname(os.path.realpath('__file__'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In thermoelastic transversely isotropic materials eight parameters are required: \n", - "\n", - "1. The axis of transverse isotropy (1,2 or 3),\n", - "2. The axial Young modulus $E_L$,\n", - "3. The transverse Young modulus $E_T$,\n", - "4. The axial Poisson ratio $\\nu_{TL}$,\n", - "5. The transverse Poisson ratio $\\nu_{TT}$,\n", - "6. The axial shear modulus $G_{LT}$,\n", - "7. The axial coefficient of thermal expansion $\\alpha_L$,\n", - "8. The transverse coefficient of thermal expansion $\\alpha_T$.\n", - "\n", - "When the axis of transverse isotropy is 1, the elastic stiffness tensor and the thermal expansion coefficients tensor are written in SMART+ formalism as\n", - "\n", - "$$\\boldsymbol{L}=\\left(\\begin{matrix} L_{1111} & L_{1122} & L_{1122} & 0 & 0 & 0 \\\\ L_{1122} & L_{2222} & L_{2233} & 0 & 0 & 0 \\\\ L_{1122} & L_{2233} & L_{2222} & 0 & 0 & 0 \\\\ 0 & 0 & 0 & L_{1212} & 0 & 0 \\\\ 0 & 0 & 0 & 0 & L_{1212} & 0 \\\\ 0 & 0 & 0 & 0 & 0 & L_{2323} \\end{matrix}\\right), \\quad \\boldsymbol{\\alpha}=\\left(\\begin{matrix} \\alpha_L & 0 & 0 \\\\ 0 & \\alpha_T & 0 \\\\ 0 & 0 & \\alpha_T \\end{matrix}\\right),$$\n", - "\n", - "where $$\\begin{array}{c}\\displaystyle{L_{1111}=\\frac{E_L}{\\omega}~(\\nu^2_{TT}-1), \\quad L_{1122}=-\\frac{E_L}{\\omega}~\\nu_{TL}~(1+\\nu_{TT}), \\quad L_{2222}=\\frac{E_L~\\nu^2_{TL}-E_T}{\\omega},} \\\\ \\displaystyle{L_{2233}=-\\frac{E_L~\\nu^2_{TL}+E_T~\\nu_{TT}}{\\omega}, \\quad L_{1212}=G_{LT}, \\quad L_{2323}=\\frac{E_T}{2(1+\\nu_{TT})},}\\\\ \\displaystyle{\\omega=\\frac{1}{E_T}(1+\\nu_{TT})~(2E_L~\\nu^2_{TL}+E_T~(\\nu_{TT}-1)).}\\end{array}$$\n", - "\n", - "Details on the elastic stiffness tensor of transversely isotropic media can be found in Christensen (1979). For axis of transverse isotropy being 2 or 3, the above tensors are properly rotated. The tangent stiffness tensor in this case is $\\boldsymbol{L}^t=\\boldsymbol{L}$. Moreover, the increment of the elastic strain is given by \n", - "\n", - "$$\\Delta\\varepsilon^{\\textrm{el}}_{ij}=\\Delta\\varepsilon^{\\textrm{tot}}_{ij}-\\alpha_{ij}\\Delta T.$$\n", - "\n", - "In the 1D case only one component of stress is computed, through the relation \n", - "\n", - "$$\\sigma^{\\textrm{fin}}_{11}=\\sigma^{\\textrm{init}}_{11}+L_{1111}\\Delta\\varepsilon^{\\textrm{el}}_{11}.$$\n", - "\n", - "In the plane stress case only three components of stress are computed, through the relations \n", - "\n", - "$$\\left(\\begin{matrix} \\sigma^{\\textrm{fin}}_{11} \\\\ \\sigma^{\\textrm{fin}}_{22} \\\\ \\sigma^{\\textrm{fin}}_{12} \\end{matrix}\\right) =\\left(\\begin{matrix} \\sigma^{\\textrm{init}}_{11} \\\\ \\sigma^{\\textrm{init}}_{22} \\\\ \\sigma^{\\textrm{init}}_{12} \\end{matrix}\\right)+\\boldsymbol{K} \\left(\\begin{matrix} \\Delta\\varepsilon^{\\textrm{el}}_{11} \\\\ \\Delta\\varepsilon^{\\textrm{el}}_{22} \\\\ 2\\Delta\\varepsilon^{\\textrm{el}}_{12} \\end{matrix}\\right),$$\n", - "\n", - "with $$\\boldsymbol{K}=\\left(\\begin{matrix} \\displaystyle{L_{1111}-\\frac{L_{1133}L_{3311}}{L_{3333}}} & \\displaystyle{L_{1122}-\\frac{L_{1133}L_{3322}}{L_{3333}}} & \\displaystyle{L_{1112}-\\frac{L_{1133}L_{3312}}{L_{3333}}} \\\\ \\displaystyle{L_{2211}-\\frac{L_{2233}L_{3311}}{L_{3333}}} & \\displaystyle{L_{2222}-\\frac{L_{2233}L_{3322}}{L_{3333}}} & \\displaystyle{L_{2212}-\\frac{L_{2233}L_{3312}}{L_{3333}}} \\\\ \\displaystyle{L_{1211}-\\frac{L_{1233}L_{3311}}{L_{3333}}} & \\displaystyle{L_{1222}-\\frac{L_{1233}L_{3322}}{L_{3333}}} & \\displaystyle{L_{1212}-\\frac{L_{1233}L_{3312}}{L_{3333}}} \\end{matrix}\\right).$$\n", - "\n", - "In the generalized plane strain/3D analysis case the stress tensor is computed through the relation\n", - "\n", - "$$\\sigma^{\\textrm{fin}}_{ij}=\\sigma^{\\textrm{init}}_{ij}+L_{ijkl}~\\Delta\\varepsilon^{\\textrm{el}}_{kl}.$$" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "umat_name = 'ELIST' #This is the 5 character code for the elastic transversely isotropic subroutine\n", - "nstatev = 1 #The number of scalar variables required, only the initial temperature is stored here\n", - "\n", - "rho = 4.4\n", - "c_p = 0.656\n", - "axis = 1\n", - "E_L = 4500\n", - "E_T = 2300\n", - "nu_TL = 0.05\n", - "nu_TT = 0.3\n", - "G_LT = 2700\n", - "alpha_L = 1.E-5\n", - "alpha_T = 2.5E-5\n", - "\n", - "psi_rve = 0.\n", - "theta_rve = 0.\n", - "phi_rve = 0.\n", - "solver_type = 0\n", - "\n", - "props = np.array([rho, c_p, axis, E_L, E_T, nu_TL, nu_TT, G_LT, alpha_L, alpha_T])\n", - "path_data = 'data'\n", - "path_results = 'results'\n", - "\n", - "pathfile = '/path_1.txt'\n", - "outputfile_1 = 'results_ELIST_1.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile_1)\n", - "outputfile_1 = dir + '/' + path_results + '/results_ELIST_1_global-0.txt'\n", - "\n", - "pathfile = '/path_2.txt'\n", - "outputfile_2 = 'results_ELIST_2.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile_2)\n", - "outputfile_2 = dir + '/' + path_results + '/results_ELIST_2_global-0.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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79u2jX79+x73W0aNH6dWrFzfccANffvklU6ZMYcCAAXzyySelcbzyyiuMGTOGPXv20Lp1\nax588MGo/TuVp/pCiU9jnBw0zsmhuuPs7owdOxYzIysri3POOYctW7bg7nz3u9+NTJAikpBefPFF\nLrjgAt544w327dvH7bffzo033shDDz3E7t27efTRR8nIyGDXrl2lz3nllVeYOXMmW7duZf369Vx3\n3XXccccd7N69m7Zt2zJmzJjjXuP111/nL3/5C3/5y1+YO3cu06dPB2Du3LlkZ2fz+uuv8+WXX/Jv\n//ZvX/uUxNy5c3nvvff46KOPAOjQoQN//etf2b17N7fffjv9+vWjqKiIHj168MADD9C/f3/279/P\nypUrK33P5X92+OMf/8i6dev4wx/+wNatW7nppptO+P6DpHmBRJOuL4kmXV9SIi8vj86dOzNq1Cgm\nTJhArVq1gg7pOEogV5OZReTrVC1btoyjR4/y85//nFq1apGRkcE111xzwufcfffdnHvuudStW5f3\n3nuPnTt38uCDD1KrVi1atWrFT3/6U2bNmgXAc889xyOPPMK3v/1tAC677DIaN25cei53r/A1CgsL\nOXDgACNGjKB27dp07dqVm266id/97nelx/Tu3Zurr76alJQUBgwYwKpVq075/YuIiFTm6NGjZGVl\nkZKSwujRo+nYsSP79u1j69atnHvuuUGHJyKnIKi5domSOe/LL7/MjTfeSI8ePQC4/vrr+d73vseb\nb75ZemxWVhatWrWiYcOG9OzZk9atW9O1a1dSUlLo16/f15K3I0eO5KyzzuL8889n+PDhpfPladOm\ncf/993PxxReTkpLCyJEjWbVqFZs3by597gMPPMBZZ51V+im922+/nUaNGpGSksIvfvELDh8+zN/+\n9rfTft9mxpgxY6hXrx5169at0vsXERGRUxdrzfIqUzvoAOJdZYnUaNu6dSvnnXfecftatmx5wuec\nf/75pX/fuHEjW7ZsoUmTJkDofRw7dozvf//7AGzevJmLLrrolOPatm3bceUsSuLasmVL6XaLFi1K\n/16/fn2++uqrU36d06WPKic+jXFy0Dgnh1Md5wMHDnDjjTeWflrmtttuY8aMGdSpUycK0YlITQhq\nrl3exo0befXVV0tLs7k7R48eLS3vANC8efPSv9erV+9r2+XnvGXn5i1btmTr1q2lr3X33Xdz7733\nlr6WmbFly5bSeXbZ5wI8+uijTJ8+nW3btgGwf/9+du7cWa33XP5nh4ref7du3ar1GpGieYFEk64v\niSZdX8ntyJEjDBs2jIKCAgoLC2Om3nFFlECOUyUfwy1r06ZNpSuGK1J29cW3vvUtLrrookpXJlxw\nwQV8+umntGvX7pTiOvfcc49bHVESV5s2bU7pPCIiIlX197//nbS0NDZu3AiEVvWNGzeuRpu+ikji\nKT93HjhwINOmTYvY+Tdv3swll1wChBK0JZ+Q+Na3vsWoUaNO2NyzbGzvvvsukyZNYvHixaVz9yZN\nmpQm3yv6v7BBgwYcPHiwdHv79u0nfI1ovH8REZFktmvXLvr27UvDhg0pKCiIqXrHFVEJizjVsWNH\nateuzRNPPMHRo0fJyclhxYoVVX5+hw4daNiwIRMnTuTQoUMUFxfz4Ycf8uc//xmAO+64g1/96les\nX78eCNU83r17NxBaQfzZZ59VeN60tDTq16/PxIkTOXr0KPn5+bzxxhun3d3+yJEjHDp0CHenqKiI\nw4cPV2sliuoLJT6NcXLQOCeHk43zxx9/jJnRokULNm7cyNNPP427M378eCWPRaTays55f/zjHzN/\n/nwWLlzIsWPHOHToEEuWLCldNXw6Jk2axJ49e9i8eTNTpkzh1ltvBeCuu+5i3LhxpfWN9+7dy5w5\ncyo9z/79+6lTpw5NmzalqKiIsWPHsn///tLHmzdvzoYNG46bQ7dv355Zs2Zx9OhR/vznP3/t/OXn\n29F4/5GkeYFEk64viSZdX8kplpvlVUYJ5DhVp04dcnJyeP7552natCmzZ88mIyOj0uPL/yCdkpLC\nG2+8wapVq7jwwgs5++yzufPOO9m3bx8A99xzDz/60Y/o3r07Z511Fj/96U/55z//CcDo0aMZOHAg\nTZo0+dpks06dOsyfP58333yTZs2aMWzYMF566SW+853vVBjHyXTv3p369etTWFjIkCFDqF+/Pn/6\n059O6RwiIpJYCgoKMLPST7fMnz8fd+euu+4KODIRSSQjR47k4YcfpkmTJrz66qvMnTuXcePG8c1v\nfpOWLVvy6KOPcuzYMeDU57gAt9xyC1dffTVXXXUVvXr1YvDgwQD8n//zfxg5ciS33norjRo14vLL\nLycvL6/0eeVfq0ePHvTo0YOLL76YCy+8kPr16x9XUq5fv364O02bNuV73/seAA8//DDr16+nSZMm\njBkzhgEDBhx3zvKvcf7555/w/YuIiEjVxHqzvMpYrNQVC5qZeUX/FmYWM7XX5OQ0XiIiiSsnJ+e4\nX5auWLHipA1kJfaF791aMp7ANM/+upSUFNavX39aPUfiRTKPr4iISHnuzpQpU8jOzmb27Nl06tQp\n6q8ZyXm2aiCLiIhITJs8eTLDhw8HQo2o1qxZk9BJFxERERERSRzx1CyvMiphIUlF9YUSn8Y4OWic\nE5+7069fP8yM4cOHc8kll7Bz504OHjyo5LGIxD3VaY8szQskmnR9STTp+kp8u3btonv37mzbto2C\ngoK4TB6DEsgiIiISQw4fPkyfPn1ISUlhzpw59OzZk4MHD/LRRx/RtGnToMMTEYmI4uJi/TJMREQk\nwcVjs7zKqAZymGqzJQaNl4hIfNqzZw9du3Zl1apVAAwdOpQnn3ySlBT9rjvRqQZy4tM8OzlpfEVE\nJJnl5eUxcOBAJk6cSGZmZiAxqAayiIiIJIRNmzZx+eWXs3fvXgDGjx/PyJEjA45KRERERETk1JVt\nlpeTk1MjzfJqgpb1SFJRfaHEpzFODhrn+Ldq1SrMjJYtW7J3715efvll3P245LHGWUREqkL3C4km\nXV8STbq+EsuRI0e46667ePbZZyksLEyY5DFoBfJJtWzZUk0u4kjLli2DDkFERE5g0aJFdO/evXR7\n8eLFdOnSJbiARCQwmmcnNs3LRUQkmezatYu+ffvSsGFDCgoK4rrecUVUAzmsstpsIiIiUn0vvPAC\nWVlZpdtr1qzhu9/9boARSaxQDeTEF0vz7I8++ohevXqRkZHB+PHjqVWrVtAhiYiISJxbu3Zt6fxi\n3LhxMTO/iOQ8WyUsREREJCrcnbFjx2JmZGVlcc4557BlyxbcXcljEQlEu3btWL58OStWrKB3797s\n378/6JBEREQkjuXl5dG5c2dGjRrFhAkTYiZ5HGlKIEtSUX2hxKcxTg4a59hWXFzM4MGDSUlJYfTo\n0XTs2JF9+/axdetWzj333CqfR+MsItHQrFkzFi5cSIsWLUhPT2fDhg1BhyTVpPuFRJOuL4kmXV/x\ny92ZPHkyWVlZ5OTkkJmZGXRIUaUEsoiIiETEgQMH6NatG7Vr1+b555/ntttuo6ioKCFrgIlIfDvj\njDOYNm0agwcPpmPHjixdujTokERERCROJHKzvMqoBnJYLNVmExERiSc7duzg2muv5fPPPwdg5MiR\njBs3Ts2xpEpUAznxxfo8e8GCBQwaNIhJkyYxaNCgoMMRERGRGFa2Wd7MmTNjeqFMJOfZSiCHxfrE\nVkREJNZ8/PHHtG3blpL759SpUxk6dGjAUUm8UQI58cXDPFvN9URERORkYrVZXmXURE/kNKm+UOLT\nGCcHjXOwCgoKMDPatGmDuzNv3jzcPeLJY42ziNQUNdeLb7pfSDTp+pJo0vUVP5KlWV5llEAWERGR\nKsnJycHMSE9PB2D58uW4O7169Qo4MhGR6lNzPRERESkv2ZrlVUYlLMLi4aN1IiIiQZg8eTLDhw8H\noG7dunz44Ye0bt064KgkUaiEReKLt3l2yQ+KEyZMYM6cOaW/NBMREZHkcuTIEYYNG0ZBQQHz58+n\nVatWQYd0SlTCQkRERKLK3bnnnnswM4YPH07btm358ssvOXTokJLHIpLQSv7fmz59Or1792bGjBlB\nhyQiIiI1bNeuXXTv3p1t27ZRUFAQd8njSFMCWZKK6gslPo1xctA4R8/hw4fp06cPKSkpPP744/Ts\n2ZODBw+ydu1amjVrVqOxaJxFJEg9e/YkPz+fsWPHct9991FcXBx0SFIJ3S8kmnR9STTp+opNa9eu\nJS0tjQ4dOpCbm0vDhg2DDilwSiCLiIgIe/bs4aqrruIb3/gGubm5DB06lOLiYt58803q1asXdHgi\nIoFQcz0REZHkkuzN8iqjGshh8VabTUREJBI2b97MFVdcwe7duwEYP348I0eODDgqSSaqgZz4EmGe\nXVRUxLBhw1i2bBnz5s1L+o+xioiIJBp3Z8qUKWRnZzN79mw6deoUdEjVFsl5thLIYYkwsRUREamq\n1atX0759+9Ltl19+mQEDBgQYkSQrJZATX6LMs9VcT0REJDHFe7O8yiR9Ez0zu9XM3jez/Wb2hZnN\nMLNzKjjuATPbZGYHzWyJmV0RRLwSO1RfKPFpjJODxvn0LVq0CDMrTR6/8847uHtMJo81ziKnz8wu\nMbO3zeyAmW0xszFmdsIfIMysjplNMrM/hufPFRb9DR/3kJl9Ej7uEzP7bzM7IzrvJjaouV7s0v1C\noknXl0STrq/gqVle1cRdAtnMbgZ+C7wL3AzcB3wfeKPccfcDDwLjgZuAr4C3zOzsGg1YREQkBrzw\nwguYGd27dwfggw8+wN3p2rVrwJGJSKSZWSPgLeAoofnyGODe8J8nUh8YDBwAlp7guAmE5uBPAj2B\nqeHtCdUKPE6ouZ6IiEhiULO8qou7EhZm9jvg2+5+TZl9vYDXgXbu/jczqwv8HZjk7o+Ej6kPbACe\ncfeHKjhvQny0TkREpIS78/DDDzN69GgAmjdvzvvvv895550XcGQi/6ISFpEXXkjxX8AF7n4gvO+X\nwGighbt/VYVz/Ccwxd2/1jnGzLYBL7n7fWX2PQbc7u4VfSowIefZO3fupG/fvqSmpjJz5kz90Cki\nIhJH8vLyGDhwIBMnTiQzMzPocKIi2UtY1AH2lttXsl3yj5IONARmlxzg7geB+YRWSYiIiCSs4uJi\nBg8eTEpKCqNHjyYtLY29e/eyfft2JY9FksMNwB9KksdhswitMO4cgfPXAfaV27eXf83Fk0KzZs1Y\nuHAhLVq0ID09nQ0bNgQdkoiIiJxESU+DrKwscnJyEjZ5HGnxmECeDvybmf3EzBqa2cXAw8Db7r4u\nfEwboBj4pNxz1wJtay5UiTWqL5T4NMbJQeNcsQMHDtCtWzdq167N888/z6233kpRURHLli0jNTU1\n6PBOmcZZ5LS1BdaV3eHum4GDRGYu/CwwxMyuM7MGZvZvwF3AExE4d1w544wzmDZtGoMHD6Zjx44s\nXXqiyh8SLbpfSDTp+pJo0vVVs44cOcJdd93Fs88+S2FhIZ06dQo6pLgRdwlkd38TyAL+l9BKh3WE\n3kffMoc1Br6q4LNyu4H6Zla7JmIVERGpCTt27OCiiy7izDPPZPHixYwcOZJjx47xu9/9jjp16gQd\nnojUvMbAngr27w4/Vi3uPhLIIdSTZD+QD7xWUjou2ai5noiISOxTs7zqiccayF2BuYSaduQBzYH/\nJlTz+Hp3dzN7APgvd29S7rl3EEo813X3o+UeS8jabCIikrg+/vhj2rZtS8n9a+rUqQwdOjTgqERO\njWogR56ZFRGaC08pt38zMMPdR1XhHCeqgXwfoaZ5vwI+AK4Afh0+fnQFxyfNPPujjz6iV69eZGRk\nMH78eGrV+to/n4iIiNSwtWvXlt6fx40blzT350jOs+NxJe6jwOvu/kDJDjNbTWgl8i2EmuntBs60\nr89WGwMHyyePS2RmZpb+BqJRo0a0b9+eLl26AP/6WIG2ta1tbWtb20Fvr1mzhp/97GeUeOSRR3jg\ngQdiJj5ta/tE26tWrWLPntDiWNWMjZrdwFkV7G8cfuy0mVlTQuXjhrr79PDud83sCPCEmT3h7jvL\nPy9Z5tnt2rXjN7/5DaNHj2bdunXMnDmT999/P2bi07a2ta1tbWs72bYPHTrEwIEDGTx4MDfccENp\n8jhW4ovsdHQDAAAgAElEQVTkdjTn2fG4AvkA8JC7P1bZ/vAq5beAtu7+SZljngWucPdrKjhv0qyM\nSGb5+fml31ySmDTGySFZxzknJ4eMjIzS7eXLl9OhQ4cAI4quZB3nZKMVyJFnZkuAL9x9QJl95wOb\ngF7u/vsqnKPCFchmdg2wDOjg7u+fbH/4saSbZxcVFTFs2DCWLVvGvHnz9DHZKNP9QqJJ15dEk66v\n6HF3pkyZQnZ2NrNnz07KeseRnGenROIkNWwjcFXZHWZ2CVAP2BDeVUCoHlu/MsfUB3oBb9ZIlCIi\nIhEyefJkzIyMjAzq1q3L+vXrcfeETh6LSLUsAHqYWYMy+24l1ERvSTXPvREwys3Hge+F/9xQzfMn\nBDXXExERCU5RURFDhgxRs7wIiscVyD8HfgP8D6HJcQtC9ddqA5e5+z/Dx40ERhGqz7YOuBe4Bviu\nu39ZwXmTbmWEiIjELnfn3nvv5fHHHwegTZs2vPvuuzRr1izgyEQiSyuQI8/MGgEfhr8mAK2Bx4Df\nlK1RbGbrgcXufmeZfTcADYCehBpX/yj80Hvuvil8TA7QhVAfkr8CVwKjgQXuflsF8ST1PHvBggUM\nGjSISZMmMWjQoKDDERERSWi7du0iIyOD1NRUZs6cScOGDYMOKTCRnGfHXQIZwMyGAEMJTYb3AH8C\nHnD3DeWOuz98XFPgPeDn7v7XSs6Z1BNbERGJDYcPH+a2224jNzcXgB49epCbm0u9evUCjkwkOpRA\njg4za0uo6XRHQvPl/weMKTvhNbPPCCWQ7yiz73PgggpOmeXuL4aPORN4COgNnAtsAV4Dfu3uByqI\nJenn2WquJyIiEn3J2iyvMkmfQI4GTWyTg+oLJT6NcXJIxHHes2cP3bp1Y+XKlQAMGTKEp556Kqkn\nPYk4zvJ1SiAnPs2zQ3bu3Enfvn21IioKdL+QaNL1JdGk6yty8vLyGDhwIBMnTiQzMzPocGJCstdA\nFhERSRibN2+mSZMmNG7cmJUrVzJ+/HjcnWeeeSapk8ciIommWbNmLFy4kBYtWpCenh7x7ugiIiLJ\nyN2ZPHkyWVlZ5OTkKHkcJVqBHKaVESIiUpNWr15N+/btS7dffvllBgwYEGBEIsHQCuTEp3n28Up+\n0J0wYQJz5swhPT096JBERETiUlFREcOGDaOwsJD58+fTqlWroEOKKVqBLCIiEqcWLVqEmZUmj995\n5x3cXcljEZEkYWYMHz6c6dOn07t3b2bMmBF0SCIiInFn165ddO/ene3bt1NQUKDkcZQpgSxJJT8/\nP+gQJMo0xskhHsd5xowZmBndu3cH4IMPPsDd6dq1a8CRxa54HGcRkarq2bMn+fn5jB07lvvuu4/i\n4uKgQ4pbul9INOn6kmjS9XV61q5dS1paGmlpaeTm5qqvQA1QAllERCRK3J2xY8diZmRmZtK8eXO+\n+OIL3J1LL7006PBERCRg7dq1Y/ny5axYsYLevXuzf//+oEMSERGJaXl5eXTu3JlRo0YxYcIE9Y2p\nIaqBHKbabCIiEinFxcXceeedPP/88wCkpaWxcOFCUlNTA45MJPaoBnLi0zz75EpqOC5btox58+bp\nY7giIiLluDtTpkwhOzub2bNn06lTp6BDinmqgSwiIhKDDhw4QLdu3ahduzbPP/88/fv3p6ioiGXL\nlil5LCIilTrjjDOYNm0agwcPpmPHjixdujTokERERGJGUVERQ4YM4dlnn6WwsFDJ4wAogSxJRfWF\nEp/GODnE2jjv2LGDiy66iDPPPJPFixczYsQIjh07xqxZs6hTp07Q4cWtWBtnEZFoUnO906f7hUST\nri+JJl1fJ6dmebFBCWQREZHT9PHHH5OSkkLz5s35/PPPmTp1Ku5OdnY2ZvpEvoiInDo11xMREQlR\ns7zYoRrIYarNJiIiVVVQUEB6enrp9ty5c7n55psDjEgkfqkGcuLTPPv07Ny5k759+5KamsrMmTP1\nQ7OIiCSVvLw8Bg4cyMSJE8nMzAw6nLikGsgiIiIByMnJwcxKk8fLly/H3ZU8FhGRiGvWrBkLFy6k\nRYsWpKens2HDhqBDEhERiTp3Z/LkyWRlZZGTk6PkcYxQAlmSiuoLJT6NcXKo6XGeMmUKZkZGRgZ1\n69Zl/fr1uDsdOnSo0TiSjb6fRSTZqble1eh+IdGk60uiSdfX8dQsL3YpgSwiIlIBd+eee+7BzLj7\n7rtp06YNX375JYcOHaJ169ZBhyciIklCzfVERCQZqFlebFMN5DDVZhMREQj91vvWW28lNzcXgB49\nepCbm0u9evUCjkwkMakGcuLTPDtyPvroI3r16kVGRgbjx4+nVq1aQYckIiJSbWvXri29v40bN073\ntwiJ5DxbCeQwTWxFRJLbnj176NatGytXrgRgyJAhPPXUU5q8iESZEsiJT/PsyFJzPRERSSRqlhc9\naqIncppUXyjxaYyTQyTHefPmzTRp0oTGjRuzcuVKxo8fj7vzzDPPKHkcMH0/i4h8nZrrfZ3uFxJN\nur4kmpL5+lKzvPiiBLKIiCSl1atXY2ZccMEF7N69m5dffhl3Z+TIkUGHJiIickJqriciIvFMzfLi\nj0pYhOmjdSIiyWHRokV07969dPudd96ha9euAUYkktxUwiLxaZ4dXQsWLGDQoEFMmjSJQYMGBR2O\niIjICe3atYuMjAyVYqoBKmEhIiJyimbMmIGZlSaPP/jgA9xdyWMREYlrPXv2JD8/n7Fjx3LfffdR\nXFwcdEgiIiIVWrt2LWlpaaSlpZGbm6vkcRxRAlmSSjLXF0oWGuPkUNVxdnfGjh2LmZGZmUnz5s35\n4osvcHcuvfTS6AYp1abvZxGRqmnXrh3Lly9nxYoV9O7dm/379wcdUo3S/UKiSdeXRFMyXV95eXl0\n7tyZUaNGMWHCBPWbiTNKIIuISMIpLi5m8ODBpKSkMHr0aNLS0ti7dy/bt2/nvPPOCzo8ERGRiFNz\nPRERiUVqlpcYVAM5TLXZRETi38GDB7nppptYvHgxAP379+ell16iTp06AUcmIpVRDeTEp3l2zSr5\nQX3ChAnMmTOH9PT0oEMSEZEkVVRUxLBhwygsLGT+/Pm0atUq6JCSimogi4iIlLFjxw4uuugiGjRo\nwOLFixkxYgTHjh1j1qxZSh6LiEhSMTOGDx/O9OnT6d27NzNmzAg6JBERSUK7du2ie/fubN++nYKC\nAiWP45wSyJJUkqm+ULLSGCeHknH++OOPSUlJoXnz5nz++edMnToVdyc7OxszLWiMd/p+FhE5fcnU\nXE/3C4kmXV8STYl6falZXuJRAllEROLOmjVrMDPatGmDuzN37lzcnaFDhwYdmoiISMxI9uZ6IiJS\n89QsLzGpBnKYarOJiMS+nJwcMjIySreXL19Ohw4dAoxIRKpLNZATn+bZwSupQbls2TLmzZunjxGL\niEjEuTtTpkwhOzub2bNn06lTp6BDSnqqgSwiIkllypQpmBkZGRnUrVuX9evX4+5KHouIiFTBGWec\nwbRp0xg8eDAdO3Zk6dKlQYckIiIJpKioiCFDhvDss89SWFio5HECUgJZkkqi1heSf9EYJw535557\n7sHMuPvuu2nTpg1ffvklhw4dYvPmzUGHJzVA388iIpGTyM31dL+QaNL1JdGUCNeXmuUlByWQRUQk\nphQVFdGnTx9SUlJ4/PHH6dGjBwcPHmTdunU0a9Ys6PBERETiWjI11xMRkehSs7zkoRrIYarNJiIS\nrD179tCtWzdWrlwJwJAhQ3jqqafUdEEkwakGcuLTPDs27dy5k759+5KamsrMmTP1Q7+IiJySvLw8\nBg4cyMSJE8nMzAw6HKlAJOfZSiCHaWIrIhKMzZs3c8UVV7B7924Axo8fz8iRIwOOSkRqihLIYGaX\nAR2AFsA3gH8AHwMF7r47yNgiQfPs2KXmeiIicqrULC9+qImeyGlKhPpCcmIa4/ixevVqzIwLLriA\n3bt38/LLL+PuVUoea5yTg8ZZEpmZXWRmk8xsK7AKeAYYDmQBDwPzgS/N7G0zu83MNG+XiEuU5nq6\nX0g06fqSaIq360vN8pKXJqIiIlKjFi1ahJnRvn17AN555x3cnQEDBgQcmYhIzTCzZ4EPgfbAWOBK\n4Bvu/k13P9/dzwTOBnoBa4CJwFoz009pEnGJ3FxPREQiR83ykptKWITpo3UiItE1Y8aM42pjffDB\nB1x66aXBBSQiMSEZS1iY2RPAo+6+sYrHpwD9ANz9lWjGFg2aZ8ePjz76iF69epGRkcH48ePVh0BE\nRIBQs7yS+8O4ceN0f4gTKmEhIiJxwd0ZO3YsZkZmZibNmzfniy++wN2VPBaRZDavKsljM6tjZr9z\n92Pu/ko8Jo8lvrRr147ly5ezYsUKevfuzf79+4MOSUREApaXl0fnzp0ZNWoUEyZMUPI4SSmBLEkl\n3uoLyanTGMeG4uJiBg8eTEpKCqNHjyYtLY29e/eyfft2zjvvvGqfX+OcHDTOksDmmlnPEx1gZg2A\nN4E+NROSSEizZs1YuHAhLVq0ID09nQ0bNgQd0knpfiHRpOtLoimWry93Z/LkyWRlZZGTk3Pcp0kl\n+dQ+lYMTvUO0iIhUz8GDB7nppptYvHgxAP379+ell16iTp06AUcmIhJTcoFcM7vV3V8v/6CZNQMW\nAO1QAlkCUNJcb/LkyXTs2JE5c+aQnp4edFgiIlJDioqKGDZsGIWFhRQWFqresZy8BrKZXQQMBQYA\nzYFjwB7gMNAIqB/etwR4FnjF3Y9FMeaoUG02EZHTt2PHDq699lo+//xzAEaMGMH48eMxS6qypiJy\nGpK0BrIRmjf/GPiJu79a5rFWwB+AZsBN7l4YRIyRpHl2fFuwYAGDBg1i0qRJDBo0KOhwREQkynbt\n2kVGRgapqanMnDmThg0bBh2SnKYaq4F8ih2iP6CGOkSbWS0zG2lmH5vZITPbbGaPVXDcA2a2ycwO\nmtkSM7simnGJiCSbTz75hJSUFJo3b87nn3/O1KlTcXeys7OVPBYRqYSH3EEoiTzTzAYCmNnlwFJC\nCzT+rTrJYzO7xMzeNrMDZrbFzMbYSf5jDtdcnmRmfwzPn4tPcGwTM5tmZtvCx35kZj8+3XgldvXs\n2ZP8/HzGjh3LfffdR3FxpZeFiIjEubVr15KWlkZaWhq5ublKHkupk9VA/ifQ1t1/4O7PuPtf3f24\nGYO773T3Be4+HGgJPARUv8Dlic0AhhFKWP8AGBGOtZSZ3Q88CIwHbgK+At4ys7OjHJvEsFiuLySR\noTGuGYWFhZgZF198Me7O3LlzcXeGDh1aI6+vcU4OGmdJdO7+n8ATwHQzexT4I7AXuM7dPzrd85pZ\nI+At4ChwMzAGuDf854nUBwYDBwglsis7f0PgT8DlhObkPcPv44zTjVliW6w319P9QqJJ15dEUyxd\nX2qWJydywhrI7v6zUzlZuHRFVLtDm9kNQD/gcnf/WyXH1CWUVB7n7k+H9y0DNhCa5D4UzRhFRBJV\nbm4uffr8qxzn8uXL6dChQ4ARiYjEN3e/x8wOAvcDy4EbI9BbZCihfiV93P0A8LaZnQWMNrOJ7v5V\nJbHsBZoCmNl/At0qOf+DQB2gs7sXhfctqWbMEuNKmusNGzaM9PR05s2bp5qYIiIJwN2ZMmUK2dnZ\n5OTk0KlTVIsKSJw6aQ3kWGNmrwCp7l5p52oz60po1cUl7v5xmf3PEUo8X1PBc1SbTUSkElOmTOHu\nu+8GoG7dunz44Ye0bt064KhEJBEkaQ3kL4HyE89mhFYfHyl/vLuf0ifozGwJsMXdby+z71vARqCX\nu/++Cuf4T2CKu39t+ZGZbQd+4+4TqxiP5tkJxN2ZPHkyEyZMUHM9EZE4V7ZZ3vz58/WLwQQTyXn2\nCVcgx6g0YK6ZPQEMJPQe8oBh7r4tfExboBj4pNxz1wI/qqlARUTimbtz77338vjjjwPQpk0b3n33\nXZo1axZwZCIice8pvp5AjqS2wNtld7j75vBK57bASRPIlQk3+Tsb2Gdmvwf+nVDi+yVghLsfPd1z\nS3wwM4YPH06bNm3o3bu3muuJiMSpss3yCgoKVO9YTuhkNZCPY2b9zeytcGO6HeW/ohVkOS2ALOAK\nQsngTOBqIKfMMY2BrypY6rAbqG9m8Zg4lwiIpfpCEh0a4+orKiqiT58+pKSk8Pjjj9OjRw8OHjzI\nunXrYiZ5rHFODhpnSVTu/t/uPqaqX6fxEo2BPRXs3x1+rDpahP+cAHwB9AAeIVQ249fVPLfEkVhq\nrqf7hUSTri+JpqCuLzXLk1NV5QSymd1OqHndeuB8YB7wRvgc+4AnoxFgRaGE/7zZ3f/g7rOBnwBp\n4dIVIiJyGvbs2cNVV11F3bp1yc3NZciQIRw9epS8vDzq1asXdHgiIhIbSubia9x9iLvnu/tkQo2r\nf25m3wgwNqlhsd5cT0REvk7N8uR0nMpK3F8CDwPZwP8Fprr7X8JdmBcBB6MQX0V2A5+6e9lVFe8C\nRUA7YHH4mDPt6wXXGgMHK/toXWZmZmm9l0aNGtG+fXu6dOkC/Ou3QtrWtrZje7tLly4xFU88bL/6\n6qvccccdfPVVqKfSnXfeye233x4z8VW2XSJW4tF25Lf1/ZyY26tWrWLPntA0bsOGDSQjM/sJ8Ft3\nr/KSTTP7NnCOu/+pCofvBs6qYH/j8GPVUfL8/HL73wH+G2gNfFj+SZpnJ+72mjVrePDBB5k9ezbp\n6ek88MADtGjRosbjKRH0v4e2E3O7RKzEo+3E2i4R7ddbvHgxr732Gq+99hq5ubkcOXKE/Pz8wN+/\ntiO3Hc15dpWb6JnZV8BN7p5vZkeAH7h7fvix3sDj7t4qotFVHMdioK67X1dmnwGHgOHu/nSZJnpt\n3f2TMsc9C1yhJnoiIrB69Wrat29fuv3yyy8zYMCAACMSkWSUpE30VhJK5r4EzHH31ZUc1xS4AbgV\n6AoMdvdXq3D+JcAX7j6gzL7zgU1Us4memdUB9hNqovdAmf2dgCXApe6+ttxzNM9OAmquJyISu0qa\n5S1btox58+apWV6SiOQ8O+UUjt0H1A3/fQtwSdmYgKaRCKgK3gAuM7MmZfZ1JrSaumTyXUBoYtuv\nNECz+kAv4M0ailNiUPnf8Eni0Rif3KJFizCz0uTxO++8g7vHVfJY45wcNM6SqNz9SmAEoaTwSjPb\nZ2bLzez3ZpZjZu+Y2efADmAy8CnQpirJ47AFQA8za1Bm362EPjG4pJqxHyH06cPypeP+PXz+9dU5\nv8SvkuZ606dPp3fv3syYMaPGXlv3C4kmXV8STTVxfe3atYvu3buzfft2li5dquSxnJZTKWHxHnA5\n8AdC9Y8fMrOjhEpHPAQsi3x4Ffpf4GfAG2Y2DkglVFZjkbsXALj7YTPLBkaZ2R5gHXAvoUR3TdVq\nFhGJKS+++OJxXdI/+OADLr300gAjEhFJXu7+CvCKmbUmlHy9ilCDugbA34E/AkuB/HDS9lQ8Q2i+\nnGtmEwiVlRgNPObuX5UcZGbrgcXufmeZfTeEY7gyvJ0Rfug9d98U/vtY4E9mNh34HaHm1iOAMacR\nqySYkuZ6vXr14sMPP2T8+PGqrykiEpC1a9fSq1cvMjIyGDdunP4/ltN2KiUsrgVauvsrZtaIUEO9\nGwmtYn4PuM3dP4tapMfHchEwhdDK4yLgdeAed99b7rj7CXWEbhqO8efu/tdKzqmP1olIwnF3fv3r\nX/PQQw8B0Lx5c95//33OO++8gCMTEQlJxhIWNcHM2hJaONER2AP8P0IJXi9zzGeEEsh3lNn3OXBB\nBafMcvcXyxz3A0KN875LaKX0NHcfV0ksmmcnoZ07d9K3b19SU1OZOXMmDRs2DDokEZGkkpeXx8CB\nA5k4cSKZmZlBhyMBiOQ8u8oJ5EoCqUuoHvG+SAQTJE1sRSSRFBcXc+edd/L8888DkJaWxsKFC0lN\nTQ04MhGR4ymBnPg0z05eqrkpIlLz3J0pU6aQnZ2tmvRJrkZrIJtZPTPLMLN7zex2M2te8pi7H06E\n5LEkD9WvSnzJPsYHDx6kW7du1K5dm+eff57+/ftTVFTEsmXLEip5nOzjnCw0zpLszOz74ZXEInHp\njDPOYNq0aQwePJiOHTuydOnSqLyO7hcSTbq+JJoifX0VFRUxZMgQnnvuOQoLC5U8log5YQI5XCri\nQ2A2MAl4GfibmXWvgdhERKSKduzYwUUXXUSDBg1YvHgxI0aM4NixY8yaNYs6deoEHZ6IiJyefOBD\nM3vbzG4MOhiR0xFkcz0RkWSiZnkSTScsYWFmc4D2wCDgfeBCYCrQyt0vrJEIa4g+Wici8eiTTz6h\nTZs2lPz/9dRTT/Ef//EfAUclIlJ1KmFROTPrDNQHrgWudfceAYd0WjTPlhIfffRRaTMnNdcTEYkc\nNcuTitRYDWQz2wLc6+6zyuy7GFgLnO/u2yIRRCzQxFZE4klhYSHXXXdd6fbrr7/OLbfcEmBEIiKn\nRwnkxKd5tpSl5noiIpGlZnlSmZqsgXwO8Fm5fZ8CBrSIRAAiNUn1qxJfoo9xbm4uZlaaPF6+fDnu\nnnTJ40QfZwnROEsyM7Mzzay3mT1rZluDjkckUpo1a8bChQtp0aIF6enpbNiwodrn1P1CoknXl0RT\nda4vd2fy5MlkZWWRm5ur5LFE1Umb6AFaLiAiErAnnngCM6NPnz7UrVuX9evX4+506NAh6NBERCRC\nzOw7ZjbczBYBO4FngNrA3cFGJhJZNdVcT0QkUalZntS0k5WwOAbsAY6We6hZRfvd/exIB1hT9NE6\nEYk17s4vf/lLHnvsMQDatGnDu+++S7NmzQKOTEQkcpK5hIWZ1QE6AzeGvy4CVgG/D3+9lwgTVM2z\n5UQWLFjAoEGDmDRpEoMGDQo6HBGRmLdr1y4yMjJUCkhOqiZrII8+lZO5+5hqRxQQTWxFJFYUFRVx\n2223kZOTA0CPHj3Izc2lXr16AUcmIhJ5yZhANrM7CCWMf0Do036LCCWM33T37UHGFg2aZ8vJqLme\niEjVqFmenIoaSyAnE01sk0N+fj5dunQJOgyJonge4z179tCtWzdWrlwJwJAhQ3jqqac0KahAPI+z\nVJ3GOTkkaQL5b/xrlfEf3f1IwCFFlebZUhXVaa6n+4VEk64viaZTub7ULE9OVU020RMRkSjbvHkz\nTZo0oXHjxqxcuZLx48fj7jzzzDNKHouIJCB3bwM8CQwA3jOztWaWb2aPmVnXgMMTCUQ0muuJiCQC\nNcuTWHCyEhYPncrJ3H1stSMKiFZGiEhNW716Ne3bty/dfumll/jxj38cYEQiIjUvGVcgA5jZNOC3\nwAHgfOBKYCDQHNgC/Le7zwwuwsjRPFtORUmiZMKECcyZM0eNoUQkqRUVFTFs2DCWLVvGvHnzaNWq\nVdAhSRypyRrIx4B/EprYnuwFXU30RERObtGiRXTv3r10+6233uL6668PMCIRkeAkcQJ5pLtnl9s3\nHhgD/BD4KVAE9Hf3wwGEGDGaZ8vpUHM9EUl2apYn1VWTJSw+BeoA7wP/BVzk7t+s5Ctuk8eSPPLz\n84MOQaIslsf4xRdfxMxKk8d//etfcXclj09DLI+zRI7GWRLcn83sf83sO2X2ubsfcvccd/8hMBU4\npabWIomiZ8+e5OfnM3bsWO677z6Ki4srPVb3C4kmXV8STZVdX2vXriUtLY20tDRyc3OVPJbAnTCB\n7O7fAa4DPgQeBv5uZjlm1s/M6tVEgCIi8czd+fWvf42ZMWjQIM4++2y++OIL3J3LLrss6PBERCQg\n7v4W8Dzwhpm9a2a/BM4ud8xCYEcQ8YnEgnbt2rF8+XJWrFhB79692b9/f9AhiYhEXV5eHp07d2bU\nqFFMmDBBfXEkJpywhMXXDjb7PnArkAHUB+YB09z9j9EJr+boo3UiEknFxcUMGTKE5557DoC0tDQW\nLlxIampqwJGJiMSWZC1hUcLM6hCqffxT4BpgP7AK2Ap8A1jj7nG9ClnzbKku1QAVkWTg7kyZMoXs\n7GzVgJeIqLEayCcI4AzgEeAXwDx37xOJYIKkia2IRMLBgwe5+eabefvttwHo378/L730EnXq1Ak4\nMhGR2JTsCeSyzOwsQs30mgMHCSWPPw82qurTPFsiQc31RCSR6RdlEg01WQO5/Aunm9kTwEZgKDAH\nmByJQERqgupXJb6gxnjHjh20bt2aBg0a8PbbbzNixAiOHTvGrFmzlDyOAn0vJweNsyQbd9/r7vnu\n/oq7z0+E5LFIpJgZw4cPZ/r06fTu3ZsZM2aUPqb7hUSTri+Jpvz8fHbt2kX37t3Zvn07S5cuVfJY\nYtJJE8hmdpWZTTSzjcDbwLcIrTw+291vdfcl0Q5SRCRWffLJJ9SqVYvmzZvz2Wef8dRTT+HuZGdn\nY6YFdSIiIiKRdCrN9UREYt3GjRvVLE/iwglLWJjZ34ALgXeAWUCOu++rodhqlD5aJyKnorCwkOuu\nu650+/XXX+eWW24JMCIRkfikEhaJT/NsiYadO3fSt29fUlNTmTlzppIuIhJ38vLyGDhwIBMnTiQz\nMzPocCQB1VgNZDM7BhwCDgAnnfW5+9knOyZWaWIrIlWRm5tLnz7/Kvu+bNky0tLSAoxIRCS+KYGc\n+DTPlmhRzVARiUdqlic1pSZrII8BJgBPAk9V4Uskpql+VeKL1hg/8cQTmBl9+vShTp06fPLJJ7i7\nkscB0fdyctA4i4jIiZxxxhlMmzaN73//+3Ts2JGlS5cGHZIkIM1HJJKKiooYMmQIzz33HIWFhRw5\nciTokESqpPaJHnT3MTUViIhIrHF3fvnLX/LYY48B8J3vfIelS5fyzW9+M+DIREQk0ZhZO+BqQv1G\nprv7djP7NvB3d98fbHQiscvM6Nu3LzfeeCO9e/dm0qRJDBo0KOiwRES+ZteuXWRkZJCamsrSpUtp\n2LAhGzZsCDoskSo5WQmLnwC/dfcqdyYIT3TPcfc/RSC+GqOP1on8f/buPb6q+sr//2txCRXl4sT4\npbvYex4AACAASURBVA52bAUv9Gu1FRoj3460AUacMRoidr5aCKKU8Wd0mBGwXtBaBIwUxyitxUs6\nRJl2FJqUtoJcFKcTE7AoYoVWsVJFv4ikBSl0jIT1++OcpMeYQC7nnH322e/n43EesC9nnxU+m+xP\nVvZeS5o1NjZyxRVXsHz5cgDGjh1LTU0NxxxzTMCRiYhkn6iXsDCz44BKoAQ4ROwGjxHu/qKZPQG8\n5e4zgoyxuzTPlnTZunUrRUVFjB8/nvnz59OzZ8+gQxIRAWDbtm1cfPHFlJSUMG/ePH1/krRIZwmL\nfwXeMLM5Znb2EQLKNbMrzexnwGbg08kITkQknfbt28fw4cPp06cPy5cvZ9q0aRw6dIinn35ayWMR\nEUmVe4HzgdFAPyBxkv8UcGEQQYmE0bBhw9iwYQMbN26kuLiY/ft1876IBG/VqlVccMEF3HbbbZSX\nlyt5LKF0xASyu38RuAn4KvCSmX1gZhvM7Bdm9hMze8bM3gR2AxXAG8Dp7v5EyiMX6QLVr8p+XRnj\nnTt3csIJJzBw4EA2bdrE/PnzcXd+8IMf6OKeofR/ORo0zhIR44Gb3P1ZoPVTf78H/ib9IYmES+L1\nIjc3l9WrVzNo0CBGjhypx8Ol2zQfka5ydyoqKrjqqquorq5m8uTJn9hH55eExRFrIAO4+38C/2lm\npxK7M+JLwCDgWOA94L+AWmC9u6v6t4iExpYtWzj77L88XPHYY4/xjW98I8CIREQkgo4BGtrZ1o9P\nJpVF5Ciam+tVVFRQUFDAsmXLGDlyZNBhiUiENDY2UlZWRn19PXV1dZxyyilBhyTSLUesgRwlqs0m\nEh1r165lzJgxH1suLCwMMCIRkehSDWRbD7zr7leYWU/gI2B4vAZyFXCCu18UaJDdpHm2BGnlypWU\nlpaquZ6IpE1is7ylS5fSr1+/oEOSiEpnDWQRkaxRVVWFmbUkj7ds2YK7K3ksIiJBmg2MN7O1wDWA\nAxeZ2WPABOCOIIMTCbtx48axfv165syZw6xZs2hq0k39IpI627ZtIz8/n/z8fKqrq5U8lqyRlASy\nmf2tmZ2RjGOJpJLqC2W/1mPs7tx1112YGaWlpeTl5fH222/j7px11lnBBCndpv/L0aBxlihw918C\nhUAfYBGxJnp3Ap8DRrv7CwGGJxIKR7teqLmedIfmI9JRXWmWp/NLwiJZdyCvB141s3Vm9vdJOqaI\nSJc1NTVxzTXX0KNHD2bPns2IESPYu3cvu3fvZvDgwUGHJyIigpn1NrORwJvu/hWgPzAY6OfuI929\nNtgIRbKHmuuJSKp0pFmeSNglpQaymV0A9AXOA85z97/r9kHTTLXZRLLDwYMHKSoqYt26dQBMmDCB\nxx9/nJycnIAjExGRtkS5BrKZ9QD+DIxz92eCjidVNM+WTNKc6CkvL1dzPRHptsRmeStWrFCzPMko\nyZxnq4lenCa2IuH2/vvvU1BQwBtvvAHArFmzuPvuuzGLZE5CRCQ0opxABjCzXwPz3P0/go4lVTTP\nlkyk5noi0l1qlieZLqOa6JnZcWZWbGaPmNm7yQhKJFVUXyj7vP766/Tq1YsTTzyRN954gxtuuAF3\np7y8XMnjLKb/y9GgcZaIuBW43cxUmF+ki7pyvVBzPekozUekLclqlqfzS8KiSwlkMxtqZtPNbA2w\nB/gB0Av452QGJyLSnvr6esyM0047jaamJmpqanB3iouLgw5NRESkM24DcoHNZvaWmb1gZhsTX0EH\nKJKt1FxPRLqiK83yRMKuQyUszKw3cAHw9/HX54DNwC/irxfC/lyaHq0TCYeampqPJYnr6+vJz88P\nMCIREekOlbCwHx5tH3e/Kh2xpIrm2ZLpVMNURDrC3bn//vu5++67VUNdQiFtNZDN7GpiCePR8VVr\niCWMn3L3XckIIFNoYiuS2R544AFuuOEGAHr37s3WrVsZMmRIwFGJiEh3RT2BHAWaZ0sYqLmeiByJ\nftEkYZTOGsizgB1AMZDr7iXuXpltyWOJDtUXChd3Z8aMGZgZN9xwA0OHDmX37t00Nja2mzzWGEeD\nxjkaNM4iItIRybhemBnTp0+nsrKS4uJilixZ0v3AJCtoPiINDQ2MHTuWXbt2UVtbm9Tksc4vCYte\nR9ro7qenKxARkWaNjY1cccUVLF++HIAxY8ZQU1ND3759A45MREQkucxs2NH2cfet6YhFRP7SXK+o\nqIhXX32V+fPnq76pSIRt27aNiy++mJKSEubNm6fvBxJZHaqB3KkDmg1L5yTXzE4CXgOOAfq5+8GE\nbbcA/wScALwA3ODuL7dzHD1aJxKwffv2UVhYyKZNmwCYOnUqDz74oC7SIiJZLOolLMzsMHDESai7\nd/pCaGZnAouA84C9wCPAt4804Y33PZkH5APDgT5H+2wzuwSoBn7l7l9uZx/NsyV0GhoaKCkpoX//\n/ixdupR+/foFHZKIpNmqVauYNGkS99xzD5MnTw46HJFOS2cJi/YCONnMPtPWC5iSjMA64bvAB23E\neDNwKzAf+AfgT8BaMzsxveGJyNHs3LmTE044gYEDB7Jp0ybmzp3L4cOHeeihh5Q8FhGRbPdV4Gut\nXiXAQ8DvgUs6e0AzGwisBQ4BRcCdwI3xP4+kL7G5/AGgtgOf0we4F1B5O8k6ubm5rF69mkGDBjFy\n5Eh27NgRdEgikibNNdGvuuoqqqurlTwWoYsJZOAaYB3w78CSVq+vJyWyDjCzvwXGEksiJ67vA9wE\nzHP3B939GWACsbs7ytIVn2Qe1RfKLFu2bMHMOPnkk2loaKCqqgp355ZbbsGsa78k0xhHg8Y5GjTO\nEgXu/lwbrxp3vxb4D+DyLhz2WuBTwHh3X+fuDxFLHv+rmR13hFj2uXuuu48DajrwObOAncCqLsQo\nkjSpul7k5OSwePFipkyZQkFBAbW1R/29imQhzUeipbGxkWnTpvHoo49SV1eX8oaaOr8kLLqUQHb3\nO4D73f1r7v7VxBdQntwQ22ZmPYD7iU2GG1ptPh/oBzyZEPNB4GfAuHTEJyLtW7t2LWbG2Wef3bLs\n7kycODHgyERERDLKs3ThDmTgQuBpdz+QsO7HxO4wviAZgcWfPJwJ/DMQ2RIkkv3UXE8kOlLZLE8k\n7Lp6BzJAZTvrF3fjmJ1xLZADfL+NbWcATcDrrdZvi2+TiBo1alTQIURaVVUVZsaYMWOA2B3I7k5h\nYWHSPkNjHA0a52jQOIvw98TqF3fWGcBvEle4+9vAQZI3F14I/NjdNyfpeCJdlo7rRXNzvTlz5jBr\n1iyamppS/pmSGTQfiYZt27aRn59Pfn4+1dXVaat7rvNLwqJXV9/Y6o6GxPUfdT2cjjGzXOA7wBXu\n3tTGo+7HA39qo1vHH4G+ZtbL3Q+lOk4RidWPmjt3LrNnzwYgLy+PF198kcGDBwccmYiISPDM7Ik2\nVucQS/QOBW7pwmGPp+3E8x/j27rFzL4GjCYWn0hkDBs2jA0bNlBSUkJxcbGa64lkCTXLEzm6LieQ\n22Nmw9x9a7KP28pc4Hl3fzqZB508eXLLIwoDBw7knHPOafltUHNdGi2He7l5XabEk83LTU1N/OhH\nP+LRRx8F4PTTT2fDhg0MGDCA9evXs3379pR8fuuxzpR/Dy0nd3nz5s1Mnz49Y+LRcmqW9f85O5c3\nb97M3r2x3KaaUgFwIrE+HYn+B/gl8K/u/lT6Q2qfmfUEKoC73H1PR9+nebaWU/19JV3zgldeeYVb\nb72VJ598kpEjR3LLLbcwaNCgjPr30HJ4zy8tp3f52WefZfny5Sxfvpzq6mo++ugj1q9fr/NLy6Fd\nTuU82z55k24H3mR2Mu3XOrvB3Wd0K6ojf/Yw4CXgK8Bv46uvBB4ATgb+AFxFbGLbJ/EuZDObAdzh\n7p/4NbGZtXHDsmSbxIuBpMbBgwcpKipi3bp1AEyYMIHHH3+cnJyctHy+xjgaNM7RoHGOBjPD3VVD\nN4nM7D1gkbvPabX+T8Tmwgs7cIzriPU86dlq/bXAt4BzgY+I/UzwPWJ3TH8NOND6ST/NsyXVgrhe\nuDsVFRWUl5ezbNmylDfakuBoPpKdGhsbKSsro76+nhUrVgRW71jnl6RSMufZXU0g3wlcAbzNJxPJ\nQ9z95CTE1t5nXwL8pI3PhdjdG48CPwLWAae7++sJ730EONvdR7RxXE1sRbrh/fffp6CggDfeeAOA\nmTNnUl5eThslZkRERFpEPYFsZrcDj7j7u21s+zQw1d2/08ljPgfsdPcrE9YNBt4CLnb3X3TgGO0l\nkP8NuIH25+IT3f0/Wr1H82zJWitXrqS0tJQFCxZQWloadDgi0gENDQ2UlJTQv39/laKRrJbMeXaX\nSli4+x1mtsfdH2i9zczKuh/WEf0S+GqrdeOAWfE/3yQ2Of4AmADMi8fVF7gY+EGK4xOJlNdff50z\nzzyzpZHIokWLuO666wKOSkREJDTuAFYBn0ggAyfFt3cqgQysBGaY2bEJfUv+kVgTvee6GmjcA0B1\nq3U3A6cA36RV8z6RbNfcXK+oqIhXX32V+fPn07Nnz6O/UUQCsW3bNi6++GJKSkqYN2+e/r+KdFCP\nzr7BzC6K/7WynV0Wdz2co3P3P7j7fyW++MtE9b/d/XV3/xC4G7jFzP6/eKOPJ4ndKbEolfFJZmuu\nESPdV19fj5lx2mmn0dTURE1NDe4eePJYYxwNGudo0DhLRBifrIHcbDCxxned9QPgQ6DazArN7JvE\nEtEL3f1PLR9stt3MHv5YMGYXmlkJ8MX4ckn89RkAd/9dG3PxXcB+d/+lu7/fhXhFuiXo60Vzc72N\nGzdSXFzM/v37A41Hkivo80uSZ9WqVVxwwQXcdtttlJeXZ0TyWOeXhEVX7kCuJlZb+EBbG939o+6F\nlBzufrfFnp3/FpALvACM1qRWpHtqamooLi5uWa6vryc/Pz/AiERERMLFzEqB5mfdHXjQzD5otdun\ngLOA1Z09vrvvNbNCYjdOrAD2AguBO1vt2oNP3lDyIPCZhOUn4n9eBVR1NhaRqMjNzWX16tWUlZUx\ncuTIQGuqisjHJdYsr66uVs1ykS7odA1kM/sIuIjYXQm7gZ+5e0MKYksr1WYTObJFixZx/fXXA9C7\nd2+2bt3KkCFDAo5KRETCLoo1kM1sAnB5fLEEeJZYI+hEjcSesvt+2OfammdLlKi5nkhmyZRmeSJB\nCLSJnpkdBn4FrCV2d8J5QEVb9ZDDRBNbkU9yd2bOnMnChbFm7UOHDqW2tpa8vLyAIxMRkWwRxQRy\nIjP7ITDH3X8XdCyponm2RJGa64kET83yJOqSOc/udA1kYo/ZjXH3W9z9G8D/Bo6P11YTyWiqL9Qx\njY2NXHbZZfTo0YOFCxcyZswYDhw4wGuvvZbxyWONcTRonKNB4yxR4O5XZXPyWCQdMvF60dxcb86c\nOcyaNaul4bSETyaeX3J027ZtIz8/n/z8fKqrqzM2eazzS8KiKwnkbcAJzQvu/j/u/h3gfyUtKhEJ\nxL59+xg+fDh9+vRh+fLlTJ06lUOHDrF69Wr69u0bdHgiIiJZycy+bmZrzewtM9vd+hV0fCLSNWqu\nJxKMTGyWJxJ2XSlhcSlwE3CZu7+TsP5f3P3fkhxf2ujROomynTt3cs4559DQECuxOHfuXG6++WZi\nfShFRERSRyUs7AqgEvh34Jvxv/cAiog1v6uK36wRWppnS9SpBqtIeqgGucjHJXOe3auzb3D3GjPr\nCfyXmW0DXgEGAO8mIyARSZ8tW7Zw9tlntyxXVVUxceLEACMSERGJnJnAHOBuYgnk77v7i2bWD1gD\nHAwyOBHpvpycHBYvXkxFRQUFBQVKbImkQOIvaurq6vSLGpEk60oJC9x9ObHax1XAPuDn7n5XMgMT\nSQXVF4pZu3YtZtaSPF67di3unhXJY41xNGico0HjLBExFKh19yagCegP4O77gXKgLMDYREIhDNcL\nM2P69OlUVlZSXFzMkiVLgg5JOigM51fUNTQ0MHbsWHbt2kVtbW2oksc6vyQsupRABnD3P7v7E+5+\nt7s/lcygRCQ1qqqqMDPGjBkDxO5AdncKCwsDjkxERCSyPgD6xP/+DnBmwjYDctMekYikjJrriSRX\nWJrliYRdp2sgZyvVZpNs5e7MnTuX2bNnA5CXl8eLL77I4MGDA45MRERENZDN7KfAf7v7AjO7H5gA\n3A40xv/8nbuPCTLG7tI8W+STGhoaKCkpoX///ixdulRJL5EuWLVqFZMmTeKee+5h8uTJQYcjknGS\nOc/u8h3IIpLZmpqauOaaa+jRowezZ89mxIgR7N27l927dyt5LCIikjnmA2/F/347sBF4EPghsAeY\nFlBcIpJCubm5rF69mkGDBjFy5Eh27NgRdEgioeHu3HfffVx11VVUV1creSySBkogS6REob7QwYMH\nGT16NL169eLRRx9lwoQJfPjhh2zcuJEBAwYEHV7KRWGMReMcFRpnyXZm1hvoCfwSwN33uvslwLHA\nQHfPd/ffBRmjSBiE9XrR3FxvypQpFBQUUFtbG3RI0oawnl/ZqrGxkWnTplFZWUldXV3oG1Lq/JKw\nUAJZJEu8//77DBkyhGOPPZZ169Yxc+ZMDh8+zBNPPEFOTk7Q4YmIiMgnNQHPAGckrnT3D939g2BC\nEpF0UnM9kY4Lc7M8kbDrdg1kMxvo7nuTFE9gVJtNwur111/nzDPPbGnAsWjRIq677rqAoxIREekY\n1UC2XwPz3P0/go4lVTTPFumYrVu3UlRUxPjx45k/fz49e/YMOiSRjLFt2zYuvvhiSkpKmDdvnv5/\niHRAIDWQzexaM5uVsHyOme0EGsxsk5mpqKpIGtXX12NmnHbaaTQ1NVFTU4O7K3ksIiISLrcCt5vZ\nWUEHIiLBGjZsGBs2bGDjxo0UFxezf//+oEMSyQirVq3iggsu4LbbbqO8vFzJY5EAdKaExfVA4qN0\n9wPvAlfGj3N3EuMSSYlsqC9UU1ODmVFQUABAXV0d7s4ll1wScGSZIRvGWI5O4xwNGmeJiNuAXGCz\nmb1lZi+Y2cbEV9ABimS6bLpeqLle5smm8ytsotAsT+eXhEWvTuz7GeC3AGaWB4wECt19vZk1AotS\nEJ+IxC1atIjrr78egN69e7N161aGDBkScFQiIiLSTb+Ov0REgL8016uoqKCgoIBly5aFvlGYSGc1\nNjZSVlZGfX09dXV1qncsErAO10A2swbgCnd/2swuBx4l1h26ycxGAU+5e9/UhZpaqs0mmcjdmTlz\nJgsXLgRg6NCh1NbWkpeXF3BkIiIiyRH1GshRoHm2SNetXLmS0tJSFixYQGlpadDhiKRFQ0MDJSUl\n9O/fn6VLl9KvX7+gQxIJpUBqIAMbgevM7PPADcAqd2+Kb/scsXIWIpIEjY2NXHbZZfTo0YOFCxcy\nZswYDhw4wGuvvabksYiISBYys2FmNtHMbjGzQfF1Q8xMPzWLRNi4ceNYv349c+bMYdasWS2Ns0Wy\n1bZt28jPzyc/P5/q6molj0UyRGcSyDcCnwdeAU4m1vCj2deB2iTGJZISmV5faN++fQwfPpw+ffqw\nfPlypk6dyqFDh1i9ejV9+4b2Bv+0yvQxluTQOEeDxlmiwMyOM7MniJWxeASYA5wU3zwPuCOo2ETC\nItuvF2quF6xsP78ySRSb5en8krDocALZ3be6+6lAHnCKu7+WsHlG/CUiXbBz505OOOEEBg4cyKZN\nm5g7dy6HDx/moYceisRFU0REJMLuBc4HCoF+QOJjhk8BFwYRlIhkFjXXk2wWhWZ5ImHX4RrI7R7A\nbKC7701SPIFRbTYJwpYtWzj77LNblquqqpg4cWKAEYmIiKRX1Gsgm9ke4J/dfamZ9QQ+Aoa7+4tm\n9lVghbuH+vldzbNFksfdqaiooLy8XM31JCskNstbsWKFmuWJJFEgNZDN7Fozm5WwfI6Z7QQazGyT\nmQ1ORkAiUbB27VrMrCV5vHbtWtxdyWMREZHoOQZoaGdbP0AFT0WkhZkxffp0KisrKS4uZsmSJUGH\nJNJlDQ0NjB07ll27dlFbW6vksUgG60wN5OuBDxKW7yfWOO/K+HHuTmJcIikRdH2hqqoqzIwxY8YA\nsTuQ3Z3CwsJA48omQY+xpIfGORo0zhIRLwCT2tl2GfB8GmMRCaUoXi/UXC99onh+pYOa5cXo/JKw\n6EwC+TPAbwHMLA8YCcxy9x8Ta/bxteSHJxJ+7s5dd92FmVFaWkpeXh5vv/027s5ZZ50VdHgiIiIS\nrNnAeDNbC1wDOHCRmT0GTEBN9ESkHWquJ2EVxWZ5ImHX4RrIZtYAXOHuT5vZ5cCjwEB3bzKzUcBT\n7t43daGmlmqzSbI1NTUxbdo0Hn30UQBGjBjBmjVrGDBgQMCRiYiIZI6o10AGMLORxJ7mOw/oSSyJ\nXE/sZo3aIGNLBs2zRVJLNWQlLFTDWyS9kjnP7tWJfTcC18XrHt8ArHL35udkPkesnIVI5B08eJCi\noiLWrVsHwIQJE3j88cfJyckJODIRERHJRPEk8VfM7BjgeGCvux8MOCwRCYmcnBwWL15MRUUFBQUF\nSsxJRkr8RUddXZ1+0SESMp0pYXEj8HngFeBk4NaEbV8HQn93hGS/VNYXev/99xkyZAjHHnss69at\nY+bMmRw+fJgnnnhCyeM0Ug2paNA4R4PGWaLG3f/s7u8qeSzSObpeqLleKun86j41y2ufzi8Jiw7f\ngezuW4FTzSwX+EOr59BmALuSHZxIGGzfvp0zzjijpXHFAw88QFlZWcBRiYiISFiYWQ4wGfgy8Gng\n/wEbgCXu3hhgaCISMs3N9YqKinj11VeZP3++6stKoLZt28bFF19MSUkJ8+bN0/koElIdroHc8gaz\nYcC5xO5CrnT3XWY2BHjP3UNbtV+12aSz6uvrKSgoaFmuqanhkksuCTAiERGR8Il6DWQzOxNYBZwE\nbAJ2AycCXyJ2g8aF8Rs5QkvzbJH0a2hooKSkhP79+7N06VL69esXdEgSQatWrWLSpEncc889TJ48\nOehwRCInmfPszjTROw6oBC4DPiJ29/IId3/RzJ4A3nL3GckIKgia2EpH1dTUUFxc3LJcV1fHeeed\nF2BEIiIi4aUEsv0SGAD8g7u/lbD+M8DPidVD/tug4ksGzbNFgqHmehIUNcsTyQzJnGd3pgbyvcD5\nQCHQD0gM4CngwmQEJJJK3akvtGjRIsyM4uJievfuzeuvv467K3mcYVRDKho0ztGgcZaIGA7cnpg8\nBogv3wGMCCQqkRDR9aJtzc31pkyZQkFBAbW1alvUFTq/OqexsZFp06ZRWVlJXV2dksdHofNLwqIz\nCeTxwE3u/izQ1Grb74G/SVpUIhnC3ZkxYwZmxvXXX8/QoUPZvXs3jY2NDBkyJOjwREREJPx2AJ9q\nZ9ungLfa2SYiclRqrifppGZ5ItmrMyUsDgAl7r7KzHoSK2MxPF7CogiocveBKYw1pfRonSRqbGzk\niiuuYPny5QCMGTOGmpoa+vbtG3BkIiIi2UUlLOwSYCFwpbtvSFh/HvA4MMPda4KKLxk0zxbJDFu3\nbqWoqIjx48eruZ4knZrliWSeoGogrwfedfcr2kggVwEnuPtFyQgqCJrYCsC+ffsoLCxk06ZNAEyd\nOpUHH3xQFz8REZEUUQLZXiD2JF8usQZ6zU30TgQaiN2h3MLdv5zmELtN82yRzKHmepIKapYnkpmC\nqoE8GxhvZmuBawAHLjKzx4AJxGq0iWS09uoL7dy5kxNOOIGBAweyadMm5s6dy+HDh3nooYeUPA4Z\n1ZCKBo1zNGicJSJ+DfwCqAJWAS/G/6yKr3+11UtEWtH1ouNyc3NZvXo1gwYNYuTIkezYsSPokDKe\nzq/2uTv33XcfV111FdXV1Uoed4HOLwmLXh3d0d1/aWaFwN3AImJN9O4E6oHR7v5CakIUSZ0tW7Zw\n9tlntyxXVVUxceLEACMSERGRKHH3q4KOQUSipbm5XkVFBQUFBSxbtkyNzqTTGhsbKSsro76+nrq6\nOtU7FslyHSphYWa9gS8Db7r7u2Z2DHA8sNfdD6Y4xrTQo3XRsm7dOkaPHt2yvHbtWgoLCwOMSERE\nJJqiXsKimZmdDvw1n2yo5+6+MoCQkkbzbJHMtXLlSkpLS1mwYAGlpaVBhyMhoVIoIuGQ9hrIZtYD\n+DMwzt2fScYHZxpNbKPhscceY9KkSS3LW7Zs4ayzzgowIhERkWiLegLZzM4CfgScSewJv9bc3UNd\nT0vzbJHMpuZ60hlqlicSHmmvgezuh4HXgUHJ+FCRdHJ35s6di5kxadIk8vLyePvtt3F3JY+zkGpI\nRYPGORo0zhIRlcSaU/8DcDrw2VavzwUXmkg46HrRPcOGDWPDhg1s3LiR4uJi9u/fH3RIGUXn11+s\nWrWKCy64gNtuu43y8nIlj5NA55eERWea6N0K3B6/SyIwZjbBzH5qZjvNbL+Z/crM/rGN/W4xs7fM\n7KCZPWdmZ7d1PMleTU1NXHPNNfTo0YPbbruNESNG8LOf/Yzdu3czePDgoMMTERERgdidx99y95Xu\n/rq7/771qysHNbMzzWydmR0ws3fM7E4zO+IdKGbW28wWmNl/xefQTW3s08PMborvsyf+etrMhncl\nThHJDGquJ0eiZnki0qESFgBm9gJwCvBXwDvAe8DH3uzuX05yfG3F8TzwO6AG2ANcBMwArnf378X3\nuRm4Lb7+t8CNxGo4f97dd7dzXD1alyUOHjxIUVER69atA2DChAk8/vjj5OTkBByZiIiItKYSFvYM\n8CN3fziJxxwIvAr8GrgHOBW4F7jX3W8/wvsGEJtnbyTWbPtrrctnmNmxwNvAo8A6Yj8PXA+MBgrc\n/aU2jqt5tkhIuDsVFRWUl5eruZ4AH2+Wt2LFCjXLEwmRtNdAjn/ov9MqYdxaOrpIm9lfufsfWq1b\nCpzn7qeaWR9iye0F7j43vr0vsAP4QXuTZk1sw+/999+noKCAN954A4CZM2dSXl7OUW62ERERz6YR\n0AAAIABJREFUkQApgWxDiNVAvg94Ftjbep/ONq2O30wxA/iMux+Ir5sJ3AEMcvc/deAY1wH3t5FA\n7gH0c/d9Cet6A68Bz7j71W0cS/NskZBRcz0BNcsTCbu010AGcPfJ7n7VkV7JCKgDcfyhjdUvASfF\n/z4S6Ac8mfCeg8DPgHEpD1DSbvv27fTq1YsTTzyRN954gwceeAB355577vlE8lj1hbKfxjgaNM7R\noHGWiNhD7EaHKmJ39u5v49VZFwJPNyeP434M9AUu6E6w7n44MXkcX/cRsTueT2r7XSKppetF8o0b\nN47169czZ84cZs2aRVPTJyraREZUz69t27aRn59Pfn4+1dXVSh6nSFTPLwmfDieQzex2M2tzUmhm\nnzazdh+HS4Pzid31ALHmI03Emv4l2gackc6gJLXq6+sxM4YOHUpTUxPV1dW4O2VlZUGHJiIiItJR\njwNfA74L/BMwpY1XZ50B/CZxhbu/DRwkBfNhM8sBvkSsdJyIZAk114suNcsTkdY6U8KiiVhds41t\nbDsX2Nj6Ebd0MLNCYDUw2d0fM7NbgBnu/let9rsaeAjo4+6H2jiOHq0LiZqaGoqLi1uW6+rqOO+8\n8wKMSERERLpKJSzsADDV3f8jicdsJDYfvr/V+reBJe5+WweO0WYJi3b2/Q4wE/iCu7e+iUPzbJGQ\nUw3c6FANbJHsksx5dq/OfC7t10AeDPyx++F0jpmdAiwFqt39se4eb/LkyS0Xw4EDB3LOOecwatQo\n4C+PFWg5uOXq6mruvz/2c1CPHj2oqqriyiuvzJj4tKxlLWtZy1rW8tGXN2/ezN69sTK/O3bsQNhB\n7M7gUDKzvwduAf6lreRxM82ztazlcC8vXryYiooKvvSlL3HnnXdy/fXXZ1R8Wu7+cmNjI+PHj2fr\n1q3U1dVxyimnZFR8Wtaylo++nMp59hHvQDazUqC5Yv4FxGoNf9Bqt08BZwGr3b0kqdEdgZkdDzxP\nrNHIV939f+LrrwUqiN1p7An7zwDucPc2C/fozojM5O7MmjWL7373uwAMGTKE559/nry8vC4db/36\n9S3/uSQ7aYyjQeMcDRrnaNAdyHYRcCcwwd13JOmY7wGL3H1Oq/V/IjYfXtiBYxz1DmQzGwE8A/zQ\n3W84wn6aZ0tK6XqRPlFsrheF80vN8oIThfNLgpPOO5APAg3NnwvsA1o3sWsEVgLfT0ZAHWFmxwC/\nAHoC/9CcPI77TXz9ED5eB/kTteAkczU2NnLllVeybNkyAMaMGUNNTQ19+/YNODIRERGRpLoT+Azw\nmpntIHZzxMe4+5c7eczf0KrWsZkNJtZELynzYTM7Dfg5sAb452QcU0QyX3NzvaKiIl599VXmz5+v\n+rght3XrVoqKiigpKWHevHkaTxFpU2dqIP8Q+I67v5nakI4aR09gBTCcWE3m37Xa3gd4D7jH3efF\n1/UF3gR+4O53tHNc3RmRAfbt20dhYSGbNm0CYOrUqTz44IO6iImIiGQp3YFsPzzaPu5+VSeP+S1g\nBvA37n4gvm4G8G1gkLv/qQPHaPcOZDP7NFALvAuMbnUzR1vH0jxbJMvojtXsEMU7ykWiJJnz7A4n\nkFsF0Be4mtidDbuAKnf/fTIC6sBnPwRcA9wAvNBq84vu/lF80nwbMIvYXRY3AiOAz7v7++0cVxPb\nAO3cuZNzzjmHhobYDe9z587l5ptvxiyyP0+KiIhEQtQTyKlgZgOBV+OvcuBUYCFwb+LNFGa2HXjW\n3acmrLsQOBYYB1wFXB7f9IK7v2VmnwLqid01fSUffzrxQ3ff3EY8mmeLZCE11wsvNcsTiYZkzrN7\nHOWDFprZa63W9QNeBO4Dvg7cDrwcf4wtHcYQa+ZXQawGcuLr0wDufjcwF/gW8DNik+DR7SWPJThb\ntmzBzDj55JNpaGigqqoKd+eWW25JSfK4uci4ZC+NcTRonKNB4yzSNe6+FygkNtdfAdxBLIH87Va7\n9uCTPw88CDxBLHlM/O9PAKPiy/+LWP+TAcRKWCTOxX+SvK9CpON0vQhGTk4OixcvZsqUKRQUFFBb\nWxt0SCmRbedXY2Mj06ZNo7Kykrq6OiWPA5Zt55dkr6PVQP4q8HirdTOA04Br3L3SzPKI1T6bDUxM\nfogf5+6f7eB+84H5KQ5HumjdunWMHj26ZXnNmjUfWxYRERGJEjMbBpwLnAxUuvsuMxsCvOfu+zt7\nPHf/DXDEyZW7f66NdUeca8efOlRtMREBYne3TZ8+ndNPP53i4mKVQshwiaVHamtrVXpERDrsiCUs\nzOwPwER3/0XCul8DuPv/Tlg3EbizrUloWOjRuvR47LHHmDRpUsvyyy+/zBe+8IUAIxIREZEgRb2E\nhZkdB1QCJcAhYjd4jHD3F83sCeAtd58RZIzdpXm2SDQ0N2MbP368mutlIDXLE4metJWwIDaBbWmK\nYWZ/BZwJPNNqvx3AoGQEJNnH3Zk3bx5mxqRJk8jNzeWtt97C3ZU8FhERkai7Fzif2N3C/YDESf5T\nwIVBBCUi0lnDhg1jw4YNbNy4keLiYvbv7/TDE5IiK1euZNSoUcyePZvy8nIlj0Wk046WQH6Nv9Q7\nA/iH+J9Pt9rvRD7eQEOEpqYmpk6dSo8ePbj11lsZPnw4e/fuZc+ePZx88smBxKT6QtlPYxwNGudo\n0DhLRIwHbnL3Z4GmVtt+D/xN+kMSCRddLzJHbm4uq1evZtCgQYwcOZIdO3YEHVK3hfn8cnfuu+8+\npkyZQnV1tcqLZKAwn18SLUergbwIeNjMBgDvATcAbwKrW+03Fvh18sOTMDp48CCXXHIJa9euBeCy\nyy5j6dKl5OTkBByZiIiISMY5BmhoZ1s/PplUFhHJaM3N9SoqKigoKGDZsmVq1BaAxsZGysrKqK+v\np66ujlNOOSXokEQkxI5YAxnAzG4GrgMGAi8C17n7Kwnb84BXiNVAfjCFsaaUarN13/vvv8/555/P\n9u3bAZg5cybl5eWYRbasoYiIiByFaiDbeuBdd7/CzHoCHwHD4zWQq4AT3P2iQIPsJs2zRaJr5cqV\nlJaWqrlemiU2y1u6dKma5YlEVDLn2UdNIEeFJrZdt337ds444wyammI3yDzwwAOUlZUFHJWIiIiE\ngRLI9hVgDfDfwJPA94E7gNOBCcBX3P2F4CLsPs2zRaJNzfXSS83yRKRZOpvoibSrvr4eM2Po0KE0\nNTVRXV2Nu2d08lj1hbKfxjgaNM7RoHGWKHD3XwKFQB9i5eMMuBP4HFAY9uSxSDroepHZwt5cL0zn\nl5rlhU+Yzi+JNiWQpdNqamowMwoKCgB4/vnncXcuvfTSgCMTERERCRczux14092/AvQHBgP93H0k\n8Lv4dhGRUMvG5nqZRM3yRCTVVMIiTo/WHd33vve9lruLe/bsybZt2xg6dGjAUYmIiEiYqYSFNQEF\n7r6xjW3nAhvdPdS3kGmeLSLN3J2KigrKy8vVXC9JEpvlrVixQs3yRKSFSlhI2rg7s2bNwswoKyvj\n1FNPZffu3Rw6dEjJYxEREZHuM6C97Opg4I9pjEVEJKXMjOnTp1NZWUlxcTFLliwJOqRQa2hoYOzY\nsezatYva2lolj0UkZZRAljY1NjZy+eWX06NHDxYsWEBhYSEHDhxg+/bt5OXlBR1el6m+UPbTGEeD\nxjkaNM6Srcys1MyeMbNniCWPH2xeTng9DzwOPBdstCKZT9eL8Bk3bhzr169nzpw5zJo1q6UheybK\n1PNr69at5Ofnk5+fT3V1Nf369Qs6JOmCTD2/RFpTAlk+Zt++fYwYMYI+ffrw5JNPcs0113Do0CHW\nrl1L3759gw5PREREJBscBBriLwP2JSw3v94E7gG+GVCMIiIpFfbmekFSszwRSTfVQI6Lem22nTt3\n8sUvfpE9e/YAcNddd3HLLbdgFtmShCIiIpIGqoFsPwS+4+5vBh1LqkR9ni0iR6Yavh2nGtIi0hnJ\nnGcrgRwX1YntK6+8whe+8IWW5aqqKiZOnBhgRCIiIhIlUU8gR0FU59ki0nFKjB6dEu0i0llqoifd\ntm7dOsysJXm8Zs0a3D3rk8eqL5T9NMbRoHGOBo2ziIh0hK4X4ZfJzfUy4fxSs7zslQnnl0hHKIEc\nMY899hhmxujRowF4+eWXcfeWZREREREREZEghKm5XrqoWZ6IZAKVsIjL5kfr3J358+dz6623ApCb\nm8tLL73EySefHHBkIiIiEnUqYZH9snmeLSKp0dDQQElJCf3792fp0qWRTZquXLmS0tJSFixYQGlp\nadDhiEjIqISFdEhTUxPf/OY36dGjB7feeivnnnsue/fuZc+ePUoei4iIiIiISEbKzc1l9erVDBo0\niJEjR7Jjx46gQ0ord+e+++5jypQpVFdXK3ksIoFTAjkLHTx4kLFjx9KrVy8efvhhSkpK+PDDD/nV\nr37FgAEDgg4vUKovlP00xtGgcY4GjbOIiHSErhfZKScnh8WLFzNlyhQKCgqora0NJI50n1+NjY1M\nmzaNyspK6urq1FAwy+n7l4SFEshZZM+ePZx22mkce+yxrFmzhhtvvJHDhw+zbNkycnJygg5PRERE\nREREpMMyubleKqhZnohkKtVAjgtzbbbt27dz5plncujQIQAeeOABysrKAo5KRERE5OhUAzn7hXme\nLSKZY+vWrRQVFTF+/Hjmz59Pz549gw4pqZq/vpKSEubNm5d1X5+IpF8y59lKIMeFcWK7YcMGzjvv\nvJbl6upqLr300gAjEhEREekcJZCzXxjn2SKSmbK1uZ6a5YlIKqiJXsT99Kc/xcxaksfPP/887q7k\ncQeovlD20xhHg8Y5GjTOIiLSEbpeREcQzfVSeX6pWZ7o+5eEhRLIIfK9730PM+PSSy+lZ8+evPba\na7g7BQUFQYcmIiIiIiIiknKZ0lyvu9QsT0TCRCUs4jL10Tp356abbmLBggUAnHrqqdTV1ZGXlxdw\nZCIiIiLdpxIW2S9T59kiEn5hLf2QraU4RCSzqIRFBDQ2NnL55ZfTo0cPFixYQGFhIQcOHGD79u1K\nHouIiIiIiEjkjRs3jvXr1zNnzhxmzZpFU1NT0CEd1datW8nPzyc/P5/q6molj0UkFJRAzjAffPAB\nX/7yl+nTpw9PPvkkV199NYcOHWLt2rX07ds36PBCT/WFsp/GOBo0ztGgcRYRkY7Q9SLahg0bxoYN\nG9i4cSPFxcXs378/qcdP5vm1cuVKRo0axezZsykvL6dnz55JO7aEk75/SVgogZwh3nnnHU488UQG\nDBjACy+8wJw5czh8+DCPPPKILioiIiIiIiIi7QiiuV5nqFmeiISdaiDHBVWb7ZVXXuELX/hCy/KS\nJUuYNGlS2uMQERERCYJqIGc/1UAWkXRxdyoqKigvL2fZsmUZ0ZiusbGRsrIy6uvrWbFiBaecckrQ\nIYlIRKgGchZ45plnMLOW5PGaNWtwdyWPRURERERERLrAzJg+fTqVlZUUFxezZMmSQONpaGhg7Nix\n7Nq1i9raWiWPRSS0lEBOs8ceewwzo7CwEICXX34Zd2f06NEBRxYNqi+U/TTG0aBxjgaNs4iIdISu\nF9JaMpvrdfX8UrM86Qh9/5KwUAI5DdydefPmYWZMmjSJ3Nxc3nrrLdz9Y+UrRERERERERKT7Ut1c\n70jULE9Eso1qIMelojZbU1MT1157LQ8//DAA5557LuvWrWPAgAFJ/RwRERGRsFIN5OynGsgiEqR0\n1iDOxBrMIhJdqoGc4Q4ePMjYsWPp1asXDz/8MCUlJXz44Yf86le/UvJYREREREREJE1ycnJYvHgx\nU6ZMoaCggNra2pR8TmNjI9OmTaOyspK6ujolj0UkqyiBnER79uzhtNNO49hjj2XNmjXceOONHD58\nmGXLlpGTkxN0eILqC0WBxjgaNM7RoHEWEZGO0PVCjqY7zfU6cn6pWZ50lb5/SVgogZwEb7zxBjk5\nOeTl5fH6669z//334+5897vfxUxPZIqIiIiIiIgELZnN9ZqpWZ6IRIFqIMd1pTbbhg0bOO+881qW\nf/KTn1BcXJzs0ERERESylmogZz/VQBaRTNPQ0EBJSQn9+/dn6dKlXU76rly5ktLSUhYsWEBpaWmS\noxQR6R7VQO4AMzvTzNaZ2QEze8fM7rQk3Q7805/+FDNrSR4///zzuLuSxyIiIiKSEboyFzaz3ma2\nwMz+y8wOmlm7t+aZ2SVmtsXM/mxmr5rZ5cn/KkREUiM3N5fVq1czaNAgRo4cyY4dOzr1fnfnvvvu\nY8qUKVRXVyt5LCJZLysTyGY2EFgLHAKKgDuBG+N/dtn3v/99zIxLL72Unj178tprr+HuFBQUdD9o\nSQvVF8p+GuNo0DhHg8ZZpGu6MRfuC0wBDgDtdpkys/8DLAPWARcCPwd+ZGajux28SBfoeiFd0dHm\neq3PLzXLk2TS9y8Ji6xMIAPXAp8Cxrv7Ond/iNiE+V/N7LjOHMjdmTVrFmbGddddx6mnnsp7773H\noUOHGDp0aCpiFxERERHpji7Nhd19n7vnuvs4oOYIx58NPOfu/+Luz7n7TcAq4PYkfg0iIinX2eZ6\napYnIlGVlTWQzew54B13vyJh3cnA74GL3f0XbbznY7XZGhsb+cY3vsGTTz4JQGFhIStWrKBv374p\nj19EREQkKlQDOfm6Mhdu4xjXAfe7e89W63OA/cD18cR08/qJQCXwV+6+v9V7VANZRDLe1q1bKSoq\nYvz48cyfP5+ePXu2ub2kpIR58+Z9YruISKZRDeSjOwP4TeIKd38bOBjf1q4PP/yQhx9+mL59+/Lk\nk09y9dVXc+jQIdauXavksYiIiIiEQZfnwh1wKtC79fGBbcR+tjitm8cXEQnEsGHD2LBhAxs3bqS4\nuJj9+//yu7CVK1cyatQoZs+eTXl5uZLHIhI52ZpAPh7Y28b6P8a3tWnBggV89rOfpbq6mmeeeYbD\nhw/zyCOP6OKQRVRfKPtpjKNB4xwNGmeRLuvSXLgTx/Y2jv9HwJJwfJFO0/VCkqWt5nplZWVqlicp\no+9fEha9gg4gk7z00ks89dRTnHPOOUGHIiIiIiIiIiJp1txcr6KigrPOOou8vDzq6upU71hEIi1b\nE8h/BAa0sf74+LY25eTkUFNTQ01NDQMHDuScc85h1KhRwF9+K6RlLWs5s5dHjRqVUfFoOXXLzTIl\nHi0nf1n/n7NzefPmzezdG7t5dceOHUhKdGku3IljWxvHPz5h+ydMnjy5JfmiebaWU7HcLFPi0XL4\nl6dPn85xxx3HSSed1PL9K5Pi03L2LDfLlHi0HN7lVM6zs7mJ3k53vzJh3WDgLTrYRE9EREREUk9N\n9JKvK3PhNo5xtCZ6Ze7+cMJ6NdETERERySBqond0K4G/M7NjE9b9I7HGIc8FE5Jkgta/4ZPsozGO\nBo1zNGicRbosZXNhd28EngUmtNr0daCudfJYJB10vZBU0vklqaTzS8IiWxPIPwA+BKrNrNDMvgnc\nASx09z8FG5qIiIiISEp1aC5sZtvN7OHEN5rZhWZWAnwxvlwSf30mYbc5wCgz+zczu8DM7gEuBO5M\n8dclIiIiIgHIyhIWAGZ2BrAIKCDWJfph4M72np/To3UiIiIi6acSFqnRkbmwmf0OeNbdr05Y9ybw\nGT7pKnevStivCLgLGAq8Cdzh7k+2E4vm2SIiIiJplsx5dtYmkDtLE1sRERGR9FMCOftpni0iIiKS\nfqqBLNJFqi+U/TTG0aBxjgaNs4iIdISuF5JKOr8klXR+SVgogSwiIiIiIiIiIiIibVIJizg9Wici\nIiKSfiphkf00zxYRERFJP5WwEBEREREREREREZGUUwJZIkX1hbKfxjgaNM7RoHEWEZGO0PVCUknn\nl6SSzi8JCyWQRURERERERERERKRNqoEcp9psIiIiIumnGsjZT/NsERERkfRTDWQRERERERERERER\nSTklkCVSVF8o+2mMo0HjHA0aZxER6QhdLySVdH5JKun8krBQAllERERERERERERE2qQayHGqzSYi\nIiKSfqqBnP00zxYRERFJP9VAFhEREREREREREZGUUwJZIkX1hbKfxjgaNM7RoHEWEZGO0PVCUknn\nl6SSzi8JCyWQRURERERERERERKRNqoEcp9psIiIiIumnGsjZT/NsERERkfRTDWQRERERERERERER\nSTklkCVSVF8o+2mMo0HjHA0aZxER6QhdLySVdH5JKun8krBQAllERERERERERERE2qQayHGqzSYi\nIiKSfqqBnP00zxYRERFJP9VAFhEREREREREREZGUUwJZIkX1hbKfxjgaNM7RoHEWEZGO0PVCUknn\nl6SSzi8JCyWQRURERERERERERKRNqoEcp9psIiIiIumnGsjZT/NsERERkfRTDWQRERERERERERER\nSTklkCVSVF8o+2mMo0HjHA0aZxER6QhdLySVdH5JKun8krBQAllERERERERERERE2qQayHGqzSYi\nIiKSfqqBnP00zxYRERFJP9VAFhEREREREREREZGUUwJZIkX1hbKfxjgaNM7RoHEWEZGO0PVCUknn\nl6SSzi8JCyWQRURERERERERERKRNqoEcp9psIiIiIumnGsjZT/NsERERkfRTDWQRERERERERERER\nSTklkCVSVF8o+2mMo0HjHA0aZxER6QhdLySVdH5JKun8krBQAllERERERERERERE2qQayHGqzSYi\nIiKSfqqBnP00zxYRERFJP9VAFhEREREREREREZGUUwJZIkX1hbKfxjgaNM7RoHEWEZGO0PVCUknn\nl6SSzi8JCyWQRURERERERERERKRNoaqBbGb9gBnAhcDpwJ+BOuAmd3+91b4nAd8DCoEPgR8Ds9z9\nz+0cW7XZRERERNJMNZBTw8zOBBYB5wF7gUeAbx9twmtm/YEK4BJiN5v8HLjB3f+QsE9v4GZgIvDX\nwDvAUmCeuze2cUzNs0VERETSLJnz7F7JOEgafQa4mtgE+Bagb/zPDWZ2lru/A2BmvYDVwP8AlwPH\nA/8GDAAmBRC3iIiIiEhamNlAYC3wa6AIOBW4FzDg9qO8/UlgCDAFcOAeoBq4IGGfcuCbwK3AZuBL\nwFxic+1/SdbXISIiIiKZIWwlLH4HnOru33b3de7+M+AioDexSW6zCcTuUB7v7qvc/UfA9cAVZnZq\n2qOWjKH6QtlPYxwNGudo0DiLdNm1wKeIzYXXuftDwJ3Av5rZce29ycwKgDHAJHevcfefAt8AvmJm\nX0vY9f8C33f3Cnd/zt3/DXgQ+MdUfUEiR6LrhaSSzi9JJZ1fEhahSiC7+5/d/cNW6/4I/B44KWH1\nhcAL7v5Wwroa4KP4NomozZs3Bx2CpJjGOBo0ztGgcRbpsguBp939QMK6HxN7eu+Ctt/S8r5d7l7b\nvMLdXwDeBMYl7Ncb+KDVe/cRu8NZJO10vZBU0vklqaTzS8IiVAnktphZHrHH7H6bsPoM4DeJ+7n7\nR8Ab8W0SUXv37g06BEkxjXE0aJyjQeMs0mVtzYXfBg5y5LnwJ94Xt63V+x4BppnZ+WZ2rJl9Bfgn\n4IFuRS3SRbpeSCrp/JJU0vklYRG2GshtWQjsB5YkrDueWLOQ1v4Y3yYiIiIikq26Ohc+0vs+27zg\n7t8ys2OA/25eRaykxdyuhSsiIiIimSzwBHK80/Onj7afu/+29Tozuxa4glh9tz+mIDzJMjt27Ag6\nBEkxjXE0aJyjQeMskpnMbBZwJXAd8ApwNnCXmf3B3e8INDiJJF0vJJV0fkkq6fySsDB3DzYAs6uB\nh4ndudDmLoC7e89W7ysClgM3ufu9rbZtAH7t7le3Wv9r4Fl3v76NOIL9hxARERGJKHdX7dwkMrP3\ngEXuPqfV+j8Bd7j7wnbe95/ACe5e2Gr9z4nNxy82s1zgXeBad69M2OebxEpY/LW772n1fs2zRURE\nRAKQrHl24Hcgu/ujwKOdeY+ZjQR+ROxRuXvb2OU3tKrvZma9gc8R6xDdVhz6wUVEREREskFbc+HB\nxJrotVXjOPF917Sx/gygOv73zxH7GeLlVvu8FF//N8DHEsiaZ4uIiIiEW+ia6JnZ54EVwFPu/s/t\n7LYSGGFmJyesuwTIAValOEQRERERkSCtBP7OzI5NWPePxJroPXeU9w0ys/ObV5jZcGJJ46fiq35P\n7AnBL7V67/D4nzu6HraIiIiIZKLAS1h0hpnlAS8CTUAp8D8Jmz9w923x/XrF92sEZgMDgXuB1e5e\nmtagRURERETSyMwGAq/GX+XAqcQaT9+bWKPYzLYTK+82NWHdKmAIMJNYibm7gV3uPiphn58Ao4Bv\nA1uALwJ3ACvd/f+m8EsTERERkQCELYF8AfBMO5ufc/evJex7ErAIGP3/s3fnYVJU18PHv2dAVCQI\natCgwTUSlRiSiPuWqEFxQXEjLoBbEhMXBEGNP1FccUE0GHejuKCiUWNAJUjcE3cNMe6IS9xQAXVE\nBJn7/tFN3sk4wCzdU90938/z9INVXff2manbVcc7VaeAr8iVvBieUpq3mPaSJElSRYiI75PLhbcA\n5pB75sjIVCv5j4g3yE0gH1ZrXUdgDLAXubsV/wIcm1KaVWubDsCI/DZdgXfJPZvkzJTSF0X+0SRJ\nktTCyqqERUrpoZRSm0UvoAfwILkrkbtHxMiIiPy276WU+qWUOqaUvp1SOmbR5HFEdIyIayNiVkTM\niYgbI2Klup8XEX0jYlpEfBkR/46I/erZpkF9qWkiYoOImBoRX0TEu7X38VLaFWQfR8SaEVFTz2t8\nIX/O1q6Y+zkidoyI8RExI7/vRjS1LzVP1vvZ73PLKNZ+joiqiDghIh6OiI/zr8mRu72+UX2pebLe\nx36XGyal9HJKaceU0goppdVTSqelOleOpJTWqfvQ6ZTSZymlw1JKK6WUOqWUDq49eZzfpjqlNDyl\n9D1ypSveAY4BXi10rqbWrSnHG48RaqiIWDciroiIf0bE1xGxuIvV6rbz+KWlasr48vhjVq85AAAg\nAElEQVSlhoiIfSPizxHxn4j4PCKejoj+DWjXrGNX5g/Ra6rI3Zp3P/ACsAe5W/MuJFeTrd4Jolpu\nI3dr3qHkbs07j9yDQbar1f/WwO3krtw4GugD3BwRs1JK9zemLzVNCe1jgCHA32stf4wKotj7GdgZ\n+EH+M5Z0UPW7XEQltJ/B73PRFHk/Lw+cQO7Bu2fntzkaeDQitkgpPdeIvtREJbSPwe9y5lrg2K5W\nrJnjCzxGaOk2IpdDPk7j5kY8fqkhmjq+wOOXluw44A1gMLmx0QcYHxErp5T+sIR2zTt2pZTK8gWc\nBHwCrFBr3TCgGuiwhHZbADXAVrXW9cqv+1mtdZOB++u0nQQ83Ni+fJX1Pl4z365P1r+PSn0Vez/X\nafMRMKIQffkqy/3s97mM9zO5u6ZWrNNuGWAGcE1zxoyvstvHfpdL5NWSx3Zfre/VjPHlMcJXo1/k\nJlb+1oDtPH75avSrEePL45evpb6AlepZdxMwfQltmn3sKqsSFnXsDExO/1tn7RagPUuePd+Z3INA\nHlu0IqX0FLn/OdkFICLakXswyIQ6bW8BtoiIbzW0LzVLKexjFV/R9nMjY/C7XFylsJ9VfEXbzyml\nmpTSp7UbpZQWkHtIWNfG9KVmKYV9rNLhsV3F1NTxJRWTxy9JmUp1SovlPceS8+VmH7vKeQL5+8DL\ntVeklN4B5ubfa3C7vJdqtVuX3BUvdbd7idzvbP1G9KWmK4V9vMi1+bpF70XE6IhYrmE/ghqgmPu5\nyTE0oy/VrxT28yJ+n4unRfdz/o+BPwZeaW5farBS2MeL+F3OXikd21V5mjq+FvEYoWLw+KWW4PFL\njbUl8OoS3m/2satsayADnck9Ubqu2fn3mtJu7VrbpHq2m02u5lbnWtstrS81XSns46/I1Uj+K/AZ\nuauWTwTWIffkcTVfMfdzIWLwu1wYpbCf/T4XX0vv5//Lt61d68vvc3GVwj72u1w6SuHYrsrV1PHl\nMULF5PFLxeTxS40WETsAfYFBS9is2ceucp5AlooupfQBuaeKL/JwRMwE/hARP0gp/Suj0CQ1kt/n\nyhIRuwK/A45LKb2WdTwqvMXtY7/LkpbEY4SkcuXxS40VEWuRq398Z0rphmJ+VjmXsJgNrFjP+s75\n95rTbtFVqHW361zr/ebEoIYphX1cn9vzbX+yhG3UcMXcz8WOQQ1XCvu5Pn6fC6tF9nNE9CJXB/PS\nlNLYAsWghimFfVwfv8vZKNVjuypDIceJxwgViscvtTSPX6pXRHQG7iVXx/igpWze7GNXOU8gv0yd\nOh0RsQa5hyrUV9djse3yatcDmQ4sqGe7DYCF/P+6Ig3pS01XCvu4PqnOv2qeYu7nJsfQjL5Uv1LY\nz/Xx+1xYRd/PEbE+MBGYAhzbnL7UJKWwj+vjdzkbpXpsV2Vo6viqj8cIFYrHL7U0j1/6hohYHpgE\ntAF2SynNW0qTZh+7ynkC+V6gd0SsUGtdf3IPVXhoKe1Wi4gtF62IiE3I1ZS5ByClNB94ANi3Ttv9\ngX+klD5vaF9qllLYx/XZl9zB+5kG/hxasqLt50bG4He5uEphP9fH73NhFXU/R8R3gPuA14ADUkr1\nJdJ+n4urFPZxffwuZ6NUj+2qDE0dX/XxGKFC8fillubxS/8jItqQuzJ9XWDnlNInDWjW7GNXNDwv\nLy0R0Qn4d/51Lrlf3GjgwpTSqbW2ex14IKV0RK119wHrAcPIfRFHAR+klLavtc1W5CYY/wDcBewK\nDAF6p5SmNqYvNU0p7OOIOBX4FvAYuSL22wHHAxNTSvsV5QdvZVpgP3cDepG77ecachMTE4AvUkr3\nNaYvNV0p7Ge/z8VXzP2cf/r040A34EBgVq2P/iql9HxD+1LTlcI+9rtcOop9bFfr1tTx5TFCDZW/\ngq8PufxxCLlxc1r+7UkppXkev9RUTRlfHr/UEBFxJXA4uXrZT9V5+9mU0oKiHLtSSmX7Inep9f3A\nF8C75L6MUWebN4Br6qzrSG6CYRa5pxDeAKxUT/97ANOAL4EXgX3r2aZBffkqz31M7orkJ8nVhJlH\nrrTFqcAyWf9uKulVzP0MDARqyJUmqf16oyljxlf57me/z+W9n4E169m/fp9b4T72u1xar2Ie2335\nasr48hjhq6Gv/HmnvvxxIdAtv43HL19NejVlfHn88tWQF7max4vLmYt27CrbK5AlSZIkSZIkScVV\nzjWQJUmSJEmSJElF5ASyJEmSJEmSJKleTiBLkiRJkiRJkurlBLIkSZIkSZIkqV5OIEuSJEmSJEmS\n6uUEsiRJkiRJkiSpXk4gS5IkSZIkSZLq5QSyJEmSJEkqKRGxb0QMrGf9AxExIYuYasUwLCK2bcT2\nQyJiaiO2HxsRVzctOkkqvEgpZR2DJJW9iBgEHAWsD3wNvAk8kFIamn9/X6B9SmlcAT/zWmCjlNKm\nhepTkiRJKgURcRuwckrpZ3XWfx9YkFKank1kEBEfAWNTSqc3YNsVgDeAA1NK9zew/zWBl8nl+m80\nK1hJKgCvQJakZoqIk4CrgHuBvYCDgbuA3Wttth/wjSsomul0YFCB+5QkSZJKVkrp5Swnj5vgAGBe\nQyePAVJKbwGPAkcWLSpJagQnkCWp+X4LXJZSOiWlNDWlNCmldHpKaf3GdhQRVRGxTEO2TSnNSCm9\n2OhoJUmSpBKWv9Nub2C7iKiJiIURMSL/3oO1S1hExGkR8VFEbBoRT0XE3Ih4JCLWjIhvR8SdEfF5\nRLwYET+t57MOj4gXImJeRLwZEcOWEtsMYCXgtFqxLamcxQDgjjp9rB4REyLiw3y8r0fEyDrt/gQc\nuKRYJKmlOIEsSc3XCfhwcW8uJQG+Lp/o9o2IF4AvgU0jYvOI+HNEvBcR1RHxXEQcUKff6yLiqdqf\nk+9rx4j4Z77dIxGxYVF+akmSJKk4TgceAJ4DNgO2ABbVBK5bhzMB7YErgAuB/sB3gRuBm4FHyN0l\n+C4wISKWW9QwP1l8KbkJ3l3z/31GRPxmCbHtCXyWj2fzfGzP1rdhRLTPx//3Om/dAKwOHA7sDJwJ\nLFtnm78Dq0bED5YQiyS1iLZZByBJFeBZ4JiIeAeYmFKaVef904FuwIrkbkML4D/59xKwFnBufrsP\ngBnANuRuW7sU+ArYCvhjRCxMKd1aq23dBLobcB5wBjAPGA3cAmxciB9UkiRJKraU0oyImEXuuU1P\nLbUBLAccnVJ6FHJX+AJ/AE5JKV2YX/cu8G9gO2ByRHwLGAGcnlI6M9/P1HzN4v+LiMtSPQ+NSin9\nMyK+Bv6TUnpyKXH9EGgDvFBnfS+gf0ppUn754Xra/huoATYF/rWUz5GkonICWZKa77fAncC1ABHx\nErlbzi5IKX3egAR4JeBnKaXaieGttTeIiEfIXUlxRN336ugMbLHoYRsR0Qa4IyLWTym9urhGEbE8\n8DtyV1CsCMwGDkspvbOEz5IkSZJKwfxFk8d5r5O70OKBOusgd+UvwJbkrly+PZ8zL/IAcAqwBtDc\nXHi1/L8f11n/PDAqIlYB/lZfzp1SWhgRc2r1IUmZsYSFJDVTfuJ3A2APclc6QC7pfCp/29rSvFtn\n8piI6BQRv8/XYVsALAB+CSytrvKbdZ7U/CK5K57XWEq7gcClKaUdU0q9yD0I8INa8YyMiO/WifEb\n6yRJkqQMfF5neX7+3zmLVqSUFuT/c1EJi5XJ5ckvksu1F73+Rm7yuRB57qLP+qrO+v2Ap8iV3Hgr\nX67uZ/W0/6pWH5KUGa9AlqQCyCekk/IvIuJQ4CrgMGDsUprXVz95HLnb1U4HXiJXZ+035Capl2RO\nneVFyfPSEs8NgdUj4iPg2Vq3/60AHEru6dHXLG6dJEmSVGYWlZ3rA8ys5/1XCvgZncjl8wCklN4n\nl08TEZsCI4E/R0S3lNLsWu071epDkjLjBLIkFUFK6Y8RcR7w/YZsXnshIpYl9xCPI1NKV9VaX5S7\nRvJXSa9IbrI5qHWFRErpC2BsROy1pHWSJElSgc2nuFff/gOYC6yeUrqvkW0bGtsr5PLrtYG369sg\npfRkRIwEHgPWJFdKjnx5i/bAYsvQSVJLcQJZkpopIr6dUvqo7jpyk7KLykA0JgFellyJoUVXD5N/\nyMce5B6kUWiHASeklD5YwjbRwHWSJElSIbwM7BERfck9gPq9/JW7DbXEXDWl9Gl+4vb3EbEWuQfZ\nVQHdge1TSv2WEtuuETEZqAZeSSlV1/MZb0bE+8BPgIcAIqIjMBm4ntzk8HLAEOB9cnceLtKLXO7/\n96X+pJJUZNZAlqTm+1dEXBERe0fENhFxMDAF+IJcYgi5JPMHEdE3In4SEd9ZXGcppc/I1UQbERH9\n8lf6TuGb5SkKpSOwzKKFiFg5IrYp0mdJkiRJDXEp8FdyJdOeJPcw6cZIi1n33/UppfPz/e4M3AWM\nB35BbjJ5SYaRy/Un5mP78RK2vQPYpdbyPGAacAzwZ3IP4v4C6J1Sql0ruTfwUJ2SFpKUCa9AlqTm\nGwn0BS4GViJ31fFjwH4ppbfy21wK9CSXAHfOtzl9CX3+AriCXC3kT4BLyN3CdlQT4qsvea7tUuCK\niFgd+BJ4BDijCZ8jSZIkFURK6RNg73rW/7TO8khyuXXtdQ8BbeppW9+68eQmjhsT27PAlg3c/Brg\nyYjoklKamVKaD/xqSQ3ypev2BoY3Ji5JKpZIaWnzCpKk1i4iHgAGppTeXtI6SZIkSf8rIv4CPJdS\nGtHA7fcnd7HJBimlYpSwk6RGsYSFJGmxImKZiDgKWA84JiI2rG9dtlFKkiRJJW0o8NFSt/pfhzl5\nLKlUeAWyJEmSJEmSJKleXoEsSZIkSZIkSaqXE8iSJEmSJEmSpHo5gSxJkiRJkiRJqpcTyJIkSZIk\nSZKkejmBLEmSJEmSJEmqlxPIkiRJkiRJkqR6OYEsSZIkSZIkSaqXE8iSJEmSJEmSpHo5gSxJkiRJ\nkiRJqpcTyJIkSZIkSZKkejmBLEmSJEmSJEmqlxPIkiRJkiRJkqR6OYEsSZIkSZIkSapXyU8gR8S6\nEXFFRPwzIr6OiL81oM0mEfHHiHgtIr6IiJcjYkRELNsSMUuSJKn8RcQGETE1n0++GxEjIyIa0K5j\nRFwbEbMiYk5E3BgRK9WzXd+ImBYRX0bEvyNiv6z7iogdI2J8RMyIiJqIGFFPP+bakiRJrUjJTyAD\nGwE7Ay8DrzSwzf7AOsAoYBfgEmAIcGMxApQkSVJliYhOwP3A18AewEhgaP7fpbkN2BY4FBgI9ALu\nrNP/1sDtwFRyue5E4OaI2DHLvvLtf5D/2b9YzM9nri1JktSKREop6xgaLCJuA1ZOKf1sKdutlFKa\nVWfdEcDlwFoppXeKGKYkSZLKXEScBBwPdEspfZFfNww4FVgtpVS9mHZbAI8B26SUHsuv6wU8AeyY\nUvpbft1koE1KacdabScB30opbZtVX3V+lo+AsSml0+usN9eWJElqRcrhCuRGq5vQ5j2X/7drS8Yi\nSZKksrQzMHnR5HHeLUB7YLultPtg0SQtQErpKWAGuat1iYh2wPbAhDptbwG2iIhvZdFXQ5lrS5Ik\ntS4VOYG8GFsCNcD0rAORJElSyfs+uRJq/5W/snZu/r0Gt8t7qVa7dYFl6tnuJXL5+foZ9dUc5tqS\nJEkVqlVMIEfEasDJwPUppY+zjkeSJEklrzMwp571s/PvNaddZyDVs91sIOps15J9NYm5tiRJUmVr\nm3UAxRYRy5C7pe8zcg/3WNx25VMMWpIkqYKklCLrGNQ0Dcm1zbMlSZKyUag8u+InkIEbgA2ALVNK\nny5pw3J6oKDKy6BBg7juuuuyDkMVyvGlYnJ8qdgiSnbueDawYj3rO+ffW1K7VZbSbtHVwXX771zr\n/Sz6aooG5drm2Somz1UqJseXisnxpWIqZJ5d0SUsIuJiYHdgj5TSa1nHo9ZrrbXWyjoEVTDHl4rJ\n8aVW7GXq1AaOiDXIPUSvvlrCi22XV7sG8XRgQT3bbQAsBF7NqK9GMddWqfBcpWJyfKmYHF8qFxU7\ngRwRJwG/AQ5MKf0j63gkSZJUVu4FekfECrXW9Sf3EL2HltJutYjYctGKiNgEWAe4ByClNB94ANi3\nTtv9gX+klD7Poq/GMNeWJElqPUq+hEVELA/0IXdr3urAtyJi7/zbk1JK8yLideCBlNIR+TYHAGcB\n1wLvR8Rmtbqc7sM91NI6deqUdQiqYI4vFZPjS63Y5cDRwJ0RcS6wLnAqMDqlVL1oo7p5aErp8YiY\nAlwfEcPIPeBuFPBwSumBWv2fATwQEWOAu4BdgZ2B3os2yKKviOgG9CKXe7cDNsrn3l+klO7Lb2Ou\nrZLiuUrF5PhSMTm+VC5KfgIZ6ALcRi7JXWRC/t+1gbfJXUld+2rqnfLbD8q/ajsEuL4IcUqL1bNn\nz6xDUAVzfKmYHF9qrVJKcyJiB+AS4G5gDjAaGFln07p5KMB+wBjgmvx7fwGOrdP/YxGxD3Am8Gtg\nBvCLlNLULPsCfkpuYnhR7r1P/vUWuauVwVxbJcZzlYrJ8aVicnypXIQPtMiJiOTvQpIkqWVFRMGe\nDq3SZJ4tSZLU8gqZZ1dsDWRJkiRJkiRJUvM4gSy1gAcffDDrEFTBHF8qJseXJKnUea5SMTm+VEyO\nL5ULJ5AlSZIkSZIkSfWyBnKetdkkSZJanjWQK595tiRJUsuzBrIkSZIkSZIkqeicQJZagHWNVEyO\nLxWT40uSVOo8V6mYHF8qJseXyoUTyJIkSZIkSZKkelkDOc/abJIkSS3PGsiVzzxbkiSp5VkDWZIk\nSZIkSZJUdE4gSy3AukYqJseXisnxJUkqdZ6rVEyOLxWT40vlwglkSZIkSZIkSVK9rIGcZ202SZKk\nlmcN5Mpnni1JktTyrIEsSZIkSZIkSSo6J5ClFmBdIxWT40vF5PiSJJU6z1UqJseXisnxpXLhBLIk\nSZIkSZIkqV7WQM6zNpukcvTWh29x5V+v5KyDz8o6FElqEmsgVz7zbEmSpJZXyDy7bSE6kSRlo9/F\n/Xh2mWfZ6smt6LNpn6zDkSRJkiRJFcYSFlILsK6RiuHRFx7luZrnWH/6+hx282FZh6MK5fFLklTq\nPFepmBxfKibHl8qFE8iSVKYOvvZgtl1uW0YfOJqP2n7EJX+5JOuQJEmSJElShbEGcp612SSVk5sf\nvJmD7jmId054h64rd+XIy47khldv4LPRn1FV5d8GJZUPayBXPvNsSZKkllfIPNtZBkkqQ0fddRT7\nrrovXVfuCsDYX46lhhqOv/b4jCOTJEmSJEmVxAlkqQVY10iFNOq2UXze5nP++Ns/Arnx1bZNW07b\n6jTGvjiW6i+rM45QlcTjlySp1HmuUjE5vlRMji+VCyeQJamMzF8wn5F/H8ngHoNpv1z7/3lv+D7D\n6VTTiUGXDMomOEmSJEmSVHGsgZxnbTZJ5eDQsYdy+/TbmXPhnHprHd/+yO3sN3E/3hz6Jt26dMsg\nQklqHGsgVz7zbEmSpJZnDWRJaoU+/vRjxr09jjG7jFnsg/L22WYf1qtZj/5j+7dwdJIkSZIkqRI5\ngSy1AOsaqRD6X9yf1ReuzmG9D/uf9XXH102H38TjCx7nqVeeasHoVKk8fkmSSp3nKhWT40vF5PhS\nuXACWZLKwLQ3pvG3uX/j+oHXL3XbXt17sfkym3PAVQe0QGSSJEmSJKmSWQM5z9pskkrZhsM3pH3b\n9jx99tMN2v7tmW+z1ui1mLDbBPbZZp8iRydJTWcN5Mpnni1JktTyrIEsSa3I3Y/fzcvxMrcedWuD\n23Tr0o1+q/TjyDuOLGJkkiRJkiSp0jmBLLUA6xqpOY649Qj26LwH63Zdt973Fze+rjvqOj6t+pTz\nbj+viNGp0nn8kiSVOs9VKibHl4rJ8aVy4QSyJJWw0XeMZlabWVx/9NJrH9fVYfkOHL3h0Zz22Gl8\nvfDrIkQnSZIkSZIqnTWQ86zNJqnUfL3wazoe35FfbfQrxhw+pkl91NTU0HFoRw5e/2AuO/KyAkco\nSc1nDeTKZ54tSZLU8qyBLEmtwFFXHEUVVYw+dHST+6iqquK8Hc7jqjeuYtZnswoYnSRJkiRJag2c\nQJZagHWN1FizPpvF1TOu5vwdz6eqasmH6qWNr9/s9htWXbgq/S/uX8AI1Vp4/JIklTrPVSomx5eK\nyfGlcuEEsiSVoAN+fwCrLVyNI3c9siD93TDgBu7/4n6mvTGtIP1JkiRJkqTWwRrIedZmk1QqXnr7\nJTa6dCMm7zuZnX6yU8H67XVyLz6b/xmvnP9KwfqUpOayBnLlM8+WJElqedZAlqQKtv+l+7NxbFzQ\nyWOA24+5ndeqXmPCwxMK2q8kSZIkSapcTiBLLcC6Rmqoe5+6lxd4gVt/c2uD2zR0fK256prs12U/\nfn3Hr6mpqWlihGptPH5Jkkqd5yoVk+NLxeT4Urko+QnkiFg3Iq6IiH9GxNcR8bcGtusYEddGxKyI\nmBMRN0bESsWOV5KaY9D4QezScRe6f7d7Ufq/7ujrmFs1l1NuOqUo/UtSJYmIDSJiakR8ERHvRsTI\niFjqbYANzUMjom9ETIuILyPi3xGxX9Z9RcSOETE+ImZERE1EjGjOzyhJkqTyV/I1kCNiD2As8DjQ\nA/gwpfSzBrSbDKwHDAUScB7wQUppu8Vsb202SZk6Z8I5jHhiBJ+c/gkdV+hYtM8585YzOf2p05l1\n5iw6LN+haJ8jSQ1RqjWQI6IT8G/gBXJ55LrAhcCFKaV6J1VrtV1qHhoRWwMPAJcAdwF9gOOB3iml\n+zPs6wKgN7ncuz9wfkrp9Kb8jLW2Nc+WJElqYYXMs0t+Arm2iLgNWHlpE8gRsQXwGLBNSumx/Lpe\nwBPAjimlb1zFbGIrKUvz5s+j04mdOO6Hx3HOwHOK/nmrHLcK235nW+4YfkfRP0uSlqSEJ5BPIjcJ\n2y2l9EV+3TDgVGC1lFL1Yto1KA/NT8C2SSntWKvtJOBbKaVts+qrzs/yETC27gRyY/syz5YkSWp5\nPkRv6XYmdwXEY4tWpJSeAmYAu2QWlVot6xppaQaNHcTyNctz1sFnNbptU8bXNftew12f3MX096Y3\nuq1aF49fasV2BiYvmjzOuwVoD9R7R1utdkvMQyOiHbA9UPepprcAW0TEt7LoqxHMtVVSPFepmBxf\nKibHl8pFpU4gfx94uZ71L+Xfk6SSMeP9GUyYOYGr9r6KqqqWOSz33bIvG7Ihe4/du0U+T5LK0Dfy\nyZTSO8BclpxPNiQPXRdYpp7tXiKXn6+fUV8NZa4tSZJUor5e+DWjbhtV0D4rdQK5MzCnnvWz8+9J\nLWr77bfPOgSVsH3G7kP31J19ttmnSe2bOr5u++1tTEvTuPepe5vUXq2Dxy+1Yk3NJxvSrjO5usF1\nt5sNRJ3tWrKvhjLXVknxXKVicnypmBxfKqS3PnyLfuf1o/3w9oz8+8iC9t22oL2VuUGDBrHWWmsB\n0KlTJ3r27PnfL/Oi2wpcdtlllwu5fO9T9/Lsm8/yxz3+yCIt+fm7dNyFA889kDuOuqMkfh8uu+xy\n5S8///zzzJmTm3t88803Uetgnu2yyy677LLLLrtcnOXbH7mdIX8YwjsfvUPHuR3Zae2dWGWZVbie\n6ymUSn2I3q3AKimlHeqsnwiklNLu9bTx4R4qmgcffPC/X3KptlWPW5VNvr0Jk343qcl9NGd8ffbF\nZ6x86sqcvcXZDNt7WJNjUOXy+KViK+GH6H0IXJJSOqPO+mrg1JTS6MW0W2oeGhEbAP8GtkspPVJr\nm02AJ4FeK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B6cueeZXoiVEWsgF4GJraT6zKmew6qnrsqQjYdwzsBzsg6nrNTU1PCdod9h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\n4YmbNR3dlE3HNrHzuZ3cFHaT0+GIiJeZ3HUyp984TcuFLVmZb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JO8Pb+a125O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\nEcqV3HmYnAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "\n", - "#Get the data\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(outputfile_1, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time, T, Q, r = np.loadtxt(outputfile_1, usecols=(4,5,6,7), unpack=True)\n", - "Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt(outputfile_1, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(2, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11,s11, c='black', label='direction 1')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time t (s)', size = 15)\n", - "plt.ylabel(r'temperature $\\theta$\\,(K)',size = 15)\n", - "plt.plot(time,T, c='black', label='temperature')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 3)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "plt.plot(time,Wm, c='black', label=r'$W_m$')\n", - "plt.plot(time,Wm_r, c='green', label=r'$W_m^r$')\n", - "plt.plot(time,Wm_ir, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time,Wm_d, c='red', label=r'$W_m^d$')\n", - "plt.legend(loc=3)\n", - "\n", - "ax = fig.add_subplot(2, 2, 4)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_t$',size = 15)\n", - "plt.plot(time,Wt, c='black', label=r'$W_t$')\n", - "plt.plot(time,Wt_r, c='green', label=r'$W_t^r$')\n", - "plt.plot(time,Wt_ir, c='blue', label=r'$W_t^{ir}$')\n", - "plt.legend(loc=3)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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zc4a78/LLL/PII4/Qp08f7r//frW0EBGRuLB06VK6dOnCcccdx4gR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58BHG3DKGsDDdLC8iIoWTfCCZ6164jm8839C1TFfeGfQOZaPKuh2W+KiBHPxUZ+dN50BERESc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\nAD86m5A62m2OAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "\n", - "#Get the data\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(outputfile_2, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time, T, Q, r = np.loadtxt(outputfile_2, usecols=(4,5,6,7), unpack=True)\n", - "Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt(outputfile_2, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(2, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{22}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{22}$\\,(MPa)', size = 15)\n", - "plt.plot(e22,s22, c='black', label='direction 1')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time t (s)', size = 15)\n", - "plt.ylabel(r'temperature $\\theta$\\,(K)',size = 15)\n", - "plt.plot(time,T, c='black', label='temperature')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 3)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "plt.plot(time,Wm, c='black', label=r'$W_m$')\n", - "plt.plot(time,Wm_r, c='green', label=r'$W_m^r$')\n", - "plt.plot(time,Wm_ir, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time,Wm_d, c='red', label=r'$W_m^d$')\n", - "plt.legend(loc=3)\n", - "\n", - "ax = fig.add_subplot(2, 2, 4)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_t$',size = 15)\n", - "plt.plot(time,Wt, c='black', label=r'$W_t$')\n", - "plt.plot(time,Wt_r, c='green', label=r'$W_t^r$')\n", - "plt.plot(time,Wt_ir, c='blue', label=r'$W_t^{ir}$')\n", - "plt.legend(loc=3)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Notebooks/Umats/Thermomechanical/Elasticity/ELORT.ipynb b/Notebooks/Umats/Thermomechanical/Elasticity/ELORT.ipynb deleted file mode 100644 index f3643bd15..000000000 --- a/Notebooks/Umats/Thermomechanical/Elasticity/ELORT.ipynb +++ /dev/null @@ -1,368 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Orthotropic linear elasticity" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import pylab\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import simcoon as sim\n", - "import math\n", - "import os\n", - "dir = os.path.dirname(os.path.realpath('__file__'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In thermoelastic orthotropic materials twelve parameters are required: \n", - "\n", - "1. The Young modulus of axis 1 $E_1$,\n", - "2. The Young modulus of axis 2 $E_2$,\n", - "3. The Young modulus of axis 3 $E_3$,\n", - "4. The Poisson ratio of direction 1 with respect to 2 $\\nu_{12}$,\n", - "5. The Poisson ratio of direction 1 with respect to 3 $\\nu_{13}$,\n", - "6. The Poisson ratio of direction 2 with respect to 3 $\\nu_{13}$,\n", - "7. The shear modulus of direction 1 with respect to 2 $G_{12}$,\n", - "8. The shear modulus of direction 1 with respect to 3 $G_{13}$,\n", - "9. The shear modulus of direction 2 with respect to 3 $G_{23}$,\n", - "10. The axial coefficient of thermal expansion in direction 1 $\\alpha_1$,\n", - "11. The axial coefficient of thermal expansion in direction 1 $\\alpha_2$,\n", - "12. The axial coefficient of thermal expansion in direction 1 $\\alpha_3$,\n", - "\n", - "The elastic stiffness tensor and the thermal expansion coefficients tensor are written in SMART+ formalism as\n", - "\n", - "$$\\boldsymbol{L}=\\left(\\begin{matrix} L_{1111} & L_{1122} & L_{1133} & 0 & 0 & 0 \\\\ L_{1122} & L_{2222} & L_{2233} & 0 & 0 & 0 \\\\ L_{1133} & L_{2233} & L_{3333} & 0 & 0 & 0 \\\\ 0 & 0 & 0 & L_{1212} & 0 & 0 \\\\ 0 & 0 & 0 & 0 & L_{1313} & 0 \\\\ 0 & 0 & 0 & 0 & 0 & L_{2323} \\end{matrix}\\right), \\quad \\boldsymbol{\\alpha}=\\left(\\begin{matrix} \\alpha_L & 0 & 0 \\\\ 0 & \\alpha_T & 0 \\\\ 0 & 0 & \\alpha_T \\end{matrix}\\right),$$\n", - "\n", - "where $$\\begin{array}{c}\\displaystyle{L_{1111}=E_1~(1-\\nu_{23}\\nu_{32})~\\Delta, \\quad L_{1122}=E_1~(\\nu_{21}+\\nu_{31}\\nu_{23} \\Delta, L_{1133} = E_1~(\\nu_{31}+\\nu_{21}\\nu_{32}} \\\\\n", - "\\displaystyle{L_{2222}= E_2~(1-\\nu_{13}\\nu_{31})~\\Delta, \\quad L_{2233}= E_2~(\\nu_{yz}+\\nu_{zx}\\nu_{yz} \\Delta, \\quad L_{2233} = E_3 (1-\\nu_{12}\\nu_{21})~\\Delta} \\\\\n", - "\\displaystyle{\\quad L_{1212}= G_{12}, \\quad \\quad L_{1313}= G_{13}, \\quad \\quad L_{2323}= G_{23},}\\\\\n", - "\\displaystyle{\\Delta= \\frac{1}{1-\\nu_{12}\\nu_{21}-\\nu_{23}\\nu_{32}-\\nu_{31}\\nu_{13}-2\\nu_{21}\\nu_{32}\\nu_{13}}}\\end{array}$$\n", - "\n", - "Also, all the Poisson's ratio are defined as:\n", - "$$\\begin{array}{c} \\nu_{21}= \\nu_{12} \\frac{E_2}{E_1} \\\\\n", - "\\nu_{32}= \\nu_{23}\\frac{E_3}{E_2} \\\\\n", - "\\nu_{31}= \\nu_{13}\\frac{E_3}{E_1} \\end{array}$$ \n", - "\n", - "\n", - "Details on the elastic stiffness tensor of transversely isotropic media can be found in Christensen (1979). For axis of transverse isotropy being 2 or 3, the above tensors are properly rotated. The tangent stiffness tensor in this case is $\\boldsymbol{L}^t=\\boldsymbol{L}$. Moreover, the increment of the elastic strain is given by \n", - "\n", - "$$\\Delta\\varepsilon^{\\textrm{el}}_{ij}=\\Delta\\varepsilon^{\\textrm{tot}}_{ij}-\\alpha_{ij}\\Delta T.$$\n", - "\n", - "In the 1D case only one component of stress is computed, through the relation \n", - "\n", - "$$\\sigma^{\\textrm{fin}}_{11}=\\sigma^{\\textrm{init}}_{11}+L_{1111}\\Delta\\varepsilon^{\\textrm{el}}_{11}.$$\n", - "\n", - "In the plane stress case only three components of stress are computed, through the relations \n", - "\n", - "$$\\left(\\begin{matrix} \\sigma^{\\textrm{fin}}_{11} \\\\ \\sigma^{\\textrm{fin}}_{22} \\\\ \\sigma^{\\textrm{fin}}_{12} \\end{matrix}\\right) =\\left(\\begin{matrix} \\sigma^{\\textrm{init}}_{11} \\\\ \\sigma^{\\textrm{init}}_{22} \\\\ \\sigma^{\\textrm{init}}_{12} \\end{matrix}\\right)+\\boldsymbol{K} \\left(\\begin{matrix} \\Delta\\varepsilon^{\\textrm{el}}_{11} \\\\ \\Delta\\varepsilon^{\\textrm{el}}_{22} \\\\ 2\\Delta\\varepsilon^{\\textrm{el}}_{12} \\end{matrix}\\right),$$\n", - "\n", - "with $$\\boldsymbol{K}=\\left(\\begin{matrix} \\displaystyle{L_{1111}-\\frac{L_{1133}L_{3311}}{L_{3333}}} & \\displaystyle{L_{1122}-\\frac{L_{1133}L_{3322}}{L_{3333}}} & \\displaystyle{L_{1112}-\\frac{L_{1133}L_{3312}}{L_{3333}}} \\\\ \\displaystyle{L_{2211}-\\frac{L_{2233}L_{3311}}{L_{3333}}} & \\displaystyle{L_{2222}-\\frac{L_{2233}L_{3322}}{L_{3333}}} & \\displaystyle{L_{2212}-\\frac{L_{2233}L_{3312}}{L_{3333}}} \\\\ \\displaystyle{L_{1211}-\\frac{L_{1233}L_{3311}}{L_{3333}}} & \\displaystyle{L_{1222}-\\frac{L_{1233}L_{3322}}{L_{3333}}} & \\displaystyle{L_{1212}-\\frac{L_{1233}L_{3312}}{L_{3333}}} \\end{matrix}\\right).$$\n", - "\n", - "In the generalized plane strain/3D analysis case the stress tensor is computed through the relation\n", - "\n", - "$$\\sigma^{\\textrm{fin}}_{ij}=\\sigma^{\\textrm{init}}_{ij}+L_{ijkl}~\\Delta\\varepsilon^{\\textrm{el}}_{kl}.$$" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "umat_name = 'ELORT' #This is the 5 character code for the elastic transversely isotropic subroutine\n", - "nstatev = 1 #The number of scalar variables required, only the initial temperature is stored here\n", - "\n", - "rho = 4.4\n", - "c_p = 0.656\n", - "E_1 = 4500\n", - "E_2 = 2300\n", - "E_3 = 2700\n", - "nu_12 = 0.06\n", - "nu_13 = 0.08\n", - "nu_23 = 0.3\n", - "G_12 = 2200\n", - "G_13 = 2100\n", - "G_23 = 2400\n", - "alpha_1 = 1.0E-5\n", - "alpha_2 = 2.5E-5\n", - "alpha_3 = 2.2E-5\n", - "\n", - "psi_rve = 0.\n", - "theta_rve = 0.\n", - "phi_rve = 0.\n", - "solver_type = 0\n", - "\n", - "props = np.array([rho, c_p, E_1, E_2, E_3, nu_12, nu_13, nu_23, G_12, G_13, G_23, alpha_1, alpha_2, alpha_3])\n", - "path_data = 'data'\n", - "path_results = 'results'\n", - "\n", - "pathfile = 'path_1.txt'\n", - "outputfile_1 = 'results_ELORT_1.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile_1)\n", - "outputfile_1 = dir + '/' + path_results + '/results_ELORT_1_global-0.txt'\n", - "\n", - "pathfile = 'path_2.txt'\n", - "outputfile_2 = 'results_ELORT_2.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile_2)\n", - "outputfile_2 = dir + '/' + path_results + '/results_ELORT_2_global-0.txt'\n", - "\n", - "pathfile = 'path_3.txt'\n", - "outputfile_3 = 'results_ELORT_3.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile_3)\n", - "outputfile_3 = dir + '/' + path_results + '/results_ELORT_3_global-0.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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79u2jX79+x73W0aNH6dWrFzfccANffvklU6ZMYcCAAXzyySelcbzyyiuMGTOGPXv20Lp1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\nm02AJ4FeK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B6cueeZXoiVEWsgF4GJraT6zKmew6qnrsqQjYdwzsBzsg6nrNTU1PCdod9h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\n4YmbNR3dlE3HNrHzuZ3cFHaT0+GIiJeZ3HUyp984TcuFLVmZb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JO8Pb+a125O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\nEcqV3HmYnAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "\n", - "#Get the data\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(outputfile_1, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time, T, Q, r = np.loadtxt(outputfile_1, usecols=(4,5,6,7), unpack=True)\n", - "Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt(outputfile_1, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(2, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11,s11, c='black', label='direction 1')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time t (s)', size = 15)\n", - "plt.ylabel(r'temperature $\\theta$\\,(K)',size = 15)\n", - "plt.plot(time,T, c='black', label='temperature')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 3)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "plt.plot(time,Wm, c='black', label=r'$W_m$')\n", - "plt.plot(time,Wm_r, c='green', label=r'$W_m^r$')\n", - "plt.plot(time,Wm_ir, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time,Wm_d, c='red', label=r'$W_m^d$')\n", - "plt.legend(loc=3)\n", - "\n", - "ax = fig.add_subplot(2, 2, 4)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_t$',size = 15)\n", - "plt.plot(time,Wt, c='black', label=r'$W_t$')\n", - "plt.plot(time,Wt_r, c='green', label=r'$W_t^r$')\n", - "plt.plot(time,Wt_ir, c='blue', label=r'$W_t^{ir}$')\n", - "plt.legend(loc=3)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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Ttm1b4uLi1NIihOl7VYJJ40uCSeMrNG3atInq1avz3//+l9WrV9O2bVuvQ/Jc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\nRliY2VjgHeBLQk9fPgdIcfffzOxndz82oinDf/dZwLvAJcA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8VgM37+O9a4C3ijGLSExK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BAAAg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ssujRuty5efzPvvcsT6x7gnKecrxz2zt0urSTK3GIiIh/yjyRSd+pfXnr57eo76nPvAHzuLDO\nhW6HJTloCIvApzo7bzoHIiIi4hSNgewAFba5c+P4V2xawW0zb+NQ6CEevfhRxtw6hpAQ3SwvIiKF\nk3QwietfvJ5vPN/QpUwX3h30LmUjyrodlmRRAznwqc7Om86BiIiIOEVjIEtA2rlvJ5eMuISO73bk\nwsgLOfTEIZ66/amAaB5rXCNxkvJLnBQI+RVTJYYNz2xgyQ1L+ObwN0Q9HsWImSPweDxuhyYiIkUg\nEL6rxHcpv8RJyi/xF/7fmRO/l3osleueu47zXzyfPz1/svnezSwbtYzypcu7HZqIiASQzs06c2DS\nAZ687EkmfTuJqMFRzPp8ltthiYiIiIiI+DQNYZEl2B6te/LJJ7nooovo3r37Gddz8vg9Hg/DZgzj\npe9fIvJEJNN7Tadry66OfJaIiEh2aelp9Jnah3f3v0uNzBrMuH0GHf7Rwe2wgpKGsAh8wVRnf/XV\nVwC0atUqX+sH4jkQERER36AxkB0QTIUtwIgRIxg8eDCVKlU643pOHL/H42H8vPE8/eXTePAwps0Y\nhl0/rEg/Q0REJD9++fUXbptyG58f+5zGtjFz7pujifaKmRrIgS/Y6uyC0DkQERERp2gMZAEgKSmJ\nrl27UqZMGebOnQvArFmzCA8PZ9SoUSQnJwMwePBg4uLiWL9+PVOmTGHp0qVs2bLlrM1jJ7yy6BUi\nB0fy+NrH6de4H6kvpAZF81jjGomTlF/ipEDPr8oVKrNs1DK+v/97wkLDaPJqE64YfQVJB5PcDk1E\nXFbQWjshIYERI0ZgrWX16tX07t2b6dOnM2XKFDcPIygE+neVuEv5JU5Sfom/UAPZj8XExHDDDTdQ\nqVIlevXqBUCPHj0IDQ2lb9++REZGAnD55ZezdOlSkpKSqFatGm3atKFu3brFGuucVXOo/HBlHlz1\nID3q9SD12VQm9Z1EidASxRqHiIhIbhrUbMDGZzYSf1s8+4/tp86kOsSNieOnX35yOzQRcUlBa+26\ndeuSmpqKMYZLLrmE8PBw+vXrxwMPPODmYYiIiIicQ2bAygAAF9lJREFUMzWQ/VzFihVPe/3OO+8Q\nHR3N4cOHAdi9ezc1atSgZMmSrFy5kvbt2/PVV1/RsmVLDhw44Hh876x4hxqDa3Db4tu4ovoVJD+R\nzBsPvkF4WLjjn+1LYmNj3Q5BApjyS5wUbPnV5sI2/PDCD3zW6zOSfk+i9sTadHyyI3sP7XU7NBFx\nQUFq7fT0dGrXrs2+ffvIyMhw5Wm/YBVs31VSvJRf4iTll/gLNZD9XFRU1Km/f/nllzRp0oTKlSuf\nKmrXrFlzahKPLl26sGzZMrZs2cL+/fspW7asY3G9/unrVHm4Cnd8egctqrTg4MiDfDDsA8pGOPeZ\nIiIiRaXDPzqw84WdLLp+ETt/20mtF2rxz6f+qTuSRYJMQWrtQ4cOERERgTGGjRs30r59e1diFhER\nESlqaiCfK2OK5qeQTt4VkZ6eTkJCAq1atSIqKorDhw+zYsUK4uLiTq3btWtXbrrpJh5++GGGDBlC\nqVKlzvnwc3pxwYtEDYrivuX3ERcdx5HHjzD/kflULFfx7BsHMI1rJE5SfomTgj2/rmx+JYkTEll4\n/UJ+TPmR2hNr02pUKzbv2ux2aCJBweVSu0C1dvPmzRkwYADVqlWjU6dOdOjQ4VwPX/Ip2L+rxFnK\nL3GS8kv8hRrI58raovkppIoVK2KtZcqUKfTp0wfw3imRlJRESkoK1apVK6ojzVN6RjrDZwyn7KCy\nDI0fSte6XUl5MoU5Q+ZQvnR5xz9fRETEaVe1uIpdE3YRf1s8xzKO0XR6Uxo/0pgVm1a4HZpIQHO5\n1C5QrT1p0iQOHjx4rocsIiIi4nPUQPZz5cqVIyUlhQoVKpx6xK5ixYosXbqU6667ztHPPvzbYW6a\ncBMRj0bw8uaX6XdRP449d4wZD82g9HmlHf1sf6NxjcRJyi9xkvLrdG0ubMPm5zaz5d4tVPhbBTq+\n25GYITHMWDbD7dBExAEFqbV37NhBlSpV3Agz6Om7Spyk/BInKb/EX5RwOwA5d3Fxcdx1112nXler\nVo2xY8cSEvL/vx9YvXo1H3/8Mddddx3Lli2jdevWJCcnU7JkSXr27Fmgz9u8azMPzHyAhLQEqqZX\n5aWOL3Hvlfee9nkiIiKBrHHtxiQ8mcDeQ3vp/3p/+i3rx0OLH+Luxnfz7O3PUiq86IeJEhF35KfW\nTkhIICkpibVr19KyZUs3whQRERFxjDp+AWDevHmnvR40aBBt2rQ5bdkFF1xAamoqbdu2JS0tjRYt\nWhAdHc2RI0fy9Rkej4eXF75MzJAYmk5vypG0IyzpuYR9k/bR/+r+ah6fhcY1Eicpv8RJyq8zi64U\nzScjPuHoM0fpf3F/Zm6dSdlRZenydBd+2PuD2+GJSBHIT61ds2ZNYmNj1Tx2ib6rxEnKL3GS8kv8\nhbp+QSI9PZ369esDkJqaSmRkJEuWLKFFixakpaWdcdvbJt9GxJAIBq0cxKVVLiVxUCJbx22lc7PO\nxRG6iIiIzwsPC2f8XeNJmZzCW1e/xc6UnTSY2oD6Q+vz6uJX8Xg8bocoIg5as2YNrVu3Zs+ePW6H\nIiIiIlLk1EAOEhs2bCAuLo7MzEwqVaoEQIkSJUhJSSE8PPyM2y5PWs7oy0dzfPxxPhz+ITFVYooj\n5ICicY3EScovcZLyq+Bujr2ZH174ga39t9IwqiEPLn+QUkNLccPzN5C4P9Ht8ETEAREREezfv1+/\nLHKJvqvEScovcZLyS/yFsecyLXEAMcbY3M6FMYZgPkfBfvwiIiLnyuPxMHnBZF5MeJGfwn6iXkY9\nHmjzAA9c+wAlQjUdRVatYdyOQ5yjOjtvOgciIiLilKKss3UHskgx0LhG4iTllzhJ+XXuQkJCGNx9\nMEkTkvhf///RMKohI+JHEP5oOK0fb83i9YvdDlFExK/pu0qcpPwSJym/xF+ogSwiIiJSTBrVasTC\nEQv5Y+IfzL52Nn9m/sm1H1xL6YdL0/P5nnyX+J3bIYqIiIiIiJxGQ1hk0aN1uQv24xcREXFaWnoa\nL3z4Aq9//TpJYUmU/6M8nWt25l/d/0WTuk3cDs9xGsIi8KnOzpvOgYiIiDilKOtsNZCzqLDNXbAf\nv4iISHFKOZrC+A/HM+fbOSSFJVH2j7J0rNGRYdcM47JGl7kdniPUQA58qrPzpnMgIiIiTtEYyCJ+\nRuMaiZOUX+Ik5VfxKl+6PM/c8QyJExJJeSyFBy99kP8e/C+tZrbivIfPo/XjrXltyWukZ6S7HaqI\niM/Qd5U4SfklTlJ+ib9QA1lERETEB5WNKMtTtz/Fzhd2kvZUGhM7TsRay8DPBhI+Kpx6Q+sxYNoA\nNu/a7HaoIiIiIiISwHx+CAtjTCNgCtASSAFeB57I9Tm407crC7wIdMPbKF8IPGStTc5jfT1al4tg\nP34RERFftGbrGv796b+J3xvPgfADhP0ZRoOwBlz996u5r8t91Kpcy+0Q883fh7BwulY1xnQDngLO\nB3YBY6y17/nDvrKtqzo7DzoHIiIi4pSgGQPZGFMe2Ap8B4wH6gETgYnW2sfPsu1SoD4wBLBZ2x+w\n1rbLY/1cC9vatWuTlJR0Lofh12JiYti9e7fbYYiIiEge0jPSmbNqDu+uf5f1h9aTHJFMqeOlqP+3\n+rSr246b29zMZQ0vIyTENx888+cGstO1qjGmDbASb4P6I+AqYCjQ2Vr7ua/vK9v6qrPzoFpbRERE\nnBJMDeQReIvRWtbaY1nLhgGjgarW2qN5bNcKWAO0tdauyVrWHFgHdLTWrshlm7PdKOI4j8fD8v8u\nZ/7X8/kq6St2/rGT3yN+J+yPMOqWqEtc3TjuaHdHwE6iE8hWrVpFbGys22FIgFJ+iZOUX/4n9Vgq\nc1bP4ZNvP+GbX77hYNhBsFAtsxpNKzWl3QXt6NGqB/Wq13M7VMDvG8iO1qpZzdxQa23HbNsuAspY\na6/w5X3lON486+yUoykMemMQ7+5+F2MNt9e/nYl3T6T0eaVzXV8kN/quEicpv8RJyi9xUlHW2SWK\nYicO6gIsPVmQZ3kXGAe0AxadYbsDJwtfAGvt18aYROBK4C8N5OKWdDCJRV8vImFHAlsObuGnP3/i\n9/N+JyQzhKonqnJR1EXc3uJ2bmxzI9GVot0OV87Rpk2b9KUgjlF+iZOUX/6nbERZ7r3qXu696l7A\n+wvq+C3xzP1yLl8mfcnTXzzN8I3DCc0IpVJmJRqWb0jL2i1p26gtsU1iKRVeyuUj8CuO1arGmDAg\nFngwx7bvAm8YY8pYa3/34X2d0fY92xkwYwCrjq4iKiOKJ1s/ydAeQ332TnnxbfquEicpv8RJyi/x\nF77eQG4ILM++wFq7xxhzPOu9vIryhsC2XJZ/n/Vesfjl11+I/y6e9T+uZ+v+rexK2cXB9IOklkzl\nRNgJIo5HUKNEDf5e8e/cUf8Orrr0KhrXblxc4UkxSklJcTsECWDKL3GS8sv/hYSEEHtxLLEXx55a\nlnkik9WbV7Pom0Ws3b2WNza9wYTNE8j4JIOw42FEeiKJiYihUeVGXBh9IZfWu5RmFzTTXaF/5WSt\nWg8omct63+Mdm/gCYKMP7ytXS75ewpD3h7AtdBvnZ57Ph90/pNvl3fJaXSRf9F0lTlJ+iZOUX+Iv\nfL2BXAHvZCQ5/Zr1XmG2q1PYYDweD4d/O0zigUT2HN7D3iN72ffrPn5O+Zk9KXs4cPwAyZnJHDPH\n+DPsT2wJy9+O/40KtgLVz6vOxVUu5qLoi2jdsDVtLmxDiVBfP/0iIiISiEqElqDDPzrQ4R8dTlue\ncjSFld+uJGFbAt/+/C3xP8Uzf9d8jn55lBOlThDyRwgRGRFEhkYSGRZJpYhKVCtbjVqRtahTuQ71\nqtajWmQ1qkVVC5Zms5O1agW8YxDnXO9XwGTbv6/u6y8S9ydy7XvX0jqiNe/1fo8L61yY16oiIiIi\n4kPUwczhzpfuZMGuBWSYDE5wghMhJ/CEePCEerAlLVgI+TOEsBNh/M3zN0qZUpQtUZaqEVVpWaMl\n9SrXo2GNhjSp04Tza5yvJrEAaHIUcZTyS5yk/Aou5UuXp3vr7nRv3f0v76Wlp7Hxh418s/Mbvt/3\nPftS9nHg6AESf0tkSeISjnKUP0P/xFPy/2smk2EIzQylxIkShBBCiA3h5D+hhBJiNFxBMKlTrQ7H\nnzlOWMkwt0ORAKPvKnGS8kucpPwSf+Hr3c1fgXK5LK+Q9d6ZtqtY0O2Myd+40h48pGX98xu/sZ/9\nbGd7vraV4DVz5ky3Q5AApvwSJym/pLAslsysfwKUk7XqyTt6c+6/Qrb3fXlfp8lvnS1SWPquEicp\nv8RJyi/xB77eQN5GjjGLjTHRQClyH58t+3Z9c1neEJif2wb+Ovu3iIiIiLjGyVp1J5CRteyLbOs0\nAk4AO3x8X6eozhYRERHxb77+3OASoLMxJiLbspuA48Dqs2xX1Rhz+ckFxphmQF1gsROBioiIiEjQ\ncaxWtdamAyuBnjm27QV8Za393cf3JSIiIiIBwlhr3Y4hT8aY8sDWrJ9xeGd9ngBMtNaOzrbej8BK\na22/bMs+BeoDw/BO9PEccMBaG1tsByAiIiIiAcvpWtUY0xpvs3Yq8BFwNTAY6GytXe7r+xIRERGR\nwODTDWQAY0xDYArQCu9sz9OBMTZb4MaYXXiL8j7ZlpUFJgHd8d5p/Qkw0FqbXIzhi4iIiEgAc7pW\nNcZ0BZ4GzgcSgdHW2vdzrOOT+xIRERGRwODrQ1hgrd1mre1orY2w1taw1j5hc3S9rbV1sxfkWctS\ns5a1BjYCPYAtxpgxJh+zeBhjyhpjZhhjko0xKcaYt40xkUV5bOL/jDGNjDHLjTHHjDE/5ye/jDEx\nxhhPLj+ziytu8Q/GmHrGmFeNMd8aYzKNMSvyuZ2uX3JWhckvXb8kv4wxPY0xC4wxe40xvxtjNhhj\nbsrHdn53/TrXWtVaG2mtLW+tvT23Gx2stR9ba5tYa8+z1v49tyatm/sCLgH2A2/mtxYC//x3LcVP\ntbY4SbW2OEm1tjjFrTrb1yfROyfG+1jh58B3QFe8jxVOxDtz9ONn2fx9vI/v3Y338b3xeCcPaedU\nvOJfzjG/wPuo55fZXh8u6hjF7zUGugBrKdj1WtcvyY/C5hfo+iVn9zCwCxiENz+uAmYbY6KstVPP\nsJ2uX35EtbY4SbW2FAPV2uIk1driFFfqbJ8fwuJcGGNGAEOBWtbaY1nLhgGjgarW2qN5bNcKWAO0\ntdauyVrWHFgHdLTW5us3kxLYziG/YvA+6nmNtVaTOkq+GGPeB6KstXFnWU/XLymwAuSXrl+SL8aY\nyFyGPHgHaGmtrZfHNrp++RnV2uIk1dpSnFRri5NUa0tRcqvO9vkhLM5RF2DpyYIjy7tAKc7cYe+C\nd7KQNScXWGu/xvsf8pVOBCp+qbD5JeIkXb9ExHV5zDnxX6D6GTbT9cv/qNYWJ6nWFl+k65eIuMqt\nOjvQG8gNgW3ZF1hr9wDHs97L93ZZvj/LdhJcCptfJ83IGgtpnzFmgjEm3IkgJejo+iXFQdcvKYzL\ngR1neF/XL/+jWlucpFpbfJGuX1IcdP2SgnK8zg7oMZCBCnhnw87p16z3CrNdnSKISwJDYfPrT7yz\ntS8DUoFY4FGgLt4Zz0XOha5f4iRdv6RQjDEdgG7AnWdYTdcv/6NaW5ykWlt8ka5f4iRdv6TAiqvO\nDvQGsojPsdYeAB7KtijeGPMLMNUYc5G1dotLoYmInJGuX1IYxpjawDvAfGvtW+5GIyKBTt9VIuKv\ndP2SgirOOjvQh7D4FSiXy/IKWe8V9XYSXIoyTz7AO6P0pecalAQ9Xb+kuOn6JXkyxlQAluAdX+22\ns6yu65f/Ua0tTlKtLb5I1y8pbrp+Sa6Ku84O9AbyNnKM5WGMicY78UJuY3/kuV2WvMYMkeBU2PzK\njc3xp0hh6folxU3XL8mVMeY8YBEQinc28bSzbKLrl/9RrS1OUq0tvkjXLyluun7JX7hRZwd6A3kJ\n0NkYE5Ft2U14J15YfZbtqhpjLj+5wBjTDO+4M4udCFT8UmHzKzc98X4hbCyi2CR46folxU3XL/kL\nY0wo3jtm6gFdrLVH8rGZrl/+R7W2OEm1tvgiXb+kuOn6Jadxq8421gbuLzGMMeWBrVk/4/Ce3AnA\nRGvt6Gzr/QistNb2y7bsU6A+MAzvf6zPAQestbHFdgDi0wqbX8aY0UAZYA3egfHbAUOBhdbaG4v1\nIMSnZf1W8Sq8jywNxps3T2S9vcham6brlxRWYfJL1y/JL2PMa0BfvOP4fZ3j7W+stRm6fvk/1dri\nJNXa4jTV2uIk1driFLfq7ICeRM9am5I1G+EU4GO8Mw5OAMbkWDWEv96NfSMwCfhP1nufAAMdDVj8\nyjnk1zZgCNAHOA/4CW9R/IzTMYvfqQy8z+mPK72X9WcdvLmj65cUVmHyS9cvya9OeHPrxVze0/Ur\nQKjWFiep1pZioFpbnKRaW5ziSp0d0Hcgi4iIiIiIiIiIiEjhBfoYyCIiIiIiIiIiIiJSSGogi4iI\niIiIiIiIiEiu1EAWERERERERERERkVypgSwiIiIiIiIiIiIiuVIDWURERERERERERERypQayiIiI\niIiIiIiIiORKDWQRERERERERERERyZUayCIiRcwY09MY0zuX5SuNMe+5EVOOOGoYY1KNMXXyuf6l\nxpgjxpgyTscmIiIiInImqrVFRIqfsda6HYOISEAxxrwPRFlr43IsbwhkWGt3uhPZqTheAcpaa28t\nwDafAV9Ya590LjIRERERkTNTrS0iUvx0B7KISDGx1m7zgYK2DHAH8J8CbvomcJ8xRt8bIiIiIuJz\nVGuLiDhHFycRkSJkjJkBXA+0M8Z4jDEnjDGPZ723KvtjdcaYJ4wxh4wxLYwxXxtjjhtjvjDGxBhj\nKhlj5htjfjfG/M8Y0z6Xz+prjPnOGJNmjNltjBmWjxB7AceBlTn2NcIY84Mx5g9jzAFjzGJjTOVs\nq3wMRAGdC35WRERERETOnWptERF3lHA7ABGRAPMkUAsoB/QHDLA3672cYwZZoBTwKjAeOAa8BLwN\n/AksBqYCjwDvGWNqWmvTALIK2LHAc8Bq4FLgKWPMMWvty2eILw5Yb7ONX2SMuQN4FBgO/A9v8RoH\nRJwK1NrfjTFbgY7AkgKcDxERERGRoqJaW0TEBWogi4gUIWttojEmGe8Y81/nY5Nw4EFrbQJ4J93A\nW8iOstZOzFr2M7AVaAcszXo07nHgSWvt01n7WW6MiQAeM8a8YvMe4P5S4KMcy5oDy6y1r2ZblnMd\ngG+BFvk4JhERERGRIqdaW0TEHRrCQkTEXeknC9osP+K9W2JljmUANbL+vBzv3RQfGGNCT/5kbVMV\niD7D51UFDudYtgm4Ousxv+ZnGHvtcNb2IiIiIiL+QLW2iEgRUANZRMRdv+d4nZ71Z8rJBdbajKy/\nhmf9GYX3cb3/ARnZflbgLYhrnuHzwvE+spfdG8AIoCewFjhojHnKGGNyrPdnthhERERERHydam0R\nkSKgISxERPxPctafVwG/5PL+9rNsWz77gqxH8F4EXsx6rO9W4BlgD/BatlXLZ/tsEREREZFApFpb\nRCQHNZBFRIpeOs7ePfAV3tmda1hrPy3gttuBOnm9aa39GRhvjLkb+HuOt2sDOwr4eSIiIiIiRUm1\ntohIMVMDWUSk6G0DuhpjuuGdFXqftXZ/AbbP+Tjbaay1vxljxgAvGWNqA/F4hyRqAMRaa3ucYfM1\nwLWnfZgx0/De7bAW+A3vrND1geU5tm2GdyZqERERERG3qNYWESlmGgNZRKTovQwsA/4DrAf6FXD7\n3GZ1ttmXW2ufz9pvF7yzOM8GbsZb4J7Jh8DfjTHZJ//4CmiLd3y2RUA3oK+19pOTKxhj/gFUzNpe\nRERERMQtqrVFRIqZ8Q7HIyIiwcIY81/gbWvthAJs8yxwqbX2n85FJiIiIiLi31Rri0ggUgNZRCTI\nGGNuAMYD9a21nnysXwpIAnpYa79wOj4REREREX+lWltEApHGQBYRCTLW2g+MMXWAGnhnfz6bWsAY\nFbQiIiIiImemWltEApHuQBYRERERERERERGRXGkSPRERERERERERERHJlRrIIiIiIiIiIiIiIpIr\nNZBFREREREREREREJFdqIIuIiIiIiIiIiIhIrtRAFhEREREREREREZFc/R/GVrrzFdGo2gAAAABJ\nRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "\n", - "#Get the data\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(outputfile_2, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time, T, Q, r = np.loadtxt(outputfile_2, usecols=(4,5,6,7), unpack=True)\n", - "Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt(outputfile_2, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(2, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{22}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{22}$\\,(MPa)', size = 15)\n", - "plt.plot(e22,s22, c='black', label='direction 2')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time t (s)', size = 15)\n", - "plt.ylabel(r'temperature $\\theta$\\,(K)',size = 15)\n", - "plt.plot(time,T, c='black', label='temperature')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 3)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "plt.plot(time,Wm, c='black', label=r'$W_m$')\n", - "plt.plot(time,Wm_r, c='green', label=r'$W_m^r$')\n", - "plt.plot(time,Wm_ir, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time,Wm_d, c='red', label=r'$W_m^d$')\n", - "plt.legend(loc=3)\n", - "\n", - "ax = fig.add_subplot(2, 2, 4)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_t$',size = 15)\n", - "plt.plot(time,Wt, c='black', label=r'$W_t$')\n", - "plt.plot(time,Wt_r, c='green', label=r'$W_t^r$')\n", - "plt.plot(time,Wt_ir, c='blue', label=r'$W_t^{ir}$')\n", - "plt.legend(loc=3)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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PH8+8efP4/PPPATj77LNZsmQJGzdupGPHjnTo0IFdu3bRsmVLHnzwQa677jo2b97MwoUL\ni3zPBX92mDVrFitWrODdd99l3bp1tG7dep/vP0z63JV40vySeNL8kmShAvJBMrOY/Apqzpw5ZGdn\nc9ddd1GmTBnat29PkyZN9vmcu+++m+rVq1O+fHnmzZvHTz/9RK9evShTpgzHHXccXbp0YdSoUQC8\n+OKLPPLII5x44okA1KtXj4yMjLxruXuhr/HJJ5+wdetW/vKXv1C2bFkuvvhiWrduzWuvvZY35uqr\nr6ZRo0akpaVx4403smjRon3GvXjxYjZv3syrr75K8+bNDyg/IiIisbRt2zZatWpFWloa/fr14+yz\nz2bDhg3s2bOHunXrhh2eSMoKa62dK3fNO3LkSFq1akXLli0BuPTSS2ncuDFTpkzJG3vrrbdy3HHH\nccQRR3DFFVdQp04dLr74YtLS0ujQocOvircPPPAAFStWpEaNGtxzzz156+WhQ4fy17/+lZNPPpm0\ntDQeeOABFi1axOrVq/Oe++CDD1KxYkXKly8PQMeOHalUqRJpaWn88Y9/ZOfOnfz73/8u9vs2M7Ky\nsqhQoQLly5c/oPcvIiIiqats2AEku6IKqfG2bt06jj322L3O1a5de5/PyX8Dn5UrV7J27VoqV64M\nRN5HTk4OF1xwAQCrV6/mhBNOCBzXd999t1c7i9y41q5dm3d89NFH5/05PT2dLVu27Pe6hxxyCNdd\ndx1169alQYMG1KtXL3BsYVJfo2CUr2CUr2CUr2BKe75WrVpFkyZN+OGHHwDo3r07AwcOpEyZMkU+\np7TnTCSWwlprF7Ry5UpGjx6d15rN3cnOzs5r7wBw1FFH5f25QoUKvzouuObNvzavXbs269aty3ut\nu+++m3vvvTfvtcyMtWvX5q2zC96Y86mnnmLEiBF89913AGzevJmffvrpoN5zwZ8dCnv/l1xyyUG9\nRqzoc1fiSfNL4knzS5KFCshJ6phjjtmrKAuRH3JzdwwXJv/ui5o1a3LCCScUuTOhVq1afP3114F3\nVVWvXn2v3RG5cZ1yyimBrlOU3bt388033yRdAVlERJLLrFmzuPDCC/OOhw0bRpcuXUKMSERKWsG1\n8y233MLQoUNjdv3Vq1dz2mmnAZECbfXq1fNeq3fv3r9qW1FUbLNnz+bJJ59k+vTpeWv3ypUr5xXf\nC9uBfdhhh7Ft27a84//+97/7fI14vH8RERFJHmphkaTOPfdcypYty8CBA8nOzmbs2LF8+umnB/z8\ns88+myOOOIInnniCHTt2sGfPHpYvX878+fMB6Ny5Mw899BBfffUVEOl5vHHjRiCyg/ibb74p9Lrn\nnHMO6enpPPHEE2RnZzNjxgwmTZq0zwVwUebOnctHH33E7t272bFjB48//jg//PAD55xzTuBrhU19\njYJRvoJRvoJRvoIpbfkaPHgwZpZXPJ49ezbuHqh4XNpyJpKq8q95b7rpJiZOnMh7771HTk4OO3bs\nYObMmXm7hovjySefZNOmTaxevZpnn32W66+/HoBu3brx6KOP5vU3/vnnn3njjTeKvM7mzZspV64c\nVapUYdeuXfTr14/NmzfnPX7UUUfx7bff7rWbu0GDBowaNYrs7Gzmz5//q+sX3Pkdj/cfS/rclXjS\n/JJ40vySZKECcpIqV64cY8eO5aWXXqJKlSqMGTOG9u3bFzm+4M6DtLQ0Jk2axKJFizj++OOpVq0a\nXbt25ZdffgHgT3/6E9deey2//e1vqVixIl26dGH79u0A9OnTh1tuuYXKlSv/arFZrlw5Jk6cyJQp\nU6hatSo9e/bkX//6FyeddFKhcezLzp07ufPOO6latSo1atTgnXfeYcqUKXu1wBARETlY2dnZdO3a\nFTOjR48e1KhRg9WrV+Pu6r0vUoo98MAD9O/fn8qVKzN69GjGjx/Po48+ym9+8xtq167NU089RU5O\nDhBsjZurXbt2NGrUiLPOOos2bdpw2223AXDVVVfxwAMPcP3111OpUiXq16/PO++8k/e8gq/VsmVL\nWrZsycknn8zxxx9Penr6Xi3lOnTogLtTpUoVGjduDED//v356quvqFy5MllZWdx44417XbPga9So\nUWOf719ERERSmyVKX7GwmZkXlgszS5jea7J/+vsSEZEDtWHDBlq0aJF3Y6u2bdsyatQoKlSoEHJk\npUv03+7i3+VMEp7W2b+WlpbGV199Vax7jiSL0vz3KyIikghiuc7WDmQREREpVZYtW4aZUaVKFRYu\nXEi/fv3Iyclh/PjxKh6LiIiIiIgUoAKySAlQX6NglK9glK9glK9gUilfY8eOxczybsQ6fvx43J2H\nHnqoWF8/L0oq5UxE4iOWnzmiz12JL80viSfNL0kWZcMOQERERCRe3J2+ffvSr18/IPK18aVLl1K3\nbt2QIxOR0mzPnj1hhyAiIiJywNQDOUq92VKD/r5ERARg+/btdOjQgcmTJwPQuHFj3nvvPTIyMkKO\nTApSD+TUp3V26aS/XxERkXCpB7KIiIhIIVavXs0xxxxDeno6kydPplu3bmRnZzNv3jwVj0VERERE\nRIpBBWSREqC+RsEoX8EoX8EoX8EkS74+/PBDzIxatWrx3//+lxdeeAF3Z/DgwZQpU6ZEY0mWnImI\npAp97ko8aX5JPGl+SbJQD+T9qF27tm5ykURq164ddggiIlKChgwZQvfu3fOOZ8+eTfPmzUOMSEQO\nlNbZqU3rchERkdShHshRRfVmExERkcSSnZ1N9+7dGT58OADHHnssc+bMoUaNGiFHJsWhHsipT+ts\nERERkZIXy3W2diCLiIhIUtiwYQOXXXYZCxYsAKBt27aMGjWKChUqhByZiIiIiIhI6lIPZCkW9ekJ\nRvkKRvkKRvkKRvkKJhHytWzZMsyMKlWqsGDBArKyssjJyWH8+PEJWTxOhJyJiJQm+tyVeNL8knjS\n/JJkoR3IIiIikpDGjRvH1Vdfvddxu3btQoxIRERERESk9FEP5Cj1ZhMREQmfu9O3b1/69esHQFpa\nGkuXLqVu3bohRybxoh7IqU/rbBEREZGSpx7IIiIiklK2b99Ohw4dmDx5MgCNGzfmvffeIyMjI+TI\nRERERERESjf1QJZiUZ+eYJSvYJSvYJSvYJSvYOKdr9WrV3PMMceQnp7O5MmT6datG9nZ2cybNy9p\ni8eaYyIiJUufuxJPml8ST5pfkiy0A1lERERK3OzZszn//PPzjocOHcrtt98eYkQiIiIiIiJSGPVA\njlJvNhERkfgbOnQo3bp1yzuePXs2zZs3DzEiCZt6IKc+rbNFRERESp56IIuIiEjSyM7OpkePHgwb\nNgyA6tWrM2fOHGrWrBlyZCIiIiIiIrI/6oEsxaI+PcEoX8EoX8EoX8EoX8EcTL42bNhAo0aNKFeu\nHMOGDaNt27Zs27aNtWvXpnTxWHNMRKRk6XNX4knzS+JJ80uShQrIIiIiElPLly/HzKhSpQoLFiwg\nKyuLnJwcxo8fT4UKFcIOT0RERERERAJQD+Qo9WYTERE5OOPGjePqq6/e67hdu3YhRiTJQD2QU5/W\n2SIiIiIlL5brbO1AFhERkWJzd/r27YuZ5RWPly1bhrureCwiIiIiIpICkrKAbGbXm9lnZrbZzNaY\n2T/M7JhCxj1oZqvMbJuZzTSzM8OINxWpT08wylcwylcwylcwylcwReVr+/bttGnThrS0NLKysmjc\nuDHr16/H3Tn99NNLNsgEozkmicDMTjOzaWa21czWmlmWme1zB4qZ1TWzt6Pjd5jZSjMbZmZHFzK2\nnZktMbPtZrbczK6N37sR2Td97ko8aX5JPGl+SbJIugKymbUFXgVmA22B+4ELgEkFxv0V6AU8BrQG\ntgBTzaxaiQYsIiKSQlavXs0xxxxDeno6kyZN4o477iA7O5t58+ZRuXLlsMMTEcDMKgFTgWwi6+Us\n4N7o7/tSEfgmOva3QCbQAphsZnk/N5jZecAbwDTgciLr8NfMrEVs34mIiIiIJIKk64FsZq8BJ7p7\nk3zn2gDjgLru/m8zKw98Dzzp7o9Ex6QD3wJD3D2zkOuqN5uIiEgRZs+ezfnnn593PHToUG6//fYQ\nI5JUoR7IsRfdSPFnoJa7b42euw/oAxzt7lsCXKsF8C7QyN0XRc+9C5Rx9xb5xk0GjnD3Cwq5htbZ\nIiIiIiWstPdALgf8XOBc7nFuUpoDRwBjcge4+zZgInBFvAMUERFJFUOHDsXM8orHs2fPxt1VPBZJ\nbJcD7+YWj6NGAenAhQGvtSH6+yEAZnYIcBEwusC4UcC5ZnZE4GhFREREJKElYwF5BHC+md1sZkeY\n2clAf2Cau6+IjjkF2AN8WeC5XwCnllyoqUt9eoJRvoJRvoJRvoJRvvYvOzubO+64AzOjW7duVK9e\nnVWrVuHuNG/ePOzwEp7mmCSAU4EV+U+4+2pgGwewFraIcmZ2CpF2cJ+6+6fRh+sQ2dCxosDTviDy\ns8XJBxm7SGD63JV40vySeNL8kmSRdAVkd58C3Aq8QGTn8Qoi7+N3+YZlAFsK+a7cRiDdzMqWRKwi\nIiLJZMOGDTRq1Ihy5crxwgsv0KZNG9555x3Wrl1LzZo1ww5PRA5cBrCpkPMbo4/tzxRgJ5GicAbQ\npsC1vZDrbyTybcADub6IiIiIJJFk7IF8MTAeeA54BzgK6Euk5/Gl7u5m9iDwZ3evXOC5nYkUnsu7\ne3aBx9SbTURESqXly5dzxhln5B337duXzMxMzNSWVuJPPZBjz8x2EVkLP1vg/GrgH+7eez/PrwNU\nBk4CehPZudzM3XeZWTPgQ6Chuy8p8Jwvgd+6+9QC19M6W0RERKQETZs2jRYtWsRsnZ2MO3GfAsa5\n+4O5J8xsMZGdyO2I3ExvI3C4/Xq1mgFsK1g8ztWpUyeOO+44ACpVqkSDBg246KKLgP99rUDHOtax\njnWs41Q5HjduHFdffTW53nrrLSpVqgSQVzxOpHh1nBrHixYtYtOmyObVb7/9FomLjUDFQs5nRB/b\nJ3f/GvgamGdms4H/AB2Bl/nfTuOC18/deVzo9bXO1rGOdaxjHetYxzqO7/GiRYv4/vvvef/99/n8\n88+JpWTcgbwVyHT3p4s6H92lPBU41d2/zDdmOHCmuzcp5LraGRHAjBkz8iap7J/yFYzyFYzyFUxp\nz5e7k5WVRVZWVt65ZcuWcfrppxc6vrTnqziUs2C0Azn2zGwmsMbdb8x3rgawCmjj7pMDXu9HYLC7\nZ0ZvorcZ6Onuw/KNuZnIvUoqu/vmAs93d2fnzp08/PDD/PnPf6ZixcLq2yLFo89diSfNL4knzS+J\npalTp9KlSxcuvfRS/va3v1GpUqWYrbPTYnGRErYSOCv/CTM7DagAfBs99TGRhW2HfGPSifRvm1Ii\nUYqIiCSQ7du306ZNG9LS0sjKyqJRo0asX78edy+yeCwiSettoKWZHZbv3PVEWlHMDHKh6I30qgDf\nALj7LmA6+dbZUdcBnxQsHueXnZ3Njz/+SL169Xj33XeDhCEiIiIiRdi8eTPdunXjtttuY8iQIbz4\n4osx/8/6ZNyBfBfwN+DvRBbHRwMPEWnHUc/dt0fHPUCkZ9v9RNpb3As0AU539x8Lua52IIuISMpZ\nvXo1TZs2Zd26dQDcfvvtPP/885Qtm4xdrCQVaQdy7JlZJWB59NfjQB3gaeBv7t4n37ivgOnu3jV6\n/CSQDcwlcpO8usB9wC6gQb51dnMiReTnibSPawX8CWjp7tMKiWevdXbu7pjf/va3PPfccxxyyCEx\nzoCIiIhI6fDJJ59www035O06zl84juU6O+kKyABmdgfQnchieBORG3k86O7fFhj31+i4KsA84K78\nN/soMFYFZBERSRmzZ8/m/PPPzzseMmQId9xxR4gRiRROBeT4MLNTidx0+lwi6+VhQFb+Ba+ZfUOk\ngNw5enwd0BM4DTiUSMuLScAAd99Q4PptgYeJ3GjvP0Afdx9TRCy/Wmf/8ssv3HzzzZgZo0ePVhFZ\nREREJKDZs2dzzTXXMHz4cNq2bfurx2O5zk7GFha4+1B3b+DuR7h7TXfvWLB4HB33mLvXcvfD3P2i\noorHElxus245MMpXMMpXMMpXMKmerxdeeAEzyysef/jhh7h7sYvHqZ6veFDOJBG4+wp3bxFdBx/r\n7n0LVnEtMT2gAAAgAElEQVTd/YTc4nH0+HV3P9/dq7r74e5e193vL1g8jo6d4O713b1CdFyhxeOi\nHHnkkYwZE3nKtddey65du4r5TkX0uSvxpfkl8aT5JcWVWzx+5ZVXCi0ex1pSFpBFRETkf7Kzs7nj\njjswM+644w6qV6/OqlWrcHfOO++8sMMTESnUIYccwujRowHo0KEDmzcX2T5ZRERERKLef//9vOLx\nZZddViKvmZQtLOJBLSxERCTZbNy4kcsuu4zPPvsMgNatWzN69GgqVKgQcmQiB04tLFLf/tbZu3bt\nomfPnrz//vsMHz6cSy+9tASjExEREUkOW7Zs4YEHHmDcuHH861//4uKLL97n+FLfwkJERKQ0W758\nOWZG5cqV+eyzz+jbty85OTlMnDhRxWMRSTqHHHIIL7zwAoMHD+bWW2+le/fu2o0sIiIiks+MGTOo\nX78+mzdvZunSpfstHseaCshSLOrTE4zyFYzyFYzyFUwy52v8+PGYGWeccQYAY8eOxd3p06cPZvHZ\nwJnM+QqLciZSPJdffjlLly5l165d1KtXj2nTpoUdkiQJfe5KPGl+STxpfsn+bNmyhZ49e3LTTTfx\n7LPP8o9//IOMjIwSj0MFZBERkQTm7mRlZWFmXHXVVQAsW7YMd+fqq68OOToRkdiqWLEiL774IkOG\nDNFuZBERESnVCu46bt26dWixqAdylHogi4hIItm+fTvXXnstkyZNAqBRo0a89957VK5cOeTIRGJL\nPZBTX3HX2T///DN/+tOfmDZtGi+++KJ6I4uIiEipkL/X8ZAhQ4pdOFYPZBERkRS1Zs0ajj32WNLT\n05k0aRK33347u3fvZv78+Soei0ipot3IIiIiUtok0q7j/FRAlmJRn55glK9glK9glK9gEjVfH330\nEWZGzZo1WbduHYMHD8bdGTp0KGXLlg0trkTNVyJTzkRiS72RZX/0uSvxpPkl8aT5JbkSpddxUVRA\nFhERCdGwYcMwM8477zwAZs2ahbvTrVu3kCMTEUkcBXcjd+vWje3bt4cdloiIiMhBmz17dkLuOs5P\nPZCj1ANZRERKyp49e7jzzjsZOnQoAEcffTRz586lVq1aIUcmUvLUAzn1xXqd/fPPP9O1a1c2btzI\n+PHjSU9Pj9m1RURERErSe++9x0033cSLL75ImzZtYnrtWK6zVUCOUgFZRETibePGjfz2t79l/vz5\nALRu3ZrRo0dToUKFkCMTCY8KyKkvHuvsPXv28Pvf/57vv/9eRWQRERFJSrnF47feeovmzZvH/Pq6\niZ6ETn16glG+glG+glG+ggkjX59//jlmRuXKlZk/fz59+vQhJyeHiRMnJnzxWPMrOOVMJP7KlCnD\nP/7xD4466ijatWvHli1bwg5JQqTPXYknzS+JJ82v0uvtt9+Oa/E41lRAFhERiZMJEyZgZpx++ukA\njB07Fnenb9++mGnDpYjIwcgtItepU4f69evrh3ARERFJeNu2beOee+6hc+fOjBs3LimKx6AWFnnU\nwkJERGLB3enfvz99+vTJO7d06VLOOOOMEKMSSVxqYZH6SmKdPXnyZO644w6uuuoqBgwYwOGHHx7X\n1xMREREJ6sMPP+S2227jnHPO4dlnn6Vy5cpxfT21sBAREUkw27dvp127dqSlpdGnTx/OOuss1q9f\nj7ureCwiEmetWrVi6dKlbNmyRbuRRUREJKHk7jq+7rrreOqppxg5cmTci8expgKyFIsW5cEoX8Eo\nX8EoX8HEOl9r1qzh2GOPJT09nQkTJnD77beze/duPvvss6RbFBRG8ys45UwkHBkZGbz88ssMHDiQ\nm266iZ49e6o3cimhz12JJ80viSfNr9T34YcfcuaZZ/LTTz+xbNky2rVrF3ZIxaICsoiISDF89NFH\nmBk1a9Zk3bp1DB48GHdn6NChlC1bNuzwRERKLe1GFhERkbClwq7j/NQDOUo9kEVE5EAMGzaM22+/\nPe941qxZnH/++SFGJJLc1AM59YW5zs7fG/nxxx/nsMMOCyUOERERKT1mz57NrbfeWmK9josSy3W2\nCshRKiCLiEhR9uzZQ8+ePRkyZAgARx99NHPnzqVWrVohRyaS/FRATn1hr7M3btxIjx49WL16NW+/\n/TZHHHFEaLGIiIhIahs/fjy33347L7zwQujtKnQTPQmdvgoYjPIVjPIVjPIVTJB8bdy4kSZNmlC2\nbFmGDBlCq1at2Lp1K999912pKR5rfgWnnIkkloyMDF555RVOP/10rrjiCjZv3hx2SBJj+tyVeNL8\nknjS/EotucXjKVOmhF48jjUVkEVERAr4/PPPSUtLo3LlysyfP5/MzExycnKYNGkS6enpYYcnIiIB\npaWlMXjwYBWRRUREJC7GjRuXVzxu1KhR2OHEnFpYRIX91ToREQnfhAkT9vqf4rFjx3L11VeHGJFI\n6lMLi9SXSOvsnJwc7rrrLt555x1eeukl9bAXERGRg7J9+3Z69+7Na6+9xsSJExOqeKwWFiIiIjHi\n7vTv3x8zyyseL126FHdX8VhEJMWkpaXx3HPP8fTTT3Pddddx9913s3Xr1rDDEhERkST08ccf06BB\nA9atW8eSJUsSqngcayogS7GoT08wylcwylcwylcwufnavn077dq1Iy0tjczMTBo2bMj69etxd844\n44xwg0wgml/BKWciia9du3YsW7aM9evXc+aZZ/Lhhx+GHZIcBH3uSjxpfkk8aX4lp+3bt3PvvffS\nvn17HnvsMV577TWqVq0adlhxpQKyiIiUKj/++CM1a9YkPT2dCRMm0KVLF3bv3s2CBQuoXLly2OGJ\niEgJqVy5MiNHjtRuZBERETlg+XcdL126lGuuuSbskEqEeiBHJVJvNhERib2PP/6Y5s2b5x0PHjyY\nbt26hRiRiIB6IJcGybDO3rBhA3fddRdz5sxRb2QRERH5ldxex6+++irPP/98UhSO1QNZRETkAA0f\nPhwzyysez5w5E3dX8VhERPJoN7KIiIgUpbTuOs5PBWQpFvXpCUb5Ckb5Ckb5+rU9e/bQvXt3zIyu\nXbty9NFHs3LlStydnJycsMNLKppfwSlnIslLvZGTkz53JZ40vySeNL8SW2nsdVwUFZBFRCRlbNq0\nibPPPpuyZcsyZMgQWrVqxdatW/nuu++oVatW2OGJiEgS0G5kERER0a7jvakHclQy9GYTEZHCffHF\nF5xxxhl5u4szMzPp27cvZmqrKpLo1AM59SXzOnvDhg307NmTFStWMHXqVN1sVUREpBQYOXIkf/7z\nnxk0aFBSF45juc5WATkqmRe2IiKl1YQJE2jXrl3e8ZtvvpnU/8CLlEYqIIOZ1QPOBo4GDgU2AP8P\n+NjdN4YZWywk+zrb3bnvvvv44IMPVEQWERFJcSNHjuT+++9n6tSp1K1bN+xwDopuoiehU5+eYJSv\nYJSvYEpbvtyd/v37Y2Z5xeMlS5bg7gdUPC5t+TpYyldwypkcCDM7wcyeNLN1wCJgCHAPcCvQH5gI\n/Ghm08zsBjPTuj0kZsaTTz7JJZdcQosWLdiwYUPYIUkB+tyVeNL8knjS/EosqVQ8jjUtREVEJCns\n2LGDdu3akZaWRmZmJg0bNmT9+vW4O/Xq1Qs7PBGRA2Zmw4HlQAOgH9AQONTdf+PuNdz9cKAa0AZY\nBjwBfGFm54UVc2mXW0Ru0aIFTZs25aOPPgo7JBEREYmRHTt28Je//IW//OUvKh4XQS0sopL9q3Ui\nIqlq7dq1NG3alDVr1gDQpUsXBg8eTNmyZUOOTERioTS2sDCzgcBT7r7yAMenAR0A3P31eMYWD6m2\nzh47dix33nknHTt2pH///qSnp4cdkoiIiBTT3Llz6dSpE6effjqDBg2iWrVqYYcUM2phISIiKe+T\nTz7BzKhRowZr1qxh0KBBuDvDhg1T8VhEkt2EAykem1k5M3vN3XPc/fVkLB6nomuuuYalS5eybt06\nGjRooN3IIiIiSSh313G7du3IysrijTfeSKnicaypgCzFoj49wShfwShfwaRavoYPH46Z0axZMwBm\nzpyJu9O9e/eYXD/V8hVvyldwypkcgPFmdsW+BpjZYcAUQHcGTUBVq1bltddeY8CAAfzud7/j3nvv\nZdu2bWGHVWrpc1fiSfNL4knzKxxz586lYcOGfP311yxZsoRrr7027JASXqACspnVM7POZtbLzPqb\n2R/NrJWZZcQrQBERSX179uyhe/fumBldu3blqKOOYuXKlbg7F1xwQdjhiYjE2lvAW2Z2VWEPmllV\nYAbQDBWQE5p2I4uIiCQP7Touvv32QDazE4DuwI3AUUAOsAnYCVQC0qPnZgLDgdfdPSeOMcdFqvVm\nExFJBps2baJly5Z8+umnAFx55ZWMGTNG/SRFSpFS2gPZiKybbwJudvfR+R47DngXqAq0dvdPwogx\nlkrLOlu9kUVERBJXKvc6LkqJ9UAOeIfopZTQHaLNrIyZPWBm/8/MdpjZajN7upBxD5rZKjPbZmYz\nzezMeMYlIiIH5osvvqBMmTJkZGTw6aefkpmZSU5ODpMnT9YP3CKS8jyiM5Ei8itmdguAmdUHPiKy\nQeP8gykem9lpZjbNzLaa2Vozy4oWrvf1nMZmNsLMvow+b4WZZZpZ+QLjXjKznAK/9pjZycWNNxVo\nN7KIiEji0a7j2NhfC4vtwKnufpm7D3H3Je6+J/8Ad//J3d9293uA2kAmcGyc4s31D6AnkYL1ZcBf\norHmMbO/Ar2Ax4DWwBZgqplplsSA+vQEo3wFo3wFk0z5mjhxImZG3bp1ycnJ4c0338TdycrKYj91\njZhJpnwlAuUrOOVMDpS73wkMBEaY2VPALOBnoJm7f17c65pZJWAqkA20BbKAe6O/78t1wAnAAOAK\n4DngT8DIQsZ+AZwDNI3+Ohf4trgxpwr1Rg6HPnclnjS/JJ40v+JLvY5jZ5+3sXf3PwS5WLR1RVzv\nDm1mlwMdgPru/u8ixpQnUlR+1N0HR8/NIbKo7UmkyC0iIiXA3Xn44YfJzPzfR++SJUuoV69eiFGJ\niCQGd/+TmW0D/grMBVq5+8aDvGx34FDgGnffCkwzs4pAHzN7wt23FPG8x9x9Q77jWWa2ExhiZjXd\nfXW+x7a6+7yDjDNlXXPNNVxwwQX07NmTs88+m6lTp3L00UeHHZaIiEipMWTIEPr27cuzzz6rwnEM\n7LcHcqIxs9eBI929yDtXm9nFRHZdnObu/y/f+ReJFJ6bFPKcUtGbTUSkpOzYsYMbbriBcePGAdCg\nQQOmTp1KlSpVQo5MRBJJKe2B/CNQcOFZlcju490Fx7t7oG/QmdlMYK27d8x3riawEmjj7pMDXKsx\nkcJ2M3efGz33EnC6u599gNco1evsfv36MWrUKD744AMVkUVERErA4MGDGTBgANOnT+eEE04IO5zQ\nxHKdvc8dyAnqHGC8mQ0EbiHyHt4Berr7d9ExpwJ7gC8LPPcLQP/tICISR2vXrqVp06asWbMGgC5d\nujB48GDKlk3Gf3JEROLieX5dQI6lU4Fp+U+4++roTudTgQMuIAPNiNww++sC5+ua2c9AeWAe0Mvd\nZxU/5NSV+w2cSy65REVkERGROFPxOD721wN5L2Z2nZlNjd6Y7oeCv+IVZAFHA7cCZxIpBncCGgFj\n843JALYUstVhI5BuZqpiHCT16QlG+QpG+QomUfL1ySefYGbUqFGDNWvW8Pzzz+PuDBs2LKGKx4mS\nr2ShfAWnnMn+uHtfd8860F/FeIkMYFMh5zdGHzsgZnY0kXuK/NPdf8r30AIiPZVbAx2J/EzxfnS3\nshQiMzOTG264gfPPP585c+aEHU7K0eeuxJPml8ST5lfs7Ny5kwcffFDF4zg54J/ozawjMAJ4Gbgk\n+uc0Ijfm2AT8Mw7xFRpK9Pe27r4pGtt/gZlmdrG7Ty+hOEREBHjxxRfp0qVL3vHMmTO54IILQoxI\nREQOlpmVA0YDvxC5kV4edx9YYOzbwHLgQeCakoox2Tz00EOccsopXHXVVfz+978nKyuLQw89NOyw\nREREkt78+fPp1KkTJ554InPnztW3feIgyJaw+4D+RO7KfDswyN0XmNkRwPtASd1eeCPwdW7xOGo2\nsAuoC0yPjjncft1wLQPY5u7ZhV24U6dOHHfccQBUqlSJBg0acNFFFwH/+18hHUeOc88lSjyJfpx7\nLlHiSfTj3HOJEk+iH+eeK8nX37NnD2PHjmXQoEFA5DNz0aJF1K5dmxkzZiT031/uuUSJJ9GPc88l\nSjzJcpwrUeJJpONFixaxaVNkGfftt99SGpnZzcCr7r4nwHNOBI5x9w8PYPhGoGIh5zOijx2IfwGn\nEel9/PO+Brr7djObQmRHcqG0zo4cX3vttZQrV46///3vNGzYkJdffpnt27cnTHzJfJwrUeLRcWod\n50qUeHScWse5EiWeZDretWsXM2fOZPjw4XTt2pVLL700r3icCPGV9HE819kHfBM9M9sCtHb3GWa2\nG7jM3WdEH7sa+D93Py6m0RUex3SgvLs3y3fOgB3APe4+ON9N9E519y/zjRsOnKmb6ImIFM+mTZto\n2bIln376KQBXXnklY8aMIT09PeTIRCRZldKb6C0kUsz9F/CGuy8uYlwV4HLgeuBi4DZ3H30A158J\nrHH3G/OdqwGs4gBuomdmzwBdgBbu/skBvqeBRH5WOL6Qx7TOLsTo0aO56667tBtZRESkGPLvOh4y\nZIh2HRciluvstABjfyFykwyAtUR2JOTFBFSJRUAHYBJQz8wq5zt3IZHd1LmL74+BzUCHvADN0oE2\nwJQSijOlFfyfMtk35SsY5SuYksjXihUrKFu2LBkZGXz66af07t2bnJwcJk+enHTFY82vYJSv4JQz\n2R93bwj8hUhReKGZ/WJmc81sspmNNbMPzOw/wA/AM0RuYHfKgRSPo94GWprZYfnOXU/kG4Mz9/VE\nM/sr0AO4MUDxuALQCph/gPEJcO2117JkyRK+/vprGjZsyNy5c8MOKWnpc1fiSfNL4knzK7idO3fS\nq1cvWrVqxYMPPshbb72l4nEJCNLCYh5QH3gXmABkmlk2kdYRmUBJ3Q3iBeAPwCQzexQ4kkhbjffd\n/WMAd99pZgOA3ma2CVhB5EYfBjxXQnGKiCS9iRMn0rZt27zjN954g/bt24cYkYhIanD314HXzawO\n0AI4i8jNog8DvgdmAR8BM9x9d8DLDyGyXn7LzB4H6gB9gKfdfUvuIDP7Cpju7l2jxx2BR4CXgO/M\n7Jx81/za3X8ysyOJbOgYCXwF/Ab4I3AM8GjAOEu9atWq8cYbbzB69GjatWun3cgiIiL7kH/X8eLF\ni1U4LkFBWlg0BWq7++tmVgn4B5GdBmlEiss3uPs3cYt071hOAJ4lsvN4FzAO+FPB/mzRHRTdieyO\nngfc5e5LirimvlonIgK4O48++ii9e/fOO7dkyRLq1asXYlQikqpKYwuLkmBmpxLZOHEukRteDwOy\n8i94zewbIgXkztHjl4Bbirjkre7+TzMrD7wCNAGqEWkj9zHQ193nFRGL1tkH4IcffqBHjx4sX76c\nl19+mXPOOWf/TxIRESkFdu7cSb9+/Rg+fDj/93//xw033ECkm63sSyzX2QdcQC4ikPJE+hH/Eotg\nwqSFrYiUdjt27OCGG25g3LhxADRo0ICpU6dSpUpJdSgSkdJIBeTUp3V2MOqNLCIi8j/qdVx8JdoD\n2cwqmFl7M7vXzDqa2VG5j7n7zlQoHktw6tMTjPIVjPIVzMHma+3atdSqVYsKFSowbtw4brvtNnbv\n3s3ChQtTsnis+RWM8hWcciaxYmYXRHcSSymSvzfyWWedxcqVK8MOKeHpc1fiSfNL4knza9/+9re/\nqddxgthnD+Roq4ipwHH5Tv9iZte6+3vxDExEROLrk08+oVmzZnnHzz//PD169AgxIhERKWAG4GY2\nA/ibu08ONxwpKbm9kf/+979z8cUXM336dGrXrh12WCIiIiXm8ccf58UXX+Szzz6jRo0aYYdT6u2z\nhYWZvQE0AH4PfAYcDwwCjnP340skwhKir9aJSGkxYsQIOnfunHc8Y8YMLrzwwhAjEpHSTC0simZm\nFwLpQFOgqbu3DDmkYtE6++A888wzPPPMMyoii4hIqZFbPJ4+fTrHHnts2OEkrRLrgWxma4F73X1U\nvnMnA18ANdz9u1gEkQi0sBWRVLZnzx7uuusuBg0aBER2Nn366af6QVREQqcCcurTOvvg5RaRR48e\nTePGjcMOR0REJC527dpFnz59ePPNN1U8joGS7IF8DPBNgXNfAwao8Ugppj49wShfwShfwewrX5s2\nbaJp06aULVuWQYMGccUVV7B161a+//77Uls81vwKRvkKTjmTWDCzw83sajMbbmbrwo5HwnX33Xfz\n2GOP0apVK3r16sXOnTvDDimh6HNX4knzS+JJ8+t/FixYQOPGjVm2bBmzZs1S8TjB7PcmeoC2C4iI\nJJkVK1ZQrlw5MjIymDt3Lr179yYnJ4cpU6aQnp4edngiIlIIMzvJzO4xs/eBn4AhRO5Zcne4kUki\nuO6661i8eDGff/45jRo1Yv78+WGHJCIictB27dpFZmYml19+Offddx8TJkzQzfIS0P5aWOQAm4Ds\nAg9VLey8u1eLdYAlRV+tE5FUMHHiRNq2bZt3/MYbb9C+ffsQIxIR2bfS3MLCzMoBFwKtor9OABYB\nk6O/5qXCAlXr7Nhyd0aNGsU999xDly5dyMzMpHz58mGHJSIiEtiCBQvo1KkTtWvXZujQoVSvXj3s\nkFJKSfZA7hPkYu6eddARhUQLWxFJVu7Oo48+Su/evfPOLV68mPr164cYlYjIgSmNBWQz60ykYHwZ\nkW/7vU+kYDzF3f8bZmzxoHV2fPz3v/+le/fufPXVV7z00kvqjSwiIklj165dPPLIIwwePJinnnqK\nm2++GbNStRwsESVWQC5NtLANZsaMGVx00UVhh5E0lK9glK8Ds2PHDjp27Mhbb70FQIMGDZg6dSpV\nqlQJObLEpvkVjPIVnHIWTCktIP+b/+0ynuXuu0MOKa60zo4fd+e1117jj3/8Y6nejazPXYknzS+J\np9I4vxYuXEinTp2oWbMmL7zwgnYdx1FJ3kRPREQSzLp166hduzYVKlTgrbfe4oorrmD37t0sXLhQ\nxWMRkSTg7qcAzwE3AvPM7Aszm2FmT5vZxSGHJ0nEzOjYsSOLFy9m+fLlNGrUiK+++irssERERAr1\nxBNP0LJlS+69914mTpyo4nES2V8Li8wgF3P3fgcdUUi0M0JEEt0nn3xCs2bN8o6fe+457rzzzhAj\nEhE5eKVxBzKAmQ0FXgW2AjWAhsAtwFHAWqCvu78SXoSxo3V2yXB3Bg8ezIABA/jggw848cQTww5J\nREQkT9++fRkzZgzvvfcexx57bNjhlAol2QM5B9hOZGG7vxd03URPRCT2RowYQefOnfOOZ8yYwYUX\nXhhiRCIisVOKC8gPuPuAAuceA7KAK4EuwC7gOnffGUKIMaN1dskaNmwY/fv3VxFZREQSRm7xePr0\n6VSrlrSlw6RTki0svgbKAZ8BfwZOcPffFPFLM6AUmTFjRtghJBXlKxjlC/bs2cOdd96JmdG5c2d+\n85vf8O233+LuvyoeK1/BKF/BKF/BKWdygOab2QtmdlK+c+7uO9x9rLtfCQwCAt3UWqRr16489NBD\nXHLJJSxcuDDscEqEPnclnjS/JJ5SfX7t3r2b+++/X8XjFLDPArK7nwQ0A5YD/YHvzWysmXUwswol\nEaCISGmyadMmmjZtStmyZRk0aBCXX345W7Zs4YcffqB27dphhyciIjHi7lOBl4BJZjbbzO4DqhUY\n8x7wQxjxSXLr2rUrTz75JC1btqRPnz7s2rUr7JBERKSUWbJkCeeccw5Lly5V8TgF7LOFxa8Gm10A\nXA+0B9KBCcBQd58Vn/BKjr5aJyJhWrFiBfXq1SM7OxuA3r17069fP8xK3be6RaSUKa0tLHKZWTki\nvY+7AE2AzcAiYB1wKLDM3ZN6F7LW2eFZt24dd9xxB6tWreLll1+mYcOGYYckIiIpbvfu3QwYMICB\nAwfy+OOP06lTJ/1cG5IS64G8jwAOAR4B/ghMcPdrYhFMmLSwFZEwTJ48mdatW+cdjxkzht/97nch\nRiQiUrJKewE5PzOrSORmekcB24gUj/8TblQHT+vscLk7I0eO5N5776V79+706tWLQw45JOywREQk\nBS1ZsoROnTpx1FFHMWzYMGrUqBF2SKVaSfZALvjCzc1sILAS6A68ATwTi0AkuaR6n55YU76CSfV8\nuTuPPPIIZpZXPF68eDHuXqzicarnK9aUr2CUr+CUMykud//Z3We4++vuPjEViscSPjPj5ptvZtGi\nRSxYsIAmTZqkXG9kfe5KPGl+STylyvzavXs3/fv3p0WLFvzhD39gypQpKh6nmLL7G2BmZxFpW3Ed\nkd0Q7/C/ncfb4hueiEjq2LFjBzfeeCNjx44FoH79+nzwwQdUqVIl5MhEREQk1VWvXp0JEyYwcuRI\nWrZsqd3IIiISE/l3HS9YsECF4xS1zxYWZvZv4HjgA2AUMNbdfymh2EqUvlonIvGybt06zj33XFat\nWgXAbbfdxtChQylbdr//hycikvLUwiL1aZ2deNQbWUREDpZ6HSe+EuuBbGY5wA5gK7DfVZ+7J+0t\nFbWwFZFYmzNnDueee27e8cCBA+nZs2eIEYmIJB4VkFOf1tmJSb2RRUSkuNTrODmUZA/kLOBx4Dng\n+QP4JaVEqvTpKSnKVzDJnq+XXnoJM8srHk+fPh13j1vx+P+zd+9xWs75H8dfn84khbL4WSJEqJCK\nFh0VspaUkraDxVoiSkWIrS2lUsruOlSjk6icis4nUjpnsYqRsKTEdkLNNvP5/XHPjDGmmmu677nu\nw/v5eNyP5ntd933Np4/LNd8+870+V6Lnq7gpX8EoX8EpZyKSCArqjfzvf/877LCKRNddiSWdXxJL\nibLrLhEAACAASURBVHZ+uTsDBgxQr+MUdMD7p9390eIKREQkkWVmZtKtWzdGjRoFQOXKlVm5ciVV\nq1YNNzAREUkIZlYDuAD4LTDG3b8xs9OALe6+K9zoJJnl9EYePXo0TZo0Ye7cuZxzzjlhhyUiInHG\n3bn33nt5++231es4BR2shUUHYJK7Zxb6gJGJ7vHu/nYU4is2urVORIpi+/btXHHFFbz77rsAtGjR\ngqlTp1K+fPmQIxMRSQyp3sLCzI4AxgCtgH1EFnhc6O5rzOwl4At37xFmjIdK8+zEMXnyZO655x4V\nkUVE5BdyisdLlixhzpw5HHXUUWGHJIVQnC0s7gU+NbN+ZlbrAAEdY2btzWw6sA44PhrBiYjEq/Xr\n11O6dGmOOuoo3n33Xfr06UNWVhYzZ85U8VhERIIYBlwMNAUqAHkn+W8CLcIISlJT27ZteeKJJ2jW\nrBlr1qwJOxwREYkD+/bt4+6771bxOMUdsIDs7ucBvYBGwFoz22lmy83sDTN72cwWmNlnwFZgBPAp\nUN3dX4p55BKqROvTEzblK5h4ztcbb7yBmXHWWWexb98+pkyZgrvTv3//0J44G8/5ikfKVzDKV3DK\nmQR0HdDL3RcC+e/6+xw4ufhDklTWtm1bRo0aRYsWLejXrx//+9//wg7poHTdlVjS+SWxFO/n14cf\nfshFF13E+vXrVTxOcQdbgYy7v+juvwNOB+4jssJ4H1Ae2AI8T2RlxPHu3s3dv4phvCIixS7nQQFm\nRsuWLQF47733cHeuv/76kKMTEZEEdxjw3X72VeDXRWWRmGvVqhVr1qxh6dKl1KtXj3/9619hhyQi\nIsVo3759DBw4kIYNG3Lrrbcye/ZsFY9T3AF7IKcS9WYTkfz27NlD+/btefnllwGoWbMm8+fPp3Ll\nyiFHJiKSPNQD2RYBX7v7jWZWEvgfUCe7B/I4oLK7XxlqkIdI8+zE5e6kpaXRq1cvunbtSu/evSld\nunTYYYmISAx9+OGHdOrUiaOOOornnnuOk046KeyQpIiKsweyiEjK+frrr6latSqHHXYYL7/8Mp07\ndyYjI4P33ntPxWMREYm2h4DrzGwe8CfAgSvNbDzQGugbZnCS2syMzp07azWyiEgKKGjVsYrHkiMq\nBWQzu9TMzozGsSQxxHufnnijfAUTVr7effddzIz/+7//4/PPP2fkyJG4O2PGjInr1TY6v4JRvoJR\nvoJTziQId38baAKUBUYReYjeo8CpQFN3XxlieCIAnHjiibz55pt07dqVpk2bxl1vZF13JZZ0fkks\nxcv5ldPreOHChaxevZpbbrkltGf8SHyK1grkRcCHZjbfzK6K0jFFRIrF2LFjMTMuuugiABYuXIi7\nc+edd4YcmYiIJDMzK21mDYDP3P0S4EjgRKCCuzdw93fCjVDkZ1qNLCKSfLTqWAorKj2Qzewy4HCg\nPlDf3Zsf8kGLmXqziaSWzMxMunXrxqhRowCoXLkyK1eupGrVquEGJiKSYlK5B7KZlQB+Aq5w9wVh\nxxMrmmcnH/VGFhFJfOp1nPzirgeyuy9295nu3jcRi8cikjp27NjBxRdfTKlSpRg1ahTNmzdn9+7d\nfPvttyoei4hIsXL3LOAT4LiwYxEJoqDVyB988EHYYYmISCFkZWVp1bEEdsgFZDM7wsyuNbPnzOzr\naAQl8S9e+vQkCuUrmFjka8OGDZQtW5ZKlSqxbNkyHnjgAbKyspg1axbly5eP+vcrTjq/glG+glG+\nglPOJKA+wMNmdm7YgYgEldMb+c4776Rx48asWLEilDh03ZVY0vklsVTc51dWVha33HILM2bMUK9j\nCaRUUT5kZqcDV2W/LgF2ADOBu6MXmojIoXvzzTe56qqfW7O/9NJLtG7dOsSIREREfuFB4BhgnZl9\nBWwBftHvwd3rhhGYSGGYGV26dOHYY4+lZcuWzJgxg7p1dcqKiMSbnOJxeno6s2fP5ogjjgg7JEkg\nheqBbGalgcv4uWh8KrAOeCP7tTLRG5upN5tI8nB3HnvsMR544IHcbevWraNWrVohRiUiIgVJ5R7I\nAGY29mDvcffOxRFLrGienTpmzJhBly5dmD59OvXq1Qs7HBERyZaZmcmtt95Keno6b7zxhorHKSKa\n8+wDFpDN7GYiBeOm2ZvmEikYv+nu30QjgHihia1I4tu7dy/t27dn2rRpANSsWZP58+dTuXLlkCMT\nEZH9SfUCcirQPDu1zJgxg86dO3PPPffQs2dPSpUq0k2vIiISJR999BGdO3emYsWKTJs2TcXjFFKc\nD9HrCWwCrgWOcfdW7j4m2YrHEpz6QAWjfAUTNF9ff/01p5xyCuXKlWPatGl06tSJjIwM3nvvvZQo\nHuv8Ckb5Ckb5Ck45E5FU1rJlS1avXs2iRYuoX79+sTxcT9ddiSWdXxJLsTy/MjMzGTx4MJdeeikd\nO3Zk5syZKh5LkR3w18HuXr24AhERCWr58uXUr18/d/zkk0/StWvXECMSEREJxsxqHOw97v7v4ohF\nJFpOOukkZs+ezXPPPUejRo20GllEpJjlrDouX748K1eupGrVqmGHJAmuUD2Qf/UhsyrADcBb7v4v\nM2sLdADWA33cfU90wzxgLCcAHwOHARXc/cc8+x4A/gxUBlYCd7n7e/s5jm6tE0kQaWlpdO78czvI\nhQsX0rBhw/ACEhGRIkv1FhZmlkW+h+bl5+4li3Dcs4BRQH1gO/Ac8MiBJrxmVgf4C5GHZJ8AfAlM\nAga5+958770G6AecDmwEHnX3l/ZzXM2zU9gXX3zBn/70J77//nvS0tI455xzwg5JRCRpZWZmMnTo\nUB5//HH++te/ctttt1GixMGaD0iyiuY8u6i/Ah4I7ACamtlS4CTgVeBYYBiRiWdxGQLsJFJAzmVm\n9wN9gB7ABqA7MM/Mznb3rcUYn4hEQWZmJt26dWPUqFEAVK5cWb9JFRGRZNCogG1HAc2zX3cFPaCZ\nVQLmAR8AvweqEZmjG/DwAT56A5GHZT8GfALUBPoD5wKt8xz/d8BUIgXqrsCVwAtm9r27zwsaryQ3\nrUYWESkeWnUssVTUX0PMcvfu7v4HYIe73+nuz7r734BlUYzvgMzsUuByIkXkvNvLAr2AAe7+D3df\nQGTS68CdxRVfMlMfqGCUr2Dy5mvHjh1cfPHFlCpVilGjRnH55Zeze/duvv32W/1AzKbzKxjlKxjl\nKzjlTIJw98UFvF5199uJrP5tU4TD3g6UA65z9/nu/gzwKHCvmR2o+eFAd2/o7qPd/S13HwXcB1xn\nZr/N876HgMXufk92vL2AWRy4OC0pzMy45ZZbYtYbWdddiSWdXxJL0Ti/8vY67tSpE/PmzdO/lSXq\nilpArmBmr5jZkcCLORvNbDAHuQUvWsysBPAkkcnwd/l2XwxUAKbkbMhubTEduKI44hORQ7NhwwbK\nli1LpUqVWLZsGQ888ABZWVnMnj2b8uXLhx2eiIhIcVgIXFOEz7UAZrv7D3m2TQYOBy7b34fc/fsC\nNq/N/vMEADMrAzQE8rermAxcZGYVihCvpIic1ci33XYbjRo1YsCAAezbty/ssEREEtZHH31EgwYN\nmD17NitXruTPf/4zZinbGUxiqKg9kEsBF7j78nzbewEvuvum6IR3wBjuAO4gckvdTcAYsnsgm9nt\nwAigbN6Ga2bWA+jr7r+a2Ko3m0h8ePPNN7nqqqtyxy+99BKtW7c+wCdERCSRpXoP5AMxs2FAK3c/\nOeDntgBPuftf823fTWQuPDTAse4ChgLHu/u27N7KHwIN3f2tPO+rAywH6rr76nzH0DxbfkW9kUVE\nii5vr+N+/fpx2223qXAsvxJ6D2R330dkgph/+6BDjqgQzOwY4K/Aje6eWcD/JEcBuwuYqf4XONzM\nSmX/HUQkDrg7jz32GA888EDutnXr1lGrVq0QoxIREYk9MyvowXNlgDOJPKDugQL2H8xRRB6cl99/\ns/cVNrbjiDxTZJy7b8tzbC/g+P8l0mO50MeX1JazGnn06NE0btyYKVOmcNll+10gLyIi2fbt28eN\nN97Ili1b1OtYik2RCshmVoXIQzbecvd/mVlboAOwHujj7nuiGGNB/gYsdffZ0Txop06dcv/Hq1Sp\nErVr16Zhw4bAz31pNI6Mhw8frvwEGCtfBY8vuugi2rdvz7Rp0wA455xzWLhwIRMmTOC///0vOeIl\n3ngd6/wKNla+go2Vr+DjdevW0a1bt7iJJ97G69atY/v2SO1x06ZNCMfy6xZwe4C3gXvd/c3iDwnM\nrDSRNhU7gXsP9XiaZ2tc0NjMOO2007j//vtp3bo1U6ZMIWcNkK67GsfLWOeXxvF0fmVmZvL000/z\nww8/0KdPHzZt2pT78zUe/j4ah38+xWqeXdQWFs8BO4g80XkpcBLwHpEJ8P+5+1+iGWS+712DSC+2\nS4AN2ZvbAyOB3wLfA51RC4uYWrRoUe5JKgenfP3S5s2bufjii3MvaJ06deKZZ56hdOnSgPIVlPIV\njPIVjPIVnHIWjFpYRF92C4tR7t4v3/ZCt7Aws8lAE+Bid/8kz/acFhaXufvbebbXAVYAF6qFhRTF\nwoULueGGG4q0ElnXXYklnV8SS0HOr5yVxz/88APTpk2jXLlysQ1OEl4059lFLSBf7+5Ts7++zd2f\nzrOvg7uPj0Zw+/ne1wAvE7lFLj8HRgMvAPOB6vkmvM8Btdz9wgKOq4mtSIwtX76c+vXr546ffPJJ\nunbtGmJEIiIStlQvIJvZw8Bz7v51AfuOB27J38u4EMdcDPzH3dvn2XYi8AVwtbu/cZDPjwD+BDR1\n92X59pUBdgF3uvuzebZ3IPJMkqPdfVe+z2ieLYWycOFC2rRpQ48ePejevTulShXphlkRkaSzYcMG\nunTpwtFHH82UKVNUPJZCieY8u0QRP1fBzF4xsyOBF/MENphf34IXbW8DjYCGeV6Dsr/vFcDjRFZF\n7wRyn7xlZocDVwOh3AYoksrS0tIws9zi8YIFC3B3FY9FRESgL3DifvadkL0/qJlAczMrn2dbW+BH\nYPGBPmhm9wN/AdrnLx4DuHsGsJA88+xsNwDL8hePRYJo1KgRK1euZM6cOTRo0ICPPvoo7JBEREKV\n87C8Bg0a0LZtW1577TUVjyUUgQvIZnYlMB54zN13unveB2h8ByyJVnAFcffv3f2tvC8ivZcBlrj7\nJ+6+F3gMeMDM/mJmjYEpRFYtj4plfKkip9eKFE4q5isrK4u77roLM6Nz584cc8wxbNy4EXenUaNG\nB/xsKubrUChfwShfwShfwSlnEpCx/wUYJxJ5OF1Q/wT2Aq+YWRMzu5VIIXqou+/O/cZm6WaWdxXx\njUSeNTIO2Gxm9fK8Kuc5fj+goZk9YWaXZS8iaQE8WoRYRX6hatWqzJ07l06dOnHJJZcwaNAg9u07\n8PPPdd2VWNL5JbF0oPNrw4YNXHrppbz++ussX76crl27UqJEUdeBihyaopx5r7j7Pndfnn+Huw9y\n902HHtahc/fHiEyAewPTgfJEbsP7NtTARJLcjh07aNCgASVLlmTkyJFcfvnl7N69m23btnHKKaeE\nHZ6IiEjozKyjmS0wswVEisf/yBnneS0FJnCQFcMFyV7g0YTIXP91sovHwCP53lqCX/57oFl2PJ2I\n3NGX93VlnuO/A1yf/T1mAS2Bdu4+P2isIgUpUaIEt99+O6tWrdJqZBFJOflXHS9cuJBq1aqFHZak\nuMA9kM3sf0QmkOcBW4Hp7v5dDGIrVurNJnJoNmzYQK1atdi7dy8A999/P3/7298wS9m2liIiUgip\n2APZzFoDbbKHrYi0hPg+39syiNxl9/dEn2trni2HIisri6effpqHHnqI++67T72RRSSp5fQ6Ll26\nNKNHj1bhWA5JqA/RM7MsYBUwDzgJqA+McPeR0QgoLJrYihTNzJkzufLK3EVJvPjii7Rp0+YAnxAR\nEflZKhaQ8zKzsUA/d98Ydiyxonm2RMOmTZu4+eab2b17N2lpaZx11llhhyQiEjWZmZkMHz6cgQMH\n8sgjj/CXv/xF7SrkkIX9ED0Hmrn7A+5+E3AOcFR2bzVJEeoDFUyy5cvdGTRoEGaWWzxet24d7h6V\n4nGy5SvWlK9glK9glK/glDMJwt07J3PxWCRaDtQbWdddiSWdXxJLixYtyu11PH36dJYvX86dd96p\n4rHEnaKckR8BuQ/RcPc97v5X4DdRi0pE4tLevXtp3bo1JUqUoHfv3pxzzjls3boVd6dWrVphhyci\nIpKQzOwGM5tnZl+Y2db8r7DjE4kX6o0sIskkMzOTF198kQYNGtCuXTsWLFiglhUSt4rSwuIPQC/g\nenf/Ks/2e9z9iSjHV2x0a53I/m3evJmLL76YTZs2AdCxY0eeffZZSpcuHW5gIiKS8NTCwm4ExgBp\nwK3ZX5cAfg9sB8ZlL9ZIWJpnSyxkZWXxzDPP8NBDD5GWlsZVV10VdkgiIoW2Z88eWrVqxe7duxkz\nZowKxxITofZAzg6gFTCYyGrk94GKwNfu3j8aQYVBE1uRX1uxYgX16tXLHY8YMYK77rorxIhERCTZ\nqIBsa4GpwGPA/4A67r7GzCoAc4Gp7j4kzBgPlebZEkvLly/n6quvZuzYsSoii0hC2LNnD9dddx1H\nHHEEkyZN0oNBJWbC7oGMu08j0vt4HLADmJHIxWMJTn2ggkm0fD3//POYWW7xeMGCBbh7sRWPEy1f\nYVO+glG+glG+glPOJKDTgXfcPRPIBI4EcPddwCDgzhBjE4l79erV45FHHqFz58688cYbYYcjSUg/\n1yWa8hePlyxZEnZIIoVS5K7c7v6Tu7/k7o+5+5vRDEpEil9WVhZ33303ZkanTp04+uij2bhxI+5O\no0aNwg5PREQkWe0EymZ//RVwVp59BhxT7BGJJJgaNWowffp0OnfuzNChQ8nMzAw7JBGRX0lPT6dp\n06ZaeSwJqUgtLJKRbq2TVLVjxw6uvPJKli5dCsDll1/Oyy+/TPny5UOOTEREUoFaWNhrwBJ3f9zM\nngRaAw8DGdl/bnT3ZmHGeKg0z5bi8umnn9KlSxf27dvHmDFjqF69etghiYiQlZXFyJEj6devH336\n9OGuu+6iZMmSYYclKSCa82z9ukMkRX388cfUqlWLPXv2ANC7d28GDBiAWcr+G15ERCQMA4GTs79+\nOPvrfxC5U3AlcFtIcYkknGrVqrFw4UKeeuopGjRowP3330+3bt1UqBGR0KSnp9OlSxeysrJYtmwZ\np59+etghiRRJkVtYSGpTH6hg4ilfM2fOxMyoXr06e/bs4cUXX8TdGThwYNwUj+MpX4lA+QpG+QpG\n+QpOOZPCMrPSQEngbQB33+7u1wDlgUruXs/dN4YZo0giyHvdLVGiBF27dmX58uW8/vrrXHrppWzY\nsCG84CTh6ee6FEVWVhYjRoygfv36XHvttSxevLjA4rHOL0kUKiCLpAB3Z9CgQZgZV155JQBr167F\n3WnTpk3I0YmIiKSsTGABcGbeje6+1913hhOSSHLIWY3ctm1bGjRooN7IIlJs0tPTadiwIVOmTGHZ\nsmXcc889uhNCEt4h90A2s0ruvj1K8YRGvdkkGe3du5ebbrqJqVOnAnDOOeewYMECqlSpEnJkIiIi\nEeqBbB8AA9x9UtixxIrm2RI29UYWkeKgXscSb6I5zy70CmQzu93MeuYZ1zaz/wDfmdlqMzsxGgGJ\nyKHbvHkz1apVo1y5ckydOpWOHTuSkZHB+++/r+KxiIhIfOkDPGxm54YdiEiyyr8aedq0aWGHJCJJ\n5scff+SKK67QqmNJWkFaWHQF8t5K9yTwNdA++ziPRTEuiXPq0xNMceVrxYoVmBknnHACGzduZMSI\nEbg7aWlplC5dulhiiAadX8EoX8EoX8EoX8EpZxLQg8AxwDoz+8LMVprZiryvsAMUiXeFue7m9Eae\nN28ed9xxh4rIUmj6uS4H8+OPP9KyZUuOO+64/fY63h+dX5IoSgV470nABgAzqwI0AJq4+yIzywBG\nxSA+ESmEcePG0bFjx9zx/Pnzady4cYgRiYiISCF9kP0SkWJQu3ZtZs2aRYsWLQBo1apVyBGJSCLL\nKR7/9re/ZcyYMVp1LEmr0D2Qzew74EZ3n21mbYDRRJ4OnWlmDYE33f3w2IUaW+rNJokmKyuLe+65\nhyeffBKAo48+mlWrVnHKKaeEHJmIiEjhpXoP5FSgebbEo3Xr1tGiRQt69+7NXXfdRYkSer68iASz\nceNGOnTowGmnnabiscSlUHogAyuAO8zsbOAuYJa75zzG9lQi7SxEJMZ27tzJ7373O0qWLMmTTz5J\ns2bN2L17N999952KxyIiIgnKzGqYWQcze8DMjsvedpqZVQg7NpFkVLt2bZYsWcK0adNo2LAh6enp\nYYckIgkiKyuLp556irp163LttdeqeCwpIUgBuTtwNvA+8FsiD/zIcQPwThTjkjinPj3BRCNfH3/8\nMYcddhgVK1bknXfeoXfv3mRlZTFnzhzKly9/6EHGEZ1fwShfwShfwShfwSlnEoSZHWFmLxFpY/Ec\n0A84IXv3AKBvWLGJJIqiXndPO+00Fi1axHXXXUf9+vUZMWIEWVlZ0Q1OEp5+rkteGzdupEmTJkyY\nMIElS5bQo0ePQyoe6/ySRFHoArK7/9vdqwFVgKru/nGe3T2yXyISZbNmzcLMqF69Onv27GHy5Mm4\nOwMHDsRMd/yKiIgkuGHAxUAToAKQ94f7m0CLMIISSRUlS5akW7duLFu2jClTpmg1sogUKO+q46uu\nuoolS5Zw5plnhh2WSLEpdA/k/R7ArJK7b49SPKFRbzaJJ+7O4MGD6d27d+62tWvXUrt27RCjEhER\nib5U74FsZtuAu919opmVBP4H1HH3NWbWCHjd3RO6jYXm2ZIoMjMzGTlyJP379+ehhx6ia9eu6o0s\nImzcuJGbb76ZPXv2MHbsWBWOJWGE0gPZzG43s555xrXN7D/Ad2a22sxOjEZAIqls7969tGnThhIl\nStC7d2/OPvtstm7dirureCwiIpKcDgO+28++CkDmfvaJSJRpNbKI5KVVxyI/C/Lr1K7AzjzjJ4k8\nOK999nEei2JcEufUpyeYg+Vr8+bNVKtWjXLlyjFlyhT++Mc/kpGRwQcffECVKlWKJ8g4ovMrGOUr\nGOUrGOUrOOVMAloJ/HE/+64HlhZjLCIJKdrX3dNPP53FixerN7IA+rmeqqLd63h/dH5JoghSQD4J\n2ABgZlWABkBPd59M5GEfjaMfnkhyW7lyJWbGCSecwMaNGxkxYgTuzvPPP0/p0qXDDk9ERERi7yHg\nOjObB/wJcOBKMxsPtEYP0RMJhVYji6QmrToWKViheyCb2XfAje4+28zaAKOBSu6eaWYNgTfd/fDY\nhRpb6s0mxWncuHF07Ngxdzx//nwaN9bvYEREJPWkeg9kADNrQORuvvpASSJF5HeJLNZ4J8zYokHz\nbEl0eXsjDx8+nJtuuinskEQkBnbs2EGrVq344Ycf1OtYkkI059mlArx3BXBHdt/ju4BZ7p7Tk+1U\nIu0sRGQ/srKyuPfeexkxYgQARx11FKtWreLUU08NOTIREREJU3aR+BIzOww4Ctju7j+GHJaIZMtZ\njXz55Zdz+eWXs2/fPjp16hR2WCISRTt27KB58+bUqVOHESNGxKRdhUgiC9LCojtwNvA+8FugT559\nNwAJvzpCCk99egpv586dnHvuuZQsWZIRI0bQtGlTdu3axffff6/i8X7o/ApG+QpG+QpG+QpOOZOi\ncvef3P1rFY9Fgimu626NGjWYP38+Dz74IGlpacXyPSV8+rme/PIWj0eOHFmsxWOdX5IoCr0C2d3/\nDVQzs2OA7/Pdh9YD+CbawYkksk8++YRatWrx008/AdC7d28GDBiAWUrfpSsiIiL5mFkZoBNQFzge\n2AwsB55394wQQxORfKpXr878+fNp0qQJ27dv56677qJEiSDrskQknnz++ee0bt2aunXrMnLkSP17\nXWQ/Ct0DOfcDZjWAC4isQh7j7t+Y2WnAFnffFYMYi4V6s0m0zJo1iyuuuCJ3/MILL9C2bdsQIxIR\nEYlfqd4D2czOAmYBJwCrga3AscD5RBZotMheyJGwNM+WZPTpp5/SoUMHypYty+jRo3VnoUiCcXee\neeYZHnzwQXr06EHPnj1VPJakE815dpCH6B0BjAGuB/5HZPXyhe6+xsxeAr5w9x7RCCoMmtjKoXB3\nHn/8cXr16pW7be3atdSuXTvEqEREROKfCsj2NlARaOnuX+TZfhIwg0g/5EvDii8aNM+WZJWZmcnw\n4cN57LHHeOSRR7j99tu1GlkkAXz++efcfPPN7Ny5k7S0NGrUqBF2SCIxEc15dpCfbsOAi4EmQAUg\nbwBvAi2iEZAkBvXpidi7dy833HADJUqUoFevXtSoUYOtW7fi7r8oHitfwShfwShfwShfwShfwSln\nElAd4OG8xWOA7HFf4MJQohJJIGFdd0uWLEn37t1ZsmQJEydOpEmTJmzcuDGUWCR29HM9ebg7Tz/9\nNHXq1KFZs2YsXbo09OKxzi9JFEEKyNcBvdx9IZCZb9/nwMlRi0okzn3zzTecdtpplCtXjpdeeokO\nHTqQkZHBhx9+SJUqVcIOT0RERBLHJqDcfvaVA77Yzz4RiRPVq1fn7bffpmXLltSrV4+nnnqKrKys\nsMMSkTw+//xzmjVrxujRo1m8eDG9evWiVKlCPxZMJOUFaWHxA9DK3WeZWUkibSzqZLew+D0wzt0r\nxTDWmNKtdVIYK1eupG7durnj4cOHc/fdd4cYkYiISGJTCwu7BhgKtHf35Xm21wcmAD3c/dWw4osG\nzbMllWzYsIHOnTurN7JInMjf67h79+4qHEvKCKsH8iLga3e/sYAC8jigsrtfGY2gwqCJrRzIuHHj\n6NixY+543rx5NGnSJMSIREREkoMKyLaSyJ18xxB5gF7OQ/SOBb4jskI5l7vXJcFoni2pRr2R10gF\npAAAIABJREFUReKDeh1LqgurB/JDwHVmNg/4E+DAlWY2HmhNpEebpIhU6NOTlZXFPffcg5nRsWNH\nKlWqxKeffoq7By4ep0K+okn5Ckb5Ckb5Ckb5Ck45k4A+AN4AxgGzgDXZf47L3v5hvpeI5BNv1131\nRk4u8XZ+ycHFY6/j/dH5JYmi0Ov23f1tM2sCPAaMIvIQvUeBd4Gm7r4yNiGKFK+dO3dy1VVXsWTJ\nEgCaNm3KK6+8whFHHBFyZCIiIpJs3L1z2DGISGzk9EYePnw49erVY9CgQXTp0iXssESS2rZt22jX\nrh07duxg8eLFcVs4Fkk0hWphYWalgbrAZ+7+tZkdBhwFbHf3H2McY7HQrXXyySefULt2bX78MXJK\n9+rViwEDBuh2MxERkRhK9RYWOcysOvB//PqBeu7uM0MIKWo0zxaB9evXc8UVV3DvvffStWvXsMMR\nSUrbtm2jcePGtGjRggEDBqjXsaS8aM6zC/t/UyawALiCSB/kn4CfohGASNhmz55NixYtcscvvPAC\nbdu2DTEiERERSRVmdi7wAnAWkTv88nOgZLEGJSJRd+aZZ7Jw4UIaNWoEoCKySJTlFI+vvvpq+vfv\nj1nK/25aJKoKtbTS3bOAT4DjYhuOJIpE79Pj7gwePBgzyy0er1mzBnePSfE40fNV3JSvYJSvYJSv\nYJSv4JQzCWgMkYdTtwSqA6fke50aXmgiiSFRrrtVq1Zl4cKFDBs2jMcffxytzE8MiXJ+pbIvv/wy\nYYvHOr8kUQS5N78P8HD2KonQmFlrM3vNzP5jZrvMbJWZ/ariZ2YPmNkXZvajmS02s1phxCvxZe/e\nvdxwww2UKFGCXr16UaNGDbZs2YK7c95554UdnoiIiKSes4De7j7T3T9x98/zv4pyUDM7y8zmm9kP\nZvaVmT1qB/kXtZmVNrPHzeyt7Dl05n7eN9bMsvK9Ms3sjKLEKpJKqlatyqJFi5gyZQrNmjXj88+L\n9L+4iBBZGDZ69GjOP/98OnTokHDFY5FEUqgeyABmthKoChwNfAVsIXJLXS53rxvl+AqKYymwEXgV\n2AZcCfQAurr7U9nvuR94MHv7BqA7kR7OZ7v71v0cV73Zktg333zDJZdcQnp6OgAdOnRg9OjRlC5d\nOuTIREREUluq90A2swXAC+7+bBSPWQn4EPgAGAxUA4YBw9z94QN8riKRefYKIq3uGrv7r9pnmNlY\nInPrTvyy7cY6d88o4P2aZ4vks2/fPoYOHcqQIUPo378/t956qwpfIgF8+eWX3HLLLXz77bekpaVx\n7rmhrnUUiUvRnGcHKSCnka9gnF9xPEXazI529+/zbZsI1Hf3amZWlkhx+3F3/1v2/sOBTcA/9zdp\n1sQ2Oa1cuZK6dX/+vcYTTzxBt27dQoxIRERE8lIB2U4j0gN5OLAQ2J7/PUEfWp29mKIHcJK7/5C9\n7T6gL3Ccu+8uxDHuAJ48QAH57MIuHtE8W2T//v3vf9OpUyeOPPJIRo8ezcknnxx2SCJxzd0ZM2YM\nvXv3plu3bvTs2VMLw0T2I5rz7EK3sHD3Tu7e+UCvaARUiDi+L2DzWuCE7K8bABWAKXk+8yMwnchD\nACUK4r1Pz/jx4zGz3OLxvHnzcPfQisfxnq94o3wFo3wFo3wFo3wFp5xJQNuILHQYB3wJ7CrgFVQL\nYHZO8TjbZOBw4LJDCVYkHiXydbdGjRosXbqUZs2aUadOHZ5++mn1Ro4ziXx+JZsvv/ySK664gr//\n/e8sWLCAPn36JHzxWOeXJIpCF5DN7GEzO2E/+443s/3eDlcMLgY+zv66OpBJ5KF/eX0EnFmcQUnx\nysrK4p577sHM+OMf/0ilSpX49NNPcXeaNGkSdngiIiIiBZkANAaGAH8GuhTwCupMYH3eDe7+JfAj\n0ZsP1zCzHWa2x8zeNrNLo3RckZRTqlQpevXqxeLFixk9erR6I4vkk7fX8SWXXMK7776rlhUixSxI\nC4tM4CJ3X1HAvguAFQXd4hZrZtYEmAN0cvfxZvYA0MPdj873vpuBZ4Cy7r6vgOPo1roEtXPnTlq2\nbMnbb78NQJMmTXj11Vc54ogjQo5MREREDkYtLOwH4BZ3nxTFY2YQmQ8/mW/7l8Dz7v5gIY5xoBYW\nXYEM4N9AFSLPG6kDNHD3VQW8X/NskUJSb2SRX1KvY5Gii+Y8u1SQ78v+eyCfCPz30MMJxsyqAhOB\nV9x9/KEer1OnTlStWhWASpUqUbt2bRo2bAj8fFuBxvEz/s9//sNtt93Gjz9G2gK2bduWiRMnUqJE\nibiIT2ONNdZYY401/vV43bp1bN8eafO7adMmhE1EVgYnDHcfmXdsZjOJPLTvAeC6gj6jebbGGhdu\nvGTJEurVq8fixYvp1KkTzzzzDPfddx9t27aNi/g01ri4xu5Oz549eeaZZ+jZsyc9e/bknXfeYdGi\nRXERn8Yax+M4lvPsA65ANrOOQMfs4WVEeg3vzPe2csC5wBx3bxXV6A7AzI4ClhJ50Egjd9+Tvf12\nYASRlcae5/09gL7uXmE/x9PKiADyXrSL2+zZs2nRokXu+IUXXsidUMWrMPOViJSvYJSvYJSvYJSv\n4JSzYLQC2a4EHgVau/umKB1zCzDK3fvl276byHx4aCGOsd8VyPt5/yigpbtXLWCf5tkSU8l63c27\nGnnAgAHccsstYYeUkpL1/IpnmzdvpnPnzimx6ljnl8RSca5A/hH4Luf7AjuA/A+xywBmAn+PRkCF\nYWaHAW8AJYlMVPfk2b0+e/tp/LIP8q96wUnicHeGDBlCz549c7etXr2a888/P8SoRERERA7Zo8BJ\nwMdmtonI4ohfcPe6AY+5nny9js3sRCIP0YvVfNjZ/92KIlIEOb2Rr776aq677jq2bNnCgw8etAON\nSEL76quvaNSoEW3atKFv374J/5A8kWQRpAfyWOCv7v5ZbEM6aBwlgdeJ9Fm7yN035ttfFtgCDHb3\nAdnbDgc+A/7p7n33c1ytjIhDGRkZ/PGPf+TFF18E4KyzzmLRokUce+yxIUcmIiIi0aAVyDb2YO9x\n984Bj9kb6AGc7O4/ZG/rATwCHOfuuwtxjEKvQM5e3PEhsNrdWxewX/NskUO0efNmGjduTPv27VVE\nlqSVUzy++eab6dWrV9jhiCS8UHog5524ZhdkbyaysuEbYJy7F9djYv8BXAHcBVQxsyp59q1x971m\n9hjwoJltJ7LKojuRFdSjiilGOUTffPMNl1xyCenp6QDcdNNNjBkzRr99FBERkaQStDhcSP8EugKv\nmNkgoBrQFxiat3hsZunAQne/Jc+2FkB54LzscU6LupXu/oWZHQnMACYA6UQeoncPcDwwIAZ/FxEB\njj/+eBYsWEDjxo3JysrioYce0sP1JKl88cUXNG3aVMVjkThV4kA7zWyomX2cb1sFYA0wHLgBeBh4\nz8zOiFmUv9SMyO1xI4j0QM77Oh7A3R8D/gb0BqYTmQQ3dfdviynGpJfTrDvaVq1ahZlx/PHHk56e\nzhNPPIG7M378+IQuHscqX8lK+QpG+QpG+QpG+QpOOZOwuft2oAmRuf7rZBePiaxAzqsEv/73wD+A\nl4CcwvZL2a+G2eO9wFagD5GWcv8k0vLuUndfG8W/hkihpcp1N6eI/Prrr9OiRQu++OKLsENKCaly\nfoXF3UlLS6NOnTr85S9/Sbnisc4vSRQHW4HciMjqgrx6AGcAf3L3MdkrgOcCDwEdoh/iL7n7KYV8\n30BgYIzDkSiZMGECHTr8fPrMnTuXpk2bhhiRiIiISPExsxrABcBvgTHu/o2ZnQZscfddQY/n7uuB\nA06m3P3UArYdcK7t7nuB64PGIyLRcfzxx7Ns2TIGDx7MBRdcwMCBA7n55pu1GlkS0ldffcWtt97K\nV199xZw5c6hdu3bYIYnIfhywB7KZfQ90cPc38mz7AMDdz8mzrQPwaEGT0ESh3mzFLysrix49evDE\nE08AULFiRdasWcOppybsaSQiIiIBqQeyHQGMAVoB+4gs8LjQ3deY2UvAF+7eI8wYD5Xm2SKx8f77\n79OpUycqV67Ms88+y0knnRR2SCKF4u48//zz9OzZkzvuuIP777+fMmXKhB2WSNKJ5jz7gC0siExg\n9+T5xkcDZwEL8r1vE3BcNAKS5Ldz504uu+wySpYsyRNPPEHjxo3ZtWsX27dvV/FYREREUs0w4GIi\nq4UrEHluR443gRZhBCUi8e/cc8/l3Xff5dJLL+WCCy7gueeeQ7+skXj31Vdf0bJlS4YPH86cOXPo\n27evisciCeBgBeSP+bnfGUDL7D9n53vfscD3UYpJEkBR+vSkp6dTvnx5KlasyFtvvUXPnj3JzMxk\n/vz5HHHEEdEPMo6or1EwylcwylcwylcwyldwypkEdB3Qy90XApn59n0OnFz8IYkkllS+7pYuXZo+\nffqwYMEC/vGPf6g3cgyk8vkVTTm9js877zzq1q3LihUr1LICnV+SOA7WA3kU8KyZVQS2AHcBnwFz\n8r3vcuCD6IcnyWDOnDk0b948dzxp0iTatWsXYkQiIiIiceMwIg+hK0gFfl1UFhH5lZzVyOqNLPHo\n66+/5pZbblGvY5EEdsAeyABmdj9wB1AJWAPc4e7v59lfBXifSA/kf8Qw1phSb7boGzJkCPfdd1/u\nePXq1Zx//vkhRiQiIiLxRj2QbRHwtbvfaGYlgf8BdbJ7II8DKrv7laEGeYg0zxYpXu+//z7t2rXj\nmmuuoX///ioiS6g2bdpEw4YN6dChAw899JDaVYgUo2jOsw9aQE4VmthGR0ZGBh07dmTy5MkAnHnm\nmSxevJhjjz025MhEREQkHqmAbJcAc4ElwBTg70BfoDrQGrjE3VeGF+Gh0zxbpPht27aNxo0bc/XV\nV6uILKHJKR736NGDO++8M+xwRFJOcT5ET6RA+fv0bNmyhdNPP52yZcsyefJkbrrpJvbu3ctHH32k\n4jHqaxSU8hWM8hWM8hWM8hWcciZBuPvbQBOgLJH2cQY8CpwKNEn04rFIcdB199cqV67MggULmD59\nOn369NHD9Q6Bzq+i+eyzz1Q8LgSdX5IoVECWQ7Jq1SrMjOOOO4709HSGDRuGuzN+/HjdmiIiIiJy\nEGb2MPCZu18CHAmcCFRw9wbAxuz9IiKB5RSR58yZw9VXX81XX30VdkiSAtydCRMmUK9ePXr27Kni\nsUiSUAuLbLq1LpgJEybQoUOH3PHcuXNp2rRpiBGJiIhIIlILC8sELnL3FQXsuwBY4e4liz+y6NE8\nWyRcGRkZDBw4kKeeeorBgwfTsWNHtbSQmNi8eTN//vOf2bhxI2lpaVxwwQVhhySS0tTCQkKRlZVF\n9+7dMTM6dOhAxYoV+fTTT3F3FY9FREREisaA/VVXTwT+W4yxiEgSKlOmDH379mXOnDkMHz6cli1b\najWyRFXOquNatWpRs2ZNVq1apeKxSJJRAVkOateuXVx22WWULFmSYcOG0bhxY9588022b9/Oqaee\nGnZ4CUF9jYJRvoJRvoJRvoJRvoJTzuRgzKyjmS0wswVEisf/yBnneS0FJgCLw41WJP7puls4tWvX\nZsWKFdStW5fzzjuPtLQ09UYuBJ1fB7Z582b+8Ic/MGjQIGbOnEm/fv0oW7Zs2GElDJ1fkihUQJb9\nSk9P54gjjuDII4/krbfe4r777iMzM5P58+dz2GGHhR2eiIiISKL6Efgu+2XAjjzjnNdnwGDg1pBi\nFJEkpNXIEi05zz7SqmOR1KAeyNnUm+1nc+bMoXnz5rnjSZMm0a5duxAjEhERkWSlHsg2Fviru38W\ndiyxonm2SHz63//+x4ABA9QbWQLbvHkzt912G5s2bSItLY3zzz8/7JBEpADqgSwxMXToUMwst3i8\nevVq3F3FYxEREZEYcffOyVw8FpH4Vbp0afr27cvcuXMZMWKEViPLQeVddVyrVi1WrVql4rFIilAB\nOcVlZGTQrl07zIwePXpw5plnsmXLFtz9gD8I1KcnGOUrGOUrGOUrGOUrGOUrOOVMRKR46bp7aGrV\nqpXbG7l27dqMHTtWvZHz0PkVsXnzZq655hoef/xxZs2aRb9+/ShTpkzYYSU8nV+SKFRATlFbtmzh\njDPOoGzZskyePJmbbrqJvXv38tFHH3HssceGHZ6IiIiIiIgUk5zVyPPmzWP48OF07dpVRWTJtX79\nei688EKtOhZJYeqBnC1VerOtXr2aOnXq5I6HDh3KvffeG2JEIiIikspSvQdyKkiVebZIstixYwfN\nmzenTp06jBw5Un2RU9z69etp0qQJAwYMoGPHjmGHIyIBqAeyBDZx4kTMLLd4PGfOHNxdxWMRERER\nERHJVbFiRWbPns2qVau0EjnFqXgsIjlUQE5iWVlZdO/eHTPjpptu4sgjjyQ9PR13p1mzZod0bPXp\nCUb5Ckb5Ckb5Ckb5Ckb5Ck45ExEpXrruRl9OEXn16tVcc801bN68OeyQQpOK55e7M3nyZC677DIV\nj2MsFc8vSUwqICehXbt20bBhQ0qWLMmwYcNo3LgxO3fuZMeOHVSrVi3s8ERERERERCTOVaxYkcWL\nF1OrVi1q167NhAkTtBo5BWzZsoVWrVrRr18/ZsyYoeKxiADqgZwrGXqzpaenc95557F7924AevTo\nwaBBgyhRQr8nEBERkfikHsjJLxnm2SKpbs2aNXTq1IlTTjmFf/7znxx//PFhhyRRlrPquFu3bnTp\n0oW+fftSrly5sMMSkUOgHsjyC3PmzMHMOP3009m9ezcTJ07E3Xn88cdVPBYREREREZFDcv7557Nq\n1Spq1qyp1chJKGfVcf/+/ZkxYwYDBw5U8VhEfkHVxQQ2dOhQzIzmzZsDsGrVKtydG2+8MebfW316\nglG+glG+glG+glG+glG+glPORESKl667xaNMmTL069ePmTNnMnjwYP7whz+kRG/kZD6/3J0XXniB\nmjVrUr16dVavXs2FF14YdlgpJZnPL0kuKiAnmIyMDG688UbMjB49elC9enW2bNmCu3PBBReEHZ6I\niIiIiIgkMa1GTg5adSwiQagHcrZ47822ZcsWLr30Uj7++GMA2rdvz5gxYyhTpkzIkYmIiIgUnXog\nJ794n2eLSNGpN3LiUa9jkdShHsgpZPXq1ZgZxx13HB9//DFDhw7F3ZkwYYKKxyIiIiIiIhIarUZO\nLFp1LCJFpQJynJo0aRJmRp06dYDIg/LcnXvvvTfkyCLUpycY5SsY5SsY5SsY5SsY5Ss45UxEpHjp\nuhuu/L2Ru3TpQmZmZthhRU2ynF9r166ldu3a6nUcZ5Ll/JLkpwJyHMnKyqJHjx6YGe3bt6dChQqk\np6fj7jRr1izs8EREREREREQKdP755/Puu+/yxRdfJF0ROdGtXbuWK664glGjRmnVsYgUiXogZwuz\nN9uuXbv4/e9/n/ubp0aNGvHaa69RoUKFUOIRERERKS7qgZz81ANZJLX8+OOPXH311Zx44omMGTOG\nkiVLhh1SSsspHj/11FO0atUq7HBEpBipB3KS+PTTT6lQoQJHHnkkixYtokePHmRmZrJgwQIVj0VE\nRERERCThHH744UyfPp3//Oc/XHfddWzZsiXskFLW1KlTadGihYrHInLIVEAOwdy5czEzTjvtNHbv\n3s3EiRNxdx5//HFKlEiM/yTq0xOM8hWM8hWM8hWM8hWM8hWcciYiUrx03Y0/hx9+OG+++SZnn302\ntWrVYvLkyQn7cL1EPL++/fZbbrjhBh588EFee+01FY/jWCKeX5KaEqNamSSGDRuGmXH55ZcDsGrV\nKtydG2+8MeTIRERERERERKKnbNmyDBgwgOnTp9OvXz+uv/56rUYuBlOnTqVmzZqcfPLJrF27lvr1\n64cdkogkAfVAzhar3mwZGRl07tyZSZMmAVC9enUWL17Mb37zm6h/LxEREZFEox7IyU89kEVk7969\nPProo4wZM4bhw4dzww03YKZLfzR9++233HHHHfzrX/8iLS1NhWMRUQ/kRLB161aqV69O2bJlmTRp\nEjfeeCN79+5l/fr1Kh6LiIiIiIhIytBq5NiaMmUK5557LlWrVtWqYxGJCRWQo2z16tWYGb/5zW/4\n+OOPGTJkCO7OxIkTKVOmTNjhRY369ASjfAWjfAWjfAWjfAWjfAWnnImIFC9ddxPHhRdeyJo1a6he\nvTo1a9bkhRdeiPveyPF8fn377be0adOGhx56iFdffZXBgwdz2GGHhR2WBBDP55dIXiogR8mkSZMw\nM+rUqQPA7NmzcXe6d+8ecmQiIiIiIiIi8SFnNfKMGTPo378/rVq10mrkItCqYxEpTuqBnK0ovdmy\nsrLo1asXQ4YMAaBChQqsXbuWatWqxSJEERERkaSjHsjJTz2QRWR/cnojjx49muHDh9O2bVv1Rj6I\nrVu3cscdd/D++++r17GIHJB6IBeCmZ1lZvPN7Acz+8rMHrUo/STatWsXjRo1omTJkgwZMoSGDRuy\nc+dOdu7cqeKxiIiIiISuKHNhMyttZo+b2Vtm9qOZZR7gvdeY2b/M7Ccz+9DM2kT/byEiyS7/auT2\n7duTkZERdlhxa+nSpdSsWZNTTjlFq45FpFglZQHZzCoB84B9wO+BR4Hu2X8W2aeffkrFihU58sgj\nWbRoEd27dyczM5OFCxdSoUKFQw88gahPTzDKVzDKVzDKVzDKVzDKV3DKmYTtEObChwNdgB+Adw5w\n/N8BU4H5QAtgBvCCmTU95OBFikDX3cR34YUXsnr1anbv3k3btm3jqogcL+fX0qVL+cMf/kBaWpp6\nHSeReDm/RA4mKQvIwO1AOeA6d5/v7s8QmTDfa2ZHBD3Y3LlzMTNOO+00du7cyYQJE3B3hgwZQokS\nyZpCEREREUlQRZoLu/sOdz/G3a8AXj3A8R8CFrv7Pe6+2N17AbOAh6P4dxCRFFOuXDmmTp3Kvn37\n4q6IHLac4vH48eNp0aJF2OGISApKyh7IZrYY+Mrdb8yz7bfA58DV7v5GAZ/5VW+2YcOG/eIheKtW\nreKCCy6IWdwiIiIiqUY9kKOvKHPhAo5xB/Cku5fMt70MsAvoml2YztneARgDHO3uu/J9Rj2QRaTQ\nMjIyuP7668nMzGTs2LEce+yxYYcUqldeeYXbbruN8ePH07x587DDEZEEoh7IB3cmsD7vBnf/Evgx\ne99+ZWZmMm3aNOrWrUv37t0544wz+Oabb3B3FY9FREREJBEUeS5cCNWA0vmPD3xE5N8WZxzi8UUk\nxZUpU4apU6dyzjnnULNmTaZMmRJ2SKHYtm0b7dq1o1evXrz22msqHotIqJK1gHwUsL2A7f/N3leg\nkSNHcsYZZzB06FB69erFvn372LBhA7/5zW9iFmiiUp+eYJSvYJSvYJSvYJSvYJSv4JQziQNFmgsH\nOLYXcPz/AhaF44sEputu8ilTpgyDBg3i1Vdf5eGHH6Z169Zs3bo1lFjCOL9eeeUVatasyQknnMC6\ndeu46KKLij0GKR66fkmiSNYCcpEsWrSI8ePHs3TpUlq1akXJkiUP/iERERERERERibr69euzdu1a\nTj311JRYjZx31fGUKVMYOnQohx9+eNhhiYhQKuwAYuS/QMUCth+Vva9AFSpUYM6cOcyZM4dKlSpR\nu3ZtGjZsCPz8WyGNI+OcbfEST7yPc7bFSzzxPs7ZFi/xxPs4Z1u8xBPv45xt8RJPvI9ztsVLPIky\nzhEv8cTTeN26dWzfHlm8umnTJiQmijQXDnBsK+D4R+XZ/yudOnWiatWqAJpnaxyTcY54iUfj6I4H\nDRrEtddeS5s2bRg1ahRTpkzh2GOPTarz65VXXuFPf/oTTZo0Yd26dRx++OFxk3+NYzvOES/xaJy4\n41jOs5P5IXr/cff2ebadCHxBgIfoiYiIiEhs6SF60VeUuXABxzjYQ/TudPdn82zXQ/REJOb27NlD\n3759ef755xk5ciStW7cOO6RDtm3bNrp27crq1asZO3YsDRo0CDskEUkSeojewc0EmptZ+Tzb2hJ5\ncMjicEJKLvl/UyYHpnwFo3wFo3wFo3wFo3wFp5xJHIjZXNjdM4CFQP6qzQ3AsvzFY5HioOtu6ihX\nrlyx90aO5fmVv9exisepR9cvSRTJWkD+J7AXeMXMmpjZrUBfYKi77w43NBERERGRmCrUXNjM0s3s\n2bwfNLMWZtYKOC973Cr7dVKet/UDGprZE2Z2mZkNBloAj8b47yUiAiR+b2T1OhaRRJOULSwAzOxM\nYBRwEZGnRD8LPLq/++d0a52IiIhI8VMLi9gozFzYzDYCC9395jzbPgNO4tc6u/u4PO/7PdAfOB34\nDOjr7gVWcDTPFpFYevfdd+ncuTM1a9YkLS2Nww47LOyQDmjBggXcdNNNtGvXjn79+qlwLCIxE815\ndtIWkIPSxFZERESk+KmAnPw0zxaRWNuzZw9dunRh27ZtvPbaa3FbRJ47dy7t27dn8uTJNG7cOOxw\nRCTJqQeyhE59eoJRvoJRvoJRvoJRvoJRvoJTzkREipeuu1KuXDnGjRtHlSpVuOaaa/jpp5+iduxo\nnV85xeOXX35ZxWPJpeuXJAoVkEVEREREREQkoZUqVYrnn3+eKlWq8Pvf/55t27aFHVKuV155Jbd4\n/Lvf/S7scEREAlMLi2y6tU5ERESk+KmFRfLTPFtEitO+ffvo3bs3kyZN4qmnnuLaa68NLZbvv/+e\nu+++m2XLljFx4kTq1asXWiwiknrUwkJEREREREREJJ9SpUoxZMgQXnrpJXr16kW7du1CWY08ffp0\nzj33XI4++mjee+89FY9FJKGpgCxFoj49wShfwShfwShfwShfwShfwSlnIiLFS9ddKcjvfvf/7N13\nnFTV+fjxzwNoFAlNscdGYtAY4teI/atG/SpGI2KvEQE1dhGNGnuJhigo1ogFMRpRMXbRYK8/OzHY\nC3axASoqUvb8/pjZZN0MsLO7M3dm9vN+ve4L5957zjx7fLhz9nDnuRsxceJElllmGXoLNc6TAAAg\nAElEQVT37s3NN9/crH6Kza+pU6ey9957M2TIEK677jpGjhzJYost1qz3Vu3z+qVq4QKyJEmSJEmq\nOR07dmTEiBFluxu58V3HG2+8ccneS5LKyRrIedZmkyRJKj9rINc+59mSKsE333zDCSecwNixY1u9\nNnLDWsdXXnmlC8eSKoI1kCVJkiRJkpqo0N3IM2bMaHG/EyZM8K5jSTXPBWQ1i3V6iuN4FcfxKo7j\nVRzHqziOV/EcM0kqL6+7KkZ9beSFF16YbbbZZoGLyPPLr9tvv5299tqLa665xlrHahavX6oWLiBL\nkiRJkqQ2o2PHjowePZof//jHTVpELuT2229n8ODB3HHHHfzqV78qQZSSVDmsgZxnbTZJkqTyswZy\n7XOeLalS1dXVsd9++/H6669zyy230L179ya1u+mmmzjooIO444476NOnT4mjlKTmsQayJEmSJElS\nC7Rr147LLruMddZZh5///Ofcdttt8z1/+vTp7Lvvvhx11FHceeedLh5LajNcQFazWKenOI5XcRyv\n4jhexXG8iuN4Fc8xk6Ty8rqrlmjXrh3nnHMOf/vb3xgyZAh77703U6dO/ffx+vy66667+PnPf07H\njh3517/+xdprr51RxKolXr9ULVxAliRJkiRJbdomm2zCCy+8QLdu3b53N/KMGTPYd999Ofjggxkz\nZgwXXXQRnTp1yjhaSSovayDnWZtNkiSp/KyBXPucZ0uqNg899BADBw5kzTXX5KmnnmK77bZj2LBh\nLhxLqiqtOc92ATnPia0kSVL5uYBc+5xnS6pGX3/9Needdx7rr78+m222WdbhSFLRfIieMmednuI4\nXsVxvIrjeBXH8SqO41U8x0ySysvrrkphscUW4/jjj6ddO5dNVDpev1QtvBJKkiRJkiRJkgqyhEWe\nX62TJEkqP0tY1D7n2ZIkSeVnCQtJkiRJkiRJUsm5gKxmsU5PcRyv4jhexXG8iuN4FcfxKp5jJknl\n5XVXpWR+qZTML1ULF5AlSZIkSZIkSQVZAznP2mySJEnlZw3k2uc8W5IkqfysgSxJkiRJkiRJKjkX\nkNUs1ukpjuNVHMerOI5XcRyv4jhexXPMJKm8vO6qlMwvlZL5pWrhArIkSZIkSZIkqSBrIOdZm02S\nJKn8rIFc+5xnS5IklZ81kCVJkiRJkiRJJecCsprFOj3FcbyK43gVx/EqjuNVHMereI6ZJJWX112V\nkvmlUjK/VC1cQJYkSZIkSZIkFWQN5Dxrs0mSJJWfNZBrn/NsSZKk8rMGsiRJkiRJkiSp5FxAVrNY\np6c4jldxHK/iOF7FcbyK43gVzzGTpPLyuqtSMr9USuaXqoULyJIkSZIkSZKkgqyBnGdtNkmSpPKz\nBnLtc54tSZJUftZAliRJkiRJkiSVnAvIahbr9BTH8SqO41Ucx6s4jldxHK/iOWaSVF5ed1VK5pdK\nyfxStXABWZIkSZIkSZJUUFXVQI6IHwJHAX2BnwLfAk8Ax6SUXm907rLARcDmwHfAWOD3KaVv59G3\ntdkkSZLKzBrIpRERqwEXAusB04HLgVMWNOGNiM7ASKAfuZtN7gAOSylNbXDOaGCfRk0TsFpK6bUC\nfTrPliRJKrPWnGd3aI1OymgFYBC5CfAfgI75P5+MiJ+nlD4AiIgOwD+AmcAuQDfgXKAL8NsM4pYk\nSZLKIiK6AvcCk4DtgJ7ACCCAkxbQ/Ebgx8BAcovCfwZuBjZpdN7LwIB8n/XeblnkkiRJqkTVVsLi\nLaBnSumUlNJ9KaXbgV8DC5Gb5NbbmdwdyjuklO5OKV0HHArsERE9yx51DbJOT3Ecr+I4XsVxvIrj\neBXH8SqeY6YKcCCwCLm58H0ppVHAqcCREdFpXo0iYn3g/4DfppRuSSndCuwF/G9EbNbo9K9TSk+n\nlJ5qsM0q0c8jzZfXXZWS+aVSMr9ULapqATml9G1K6btG+6YB7wDLNtjdF3g6pfRug323ALPzx9RC\nEydOzDqEquJ4FcfxKo7jVRzHqziOV/EcM1WAvsA9KaWvG+wbS+7be43vJG7cbkpK6bH6HSmlp4HJ\nwNalCFRqDV53VUrml0rJ/FK1qKoF5EIioge5r9m92mB3L+CVhuellGYDb+aPqYWmT5+edQhVxfEq\njuNVHMerOI5XcRyv4jlmqgCF5sLvAd8w/7nwf7XLe7lAu9Uj4ouImBkRj0TExi0JWGoJr7sqJfNL\npWR+qVpU/QIyMBz4ChjTYF83cg8LaWxa/pgkSZJUq5o7F25qu+eAocC2wB7kfqeYEBFrNytaSZIk\nVbTMH6KXf9LzMgs6L6X0auN9EXEguUnrDvlSFiqTt99+O+sQqorjVRzHqziOV3Ecr+I4XsVzzFTr\nUkoXNHwdEeOBF8k93HqHTIJSm+Z1V6VkfqmUzC9Vi0gpZRtAxCDgMnJPeS54CpBSSu0btdsOuAk4\nJqU0otGxJ4FJKaVBjfZPAh5IKR1aII5sB0KSJKmNSilF1jHUkoj4GLgwpXR6o/0zgJNTSsPn0e56\nYImU0uaN9t9Bbj7+m/m854XAtimllQocc54tSZKUgdaaZ2d+B3JK6QrgimLaRMSGwHXAxY0Xj/Ne\noVGdtohYCFgFuGQecfiLiyRJkmpBobnw8uQeoleoxnHDdoML7O8F3LyA90zM44YQ59mSJEnVrepq\nIEfEz4DbgLtSSofP47TxQJ+I+FGDff2AhYG7SxyiJEmSlKXxwFYRsViDfbuRe4jeQwtot3REbFC/\nI1/XeBXgrnk1iohFgW2AZ1oStCRJkipT5iUsihERPcg9tGMusA8ws8HhL1NKL+fP65A/bxZwItAV\nGAH8I6W0T1mDliRJksooIrqSq0n8IjAM6EnuwdMjUkonNzjvDXLl3fZrsO9u4MfA0eTuKP4TMCWl\ntGn+eGfgDuAa4A2gBzAE+AWwQUrp+VL/fJIkSSqvzEtYFGl1YNn8f9/f6NhDwGYAKaU5EdEXuBC4\nHviOXMmL35cpTkmSJCkTKaXpEbE5ubnwbcB0cgvIpzY6tR3//Y3EXYBzyZWYawfcDjT81t93wCfA\n8cCS5G7oeBzY2MVjSZKk2lRVJSxSSg+llNrXb8AawIPkJq4/jYhTIyLy536YUtohpdQ5pdQjpXRY\nSmkm5O6ciIjRETE1IqZHxDUR0b3x+0VEv4h4ISK+jYgXI2KXAuc0qa9KEBGrRcR9EfF1RHzQcLwW\n0K5VxisiVoyIugLb31rz52wtpRyviNgiIv4WEZPzY3BSc/uqFFmPl/n17+PtIuKYiHg4Ij7Lb/fk\nv4JcVF+VJOvxMr++d84pkbvWfxERX0bE0+HnY4vGy/ya5/n98uPwVEv7aotSSq+klLZIKS2WUlou\npXRKavTVw5TSKo0fOp1S+jKlNCil1D2l1DWltHdKaWqD49+llHZKKa2YUloU2ABYBHiwlPmgtqk5\n15tqu6YqOxHRMyIujYh/RsSciGh8k9q82nn90gI1J7+8fqkpImLniLg1It6PiK8i4pmI2K0J7Vp0\n7aq2O5D/LXJfzbsXmARsR+6reSOAAAouxjVwI7mv5g0k99W8P5N7MMgmDfrfCBhH7s6NQ4FfA9dF\nxNSU0r3F9FUJKmi8AI4kd6dKvc+a91OVTqnHC+gL/Dz/HvP7i25+5TR1vMD8WhQ4htydY2fmzzkU\neDQi1m90d5j5Vdx4gfkF8ENgNPASuZJSOwFjI2JOSunvRfaVuQoaLzC/Gr7PD/L9TmlpXyqdcuWD\n2qYW5hdUwTVVmfsZud8z/h/FrY14/VJTNDe/wOuX5m8I8BZwBLnc+DXwt4hYPKV00XzatezalVKq\nyg04DvgcWKzBvqOBGUCn+bRbH6gDNmywr09+32YN9t0D3Nuo7Z3Aw8X2VQlbhYzXivl2v856PLIe\nr0ZtPgVOao2+2vh4mV+51+2ALo3aLQRMBq4wv5o9XubX/N/zUeAW86vZ42V+/ff5J5IrT3Yl8FRr\njb1bdeaDW9vcWpBfVXNNdaucjdzCyv1NOM/rl1vRWxH55fXLbYEb0L3AvmuBN+fTpsXXrqoqYdFI\nX+CelNLXDfaNBToy/9XzvuQeBPJY/Y6U0tPkFgu2BoiIhYFNgRsatR0LrB8RP2xqXxWkEsarmpRs\nvIqMoc3nV40q2XillOpSSl80bJRSmk3uQUrLFtNXBamE8aomWfx9/BxYuJX6KrdKGK9qUvLxiogV\nyC0SHU7uTsNm96WS8/NfpdTc/JJKyeuXpEylBqXFGnie+f/+2uJrVzUvIPcCXmm4I6X0HvBN/liT\n2+W93KBdT3J3oDU+72VyY7ZqEX1VikoYr3qj8zWAPoyI4RGxSNN+hLIq5Xg1O4YW9FVqlTBe9cyv\nRvL/yLMW8GpL+8pIJYxXPfMrLyLaR0SXiNgT+D/gkub2lbFKGK965lfOcGBsSmliK/Sl0qqkz3/V\nnubmV71quKaq+nj9Ujl4/VKxNgBem8/xFl+7qrYGMtCN3BOlG5uWP9acdis3OCcVOG8auTthujU4\nb0F9VYpKGK/vyNVI/gfwJbm7lo8FVgH6L+gHKLNSjldrxNCW8qupzK95OyHftmE9JPNr3gqNl/nV\nQESsCzyRfzkbOCSldHtz+qoAlTBe5ldeRGwGbAH8pKV9qSwq4fNftau5+VVN11RVH69fKiWvXypa\nRGwO9AMGzOe0Fl+7qnkBWVUmpTQFOKzBrocj4hPgooj4eUrpXxmFphpgfhUWEdsAfwCGpJRezzqe\nSjev8TK//ssLwNpAV2AbcuPwZUrp+mzDqljzHS/zKyci2gMjgTNSSj4sRlKzeE2VVK28fqlYEbES\nufrHN6eU/lrK96rmEhbTgC4F9nfLH2tJu/o7Zxuf163B8ZbEkIVKGK9CxuXb/nI+52ShlONV6hiy\nUAnjVUibzq+I6EOuVuDFKaULWimGLFTCeBXSZvMrpfRtSum5lNL9KaWhwF+BYa0QQxYqYbwKaYv5\ntT/QGRiTL/fRlVyt6PryH/U3PlRTftW6Sv38V21ozTyp1Guqqo/XL5Wb1y8VFBHdgPHk6hjvtYDT\nW3ztquYF5FdoVKcjIpYn91CFQnU95tkur2E9kDfJfcW08XmrAXP5T12RpvRVKSphvApJjf6sFKUc\nr2bH0IK+Sq0SxquQNptfEbEqcAcwgdyDqJrdVwWohPEqpM3mVwHPAT+KiPp5hfk1f43Hq5C2mF+r\nAssDn5CbyE4Fdgf+J//fuxTRl8qjUj//VRuam1+FVOo1VdXH65fKzeuX/ktELArcCbQHtk0pzVxA\nkxZfu6p5AXk8sFVELNZg327kHqrw0ALaLR0RG9TviIi1ydWUuQsgpTQLeADYuVHbXYEnUkpfNbWv\nClIJ41XIzuQuhM828ecol5KNV5ExtPn8aqE2mV8RsQxwN/A6sEdKqdBkw/z6z76mjFchbTK/5mEj\n4P2UUl0r9FVulTBehbTF/LoA+BW5en/12z3kHmi5Kbl/4GlqXyqPSv38V21obn4VUqnXVFUfr18q\nN69f+p582bdxQE+gb0rp8yY0a/m1K6VUlRu5OoIfkCsuvjm5rz1+BZza6Lw3gMsa7bs7v78/sD25\n1fYHG52zITALOBfYBPgzMAfYvNi+KmGrhPECTgbOyfezOXAauQngDVmPTwbjtQKwI7AT8AVwff51\nX/OreeNlfv37+CLARHJ3620NrNtgW9P8at54mV/f+7t4LzCY3ELfb4DR5L5tsp/51bzxMr/m+36j\ngacK7K+K/Kr1rdz54Na2tubmVzVdU92y3YBF+c/vGI8D/8q/3hFYJH+O1y+3Zm3NyS+vX25N2YBR\nQB1wCN///XVdYKH8Oa1+7cr8B2/hoPUi94vZ1/nJxSlANDrnLeCKRvs6A1eQWzCYTq4WYfcC/W9H\n7sE33wIvATsXOKdJfVXClvV4kbsj+SlyX0udSa60xcn1CV5pWynHC9gn/xd+bqPtLfOreeNlfv37\n+IoFxsn8auF4mV/fOz6GXOmib4AP8++zVYEYzK8mjpf5Nd/3mtcCctXkV61v5cwHt7a3NSe/qu2a\n6pbdRm4eWOh3jLnACvlzvH65NWtrTn55/XJrykau5vG8foct2bUr8p1IkiRJkiRJkvQ91VwDWZIk\nSZIkSZJUQi4gS5IkSZIkSZIKcgFZkiRJkiRJklSQC8iSJEmSJEmSpIJcQJYkSZIkSZIkFeQCsiRJ\nkiRJkiSpIBeQJUmSJEmSJEkFuYAsSZIkSZIqSkTsHBH7FNj/QETckEVMDWI4OiI2LuL8IyPiviLO\nvyAiLm9edJLU+iKllHUMklT1ImIAcAiwKjAHeBt4IKU0NH98Z6BjSmlMK77naOBnKaV1WqtPSZIk\nqRJExI3A4imlzRrt7wXMTim9mU1kEBGfAheklE5rwrmLAW8Be6aU7m1i/ysCr5Cb67/VomAlqRV4\nB7IktVBEHAdcBowH+gN7A7cAv2lw2i7Af91B0UKnAQNauU9JkiSpYqWUXsly8bgZ9gBmNnXxGCCl\n9A7wKHBgyaKSpCK4gCxJLXcwcElK6cSU0n0ppTtTSqellFYttqOIaBcRCzXl3JTS5JTSS0VHK0mS\nJFWw/DftdgQ2iYi6iJgbESfljz3YsIRFRJwSEZ9GxDoR8XREfBMRj0TEihHRIyJujoivIuKliPhV\ngfcaHBGTImJmRLwdEUcvILbJQHfglAaxza+cxW+BvzfqY7mIuCEiPs7H+0ZEnNqo3U3AnvOLRZLK\nxQVkSWq5rsDH8zq4gAnwVfmJbr+ImAR8C6wTEetFxK0R8WFEzIiI5yNij0b9XhURTzd8n3xfW0TE\nP/PtHomI1UvyU0uSJEmlcRrwAPA8sC6wPlBfE7hxHc4EdAQuBUYAuwE/Aq4BrgMeIfctwQ+AGyJi\nkfqG+cXii8kt8G6T/+/TI+Kg+cS2PfBlPp718rE9V+jEiOiYj//xRof+CiwHDAb6AmcAP2h0zuPA\nUhHx8/nEIkll0SHrACSpBjwHHBYR7wF3pJSmNjp+GrAC0IXc19ACeD9/LAErAcPy500BJgP/S+5r\naxcD3wEbAldGxNyU0vUN2jaeQK8A/Bk4HZgJDAfGAr1b4weVJEmSSi2lNDkippJ7btPTC2wAiwCH\nppQehdwdvsBFwIkppRH5fR8ALwKbAPdExA+Bk4DTUkpn5Pu5L1+z+ISIuCQVeGhUSumfETEHeD+l\n9NQC4voF0B6Y1Gh/H2C3lNKd+dcPF2j7IlAHrAP8awHvI0kl5QKyJLXcwcDNwGiAiHiZ3FfOzkkp\nfdWECXB3YLOUUsOJ4fUNT4iIR8jdSbFf42ONdAPWr3/YRkS0B/4eEaumlF6bV6OIWBT4A7k7KLoA\n04BBKaX35vNekiRJUiWYVb94nPcGuRstHmi0D3J3/gJsQO7O5XH5OXO9B4ATgeWBls6Fl87/+Vmj\n/ROBP0XEEsD9hebcKaW5ETG9QR+SlBlLWEhSC+UXflcDtiN3pwPkJp1P57+2tiAfNFo8JiK6RsT5\n+Tpss4HZwP7Aguoqv93oSc0vkbvjefkFtNsHuDiltEVKqQ+5BwFOiYgOEbFvROwUEZfn68j9174m\n/IySJElSqXzV6PWs/J/T63eklGbn/7O+hMXi5ObJL5Gba9dv95NbfP5RK8RV/17fNdq/C/A0uZIb\n7+TL1W1WoP13DfqQpMy4gCxJrSClNDv/8LzDUkprkKtn9hNgUBOaF6qfPAbYmVxpi/8D1gauZMET\nyOmNXtdPnhfUbnXgoIg4LCI2Sil9nJ9k/xLYLqU0DvgC2HUe+yRJkqRqUl927tfk5toNtz7AP1vx\nPbo23JlS+iilNDCltDi5OspTgFsjoluj9l0b9CFJmbGEhSSVQErpyoj4M9CrKac3fBERPyD3EI8D\nU0qXNdhfkn/0y98l3YXcYnPQ4A6JlNKTEbFP/mVPYExK6YXG+0oRlyRJktq0WZT27tsngG+A5VJK\ndxfZtqmxvUpufr0y8G6hE1JKT0XEqcBjwIrkSsmRL2/REZhnGTpJKhcXkCWphSKiR0rp08b7yC3K\nTsnvKmYC/ANy3xCpv3uY/EM+tiP3II3WNgg4JqU0ZR7H20XEAcDjKaUX5rNPkiRJai2vANtFRD9y\nD6D+MKX0URHtY34HU0pf5Bduz4+Ilcg9yK4d8FNg05TSDguIbZuIuAeYAbyaUppR4D3ejoiPyH2D\n7yGAiOgM3ANcTW5xeBHgSOAj4OUGzfuQm/s/vsCfVJJKzBIWktRy/4qISyNix4j434jYG5gAfE1u\nYgi5SebPI6JfRPwyIpaZV2cppS/J1UQ7KSJ2iIj++f4al6doLZ2BhepfRMTiEfG/DeKZnlK6FFgh\nInab1z5JkiSpFV0M/AO4AniK3MOki5Hmse/f+1NKZ+f77QvcAvwN2J3cYvL8HE1urn9HPra15nPu\n34GtG7yeCbwAHAbcSu5B3F8DW6WUGtZK3gp4KKU0bQGxSFLJRUqFrqmSpKaKiAOBfsAaQHdydx0/\nBpyeUnotf87iwChgE6AbcGpK6bSIGA38LKW0TqM+VwEuJVcT7XPgQnJfYTskpbRk/pzvtS3UV0Ss\nCLwF/CaldNc84u+Wf6/lgG+BR/Kx1zU6bz9g+5TSNvPbJ0mSJCknItYkt8i8fErpkya2aQe8A/w+\npXRdKeOTpKZwAVmSNE8RcQyweErp9xFxPLkF8k8a70spDc00UEmSJKlCRcTtwPMppZOaeP6uwGnA\nao1v6pCkLLiALEmap3w9uI3I1ZBbn1x9tqUb70spzcwoREmSJKmiRcSq5EpUXNDE83cFPkgpPVra\nyCSpaVxAliRJkiRJkiQV5EP0JEmSJEmSJEkFuYAsSZIkSZIkSSrIBWRJkiRJkiRJUkEuIEuSJEmS\nJEmSCnIBWZIkSZIkSZJUkAvIkiRJkiRJkqSCXECWJEmSJEmSJBXkArIkSZIkSZIkqSAXkCVJkiRJ\nkiRJBbmALEmSJEmSJEkqyAVkSZIkSZIkSVJBLiBLkiRJkiRJkgpyAVmSJEmSJEmSVFBFLyBHxM4R\ncWtEvB8RX0XEMxGxWxPadY6I0RExNSKmR8Q1EdG9HDFLkiSp7YiI1SLivoj4OiI+iIhTIyKa0K5J\n89WI6BcRL0TEtxHxYkTsknVf+eN1jba5EbHqgkdMkiRJ1aaiF5CBIcBXwBHAb4D7gb9FxMELaHcj\nsDEwENgH6APcXMI4JUmS1MZERFfgXmAOsB1wKjA0/+eCLHC+GhEbAeOA+4C+wB3AdRGxRZZ95b0M\nrAusl9/WB95uws8tSZKkKhMppaxjmKeI6J5Smtpo37XAeimlnvNosz7wGPC/KaXH8vv6AE8CW6SU\n7i9x2JIkSWoDIuI44ChghZTS1/l9RwMnA0unlGbMo12T5qsRcQ/QPqW0RYO2dwI/TCltnGFfo4Gf\npZTWadEASpIkqSpU9B3IjReP854Hlp1Ps77AlPpJb76fp4HJwNatG6EkSZLasL7APfWLx3ljgY7A\nJgtoN9/5akQsDGwK3NCo7Vhg/Yj4YRZ9SZIkqe2p6AXkedgAeG0+x3sBrxTY/3L+mCRJktQa/mve\nmVJ6D/iG+c87mzJf7QksVOC8l8nN4evrDZe7r3qrR8QXETEzIh6JiI0LtJMkSVINqKoF5IjYHOgH\nnDOf07oB0wvsn5Y/JkmSJLWG5s47m9KuG5AKnDcNiEbnlbMvgOfI1XreFtiD3O8UEyJi7QJtJUmS\nVOU6ZB1AU0XESsC1wM0ppb+WoP/KLQYtSZJUw1JKkXUMarqU0gUNX0fEeOBF4A/ADo3Pd54tSZKU\njdaaZ1fFAnJEdAPGk6u/ttcCTp8GLFFgf7f8sXmq5AcKqroNGDCAq666KuswVKPML5WS+aVSi6jq\nteNpQJcC+xc072zKfLX+7uDG/XdrcDyLvv5LSunbiLiL3B3J8zpnXoekFvOzSqVkfqmUzC+VUmvO\nsyu+hEVELArcCbQHtk0pzVxAk1coXHNuXjXdpJJbaaWVsg5BNcz8UimZX9J8/de8MyKWJ/cQvfnN\nO5syX30TmF3gvNWAufznmSDl7mteUn6Tys7PKpWS+aVSMr9ULSp6ATki2gPjyD34o29K6fMmNBsP\nLB0RGzToZ21gFeCukgQqSZKktmg8sFVELNZg327kHqL30ALazXe+mlKaBTwA7Nyo7a7AEymlr7Lo\nq5D8DR/bAM/M52eWJElSlar0EhaXAFsDhwE9IqJHg2PPpZRmR8QbwAMppf0AUkr/LyImAFdHxNHk\n7oT4E/BwSumBMscvAdC1a9esQ1ANM79USuaXNF9/AQ4Fbo6IYeRuejgZGJ5SmlF/Ugvmq6cDD0TE\nucAt5BZp+wJb1Z9Q7r4iojNwB3AN8AbQAxgCLAOc2cxxlFrEzyqVkvmlUjK/VC0qfQH5/8hNXEcW\nOLYy8C65u6gb30m9C3AucEX+2O3A4aULU5q/NddcM+sQVMPML5WS+SXNW0ppekRsDlwI3AZMB4YD\npzY6tVnz1ZTSYxGxE3AG8DtyzwPZPaV0X4Z9fQd8AhwPLAnMBB4HNk4pPY+UAT+rVErml0rJ/FK1\nCB9okRMRybGQJEkqr4hotadDqzI5z5YkSSq/1pxnV3QNZEmSJEmSJElSdlxAlsrgwQcfzDoE1TDz\nS6VkfkmSKp2fVSol80ulZH6pFGbOmsnBfzm4Vfus9BrIkiRJkiRJkqT5+GbmNxw86mCufedaFp27\naKv2bQ3kPGuzSZIklZ81kGuf82xJkqTSmT5jOgdeeiA3fnQjned05uRfnczh/Q5v1Xm2dyBLkiRJ\nkiRJUhX57IvP2O8v+3Hb57exxOwluHjLi9l/6/1L8l7WQJbKwLpGKiXzS6VkfkmSKp2fVSol80ul\nZH6pOT78/EO2/uPWLHXmUjw55Umu7ns1H5/7cckWj8E7kCVJkiRJkiSpok3+aDKDRg3iwW8fZIXZ\nKzBuh3H037B/Wd7bGsh51maTJEkqP2sg1z7n2ZIkSc338rsvM/jywTwx6wl6zulX7UIAACAASURB\nVOnJhbtdyFZrb7XAdtZAliRJkiRJkqQaNfHNiQy+YjDPpefoNbcXD+71IBv33jiTWKyBLJWBdY1U\nSuaXSsn8kiRVOj+rVErml0rJ/FIhT778JL2P7c1al6/FnDSHJwc8yUt/fimzxWPwDmRJkiRJkiRJ\nytSD/3yQ313zO17r8Bprt1+bFwa/wBorr5F1WIA1kP/N2mySJEnlZw3k2uc8W5Ikad7ueuouDr3h\nUCZ3mMyGP9iQK/e7kp8s/5MW92sNZEmSJEmSJEmqUuMeGceRNx/J+wu9z2adN+P+A+5nxaVWzDqs\ngqyBLJWBdY1USuaXSsn8kiRVOj+rVErml0rJ/Gqbrr73apYZsgy73r4rvXv05sNjPuTek+6t2MVj\n8A5kSZIkSZIkSSqpS+68hJPuPYlpHaax/bLbM+qAUXTv3D3rsJrEGsh51maTVI3e//R9rrz3Sk7a\n/aSsQ5GkZrEGcu1zni1Jktqquro6zr3lXM54+AxmdJjBrsvvysX7XUznxTqX/L2tgSxJAqD/ef15\npv0zrPvMumy19lZZhyNJkiRJUptXV1fHGdefwTlPnsPM9jPZ5yf7MHLQSDou0jHr0JrFGshSGVjX\nSKXwxEtP8OzcZ+n5Rk8G/W1Q1uGoRnn9kiRVOj+rVErml0rJ/Ko9c+bO4bgxx/HDoT/kzKfPZMDq\nA5jxpxlcdvBlVbt4DC4gS1LV2uvKvdjwBxsyYs8RTGk/hUvvujTrkCRJkiRJanNmzZ7F4ZcdTqej\nOzFy4kgO+cUhzDh7Bufvfz4LL7Rw1uG1mDWQ86zNJqma3PDwDex+5+68c9Q7LN9jeQZfOJgb3riB\n6SOm066d/zYoqXpYA7n2Oc+WJEm16puZ33DEFUcw5q0xLFy3MEf1OYoTdzuxIn4vb815tgvIeU5s\nJVWTHkN6sMmymzDu6HFA7l87u/y+C4f94jCGDRiWcXSS1HQuINc+59mSJKnWfPn1lxx02UFc//71\ndJrTiRM2PoEh2w+piIXjeq05z66cn0qqYdY1Umsa/vfhTG8/nasOuQrI5dfCCy3MCeudwLn/Opdv\nZn6TbYCqKV6/JEmVzs8qlZL5pVIyv6rP1C+nstPZO9H9lO7c/c7djNx0JNPOm8bQHYZW1OJxa6vd\nn0ySatCcuXM48ZETOXS1Q+m0aKfvHTt+1+P54dwfMvCigRlFJ0mSJElS7ZkydQrbnrUtPc7owWMf\nPsYVW17BZ+d+xkHbHpR1aGVhCYs8v1onqRr87uLfce3r1/LF8C8K/uvmdQ9ex17j9+K937/Hsosv\nm0GEklQcS1jUPufZkiSpWr37ybsMunQQ9824j+VmL8fw7Yezy8a7ZB1Wk1gDuQSc2EqqdFO/nMqS\npy/JRb+6iAN+fcA8z+t5VE+WXWxZHjn1kTJGJ0nN4wJy7XOeLUmSqs3r77/OoMsG8eh3j7LynJUZ\nufNItl1326zDKoo1kKUqY10jtYbdz9+dpecu/V+Lx43z65qB1/DYd4/x3OvPlTE61SqvX5KkSudn\nlUrJ/FIpmV+VZ9LkSax7wrr89KKf8vE3H3Pvbvfy5jlvVt3icWvrkHUAkqQFe/HtF5kwYwIT9p6w\nwHPXX319+nTowx6j9uCVs18pQ3SSJEmSJFWvZ157hv1G78c/+SdrpDV47LePsf7q62cdVsWwhEWe\nX62TVMl6H9ub9tGe5896vknnv/PxO6w8YmVu7ncz/TboV+LoJKn5LGFR+5xnS5KkSvXopEc54K8H\n8HK7l1kr1mLUvqNY6ydrZR1Wq2jNebZ3IEtShRv/9HgmMYlXD361yW1WXGpF+nXvx/437u8CsiRJ\nkiRJDUx4dgIHXXcQb3Z4k/UWXo8X93uR1VZYLeuwKpY1kKUysK6RWmLg3wby6y6/5ifL/6Tg8Xnl\n15hDxjC1/VRG3DyihNGp1nn9kiRVOj+rVErml0rJ/Cq/Wx+/lZWGrsRWN27F8j9cntcPe53HT3/c\nxeMFcAFZkirYyFtH8ln7z7jm0GuKbtt5sc4cuOqBnPjwicyZO6cE0UmSJEmSVPmue/A6ljtyOfrf\n2p9e3Xrx7tB3eeDkB+i5bM+sQ6sK1kDOszabpEozZ+4cuhzVhYG9BnLBARc0u4+uR3Vl3177NrsP\nSSolayDXPufZkiQpK5fffTnH33M8ny30Gb/p/htGHTCKJbstmXVYZdGa82zvQJakCnX4ZYcTBOcO\nPrfZfXRo34E/bvpH/vL6X5g+Y3orRidJkiRJUmUaeetIFj9icX533+/41fK/4vOTPueWY29pM4vH\nrc0FZKkMrGukYk2fMZ1L37iUs351Fh3az/95pwvKr8P7Hc4Sc5dg95G7t2KEaiu8fkmSKp2fVSol\n80ulZH61rrq6Os664Sy6HNGFox4+iu1W2Y7pp01n7NCxdO3UNevwqtr8VyUkSZnY8/w9WXLukhy6\n3aGt0t/Ve13NVjduxYtvv8jPVvpZq/QpSZIkSVLW6urqOPnakzn3uXOZHbMZ1GsQIwaOYJGFF8k6\ntJphDeQ8a7NJqhSvvvcqq120GnfueCdb99m61fpd67i1mDl3Ji/9+aVW61OSWsoayLXPebYkSSqF\nOXPncMxVx3DxpIsBOGiNgxg2YNgCv8XbVrTmPNsF5DwntpIqxZrHrUldquOFP73Qqv1O/mgyPc/r\nyY2/uZEdN9qxVfuWpOZyAbn2Oc+WJEmtaeasmRx55ZFc8doVLJQWYshaQzh1z1Np185KvQ35ED2p\nyljXSE014dkJvJBe4PqDrm9ym6bm18rLrMwOS+zA/uP2p66urpkRqq3x+iVJqnR+VqmUzC+VkvlV\nnBnfzmDf8/el83GdufaVazllvVP4cviXnL736S4el5ijK0kVZJ9r9mHLTluy2gqrlaT/qw+9mhnt\nZ3D62NNL0r8kSZIkSa1p+ozp7D58d7qe2JXb3rqNszc+my/O+4LjdjnOheMysYRFnl+tk5S14X8f\nzrGPH8unp3xa0ifEnnTNSQx7bhjTzpxGx0U6lux9JKkpLGFR+5xnS5Kk5vjsi8/Y7y/7cdvnt7HE\n7CU4fcvT2X/r/bMOq2pYA7kEnNhKytKs2bPockwXDlnjEM4eeHZJ36uuro4eR/Zgs+U248ajbyzp\ne0nSgriAXPucZ0uSpGJ8+PmHDLxkIP/46h8sM3sZztnuHHbfdPesw6o61kCWqox1jbQgAy8ayA/q\nfsCwAcOKbltsfrVr145RO43ips9uYvJHk4t+P7UtXr8kSZXOzyqVkvmlUjK/vm/yR5PZ7NTNWP7s\n5Xll2ivctN1NfDDiAxePK4ALyJKUsXc/eZfrPrqOS7a/pGz1m3bcaEd+mn7KzhfsXJb3kyRJkiSp\nkJfffZkNTtyAniN78t5X7zF+p/G8Pfxt+m/YP+vQlGcJizy/WicpK32O78OXs77k1bNfLev7Tpo8\nid6X9uaene/h/375f2V9b0mqZwmL2uc8W5IkFTLxzYkMvmIwz6Xn6DW3F6N+O4qN1tgo67BqRmvO\nszu0RieSpOa57/n7eHbus0w8cGLZ33uNlddgy05bss81+/DhLz8s+/tLkiRJktqeJ156ggOuPoBJ\nMYlf8AueHPAkfX7aJ+uwNB+WsJDKwLpGmpe9rt6LLTttSe9Veje7j5bk19gjxvJp+08ZcfOIZveh\n2ub1S5JU6fysUimZXyqltpZf90+8n15H92LDv27IIu0X4YX9X+D5s5538bgKuIAsSRk5+6az+az9\nZ4w9YmxmMXTt1JVDeh3CCQ+fwKzZszKLQ5IkSZJUm+566i56HtWTLcZuwZIdl+TVg1/lqT8+xRor\nr5F1aGoiayDnWZtNUjnNmj2LLsd04ZA1DuHsgWdnGktdXR1dj+zKTj134spDr8w0FkltjzWQa5/z\nbEmS2qZxj4zjyJuP5P2F3mfzTptz+f6Xs+JSK2YdVpvRmvNs70CWpAzse+G+/KDuBwwbMCzrUGjX\nrh0jfz2SMe+N4cPPrYUsScWIiNUi4r6I+DoiPoiIUyNigRP1iOgcEaMjYmpETI+IayKie4Hz+kXE\nCxHxbUS8GBG7VEJfjfqsi4inFvQzS5KktmHMhDEsM2QZdr19V3r36M2Hx3zIhBMnuHhcxVxAlsqg\nrdU10vy9+8m7jJ0ylku2v4R27Vp+GW6N/Np3y31ZqW4ldjxvxxb3pdri9Uuat4joCtwLzAG2A04F\nhub/XJAbgY2BgcA+QB/g5kb9bwSMA+4D+gJ3ANdFxBZZ9tWgzx8AI4ApTfh5pZLxs0qlZH6plGot\nvy658xKWGLIEg/4xiA2X3ZBPT/iUO467g6W7L511aGqhDlkHIEltzY4jd+THdT9m9013zzqU77nx\ngBtZ+4q1ue/5+9j8fzbPOhxJqgYHAosAO6SUvgbui4guwMkR8eeU0oxCjSJifeD/gP9NKT2W3/ch\n8GREbJZSuj9/6onAQymlIfnXD0XEGsBJ5Baus+qr3u+B94E3AYsYSpLUBtXV1XHuLedyxsNnMKPD\nDHZdcVcu3u9iOi/WOevQ1IqsgZxnbTZJ5TDh2QlsdeNWTNx/Ir1X6Z11OP9l6z9uzfOfPc+Uc72Z\nTFJ5VHMN5Ih4CPggpbRHg30/At4BfpNSunMe7U4F9kspLdto/5vA31NKR0fEwsBXwKEppVENztkb\nuBLonlL6qtx9Ndi3AjCJ3N3KhwM/SymtM4+f13m2JEk1pq6ujtPHns45T53Dd+2/Y59V9mHkoJF0\nXKRj1qEpzxrIklSlfnvNb9my05YVuXgMcP0R1zO13VTOGHtG1qFIUjXoBbzScEdK6T3gm/yxJrfL\ne7lBu57AQgXOe5ncHH7VjPqqNxwYm1KaWOB8SZJUo+bMncMxVx1Dp6GdOOuZs9h39X2Z8acZXHbw\nZS4e1zAXkKUyqLW6Rmqes286m8/af8bYI8a2ar+tmV+dF+vMsf9zLKc/fTozvi34zWu1MV6/pPnq\nBkwvsH9a/lhL2nUDUoHzpgHR6Lxy9kVEbAZsAfyhwLlS2flZpVIyv1RK1ZRfs2bP4rBRh9Hp6E5c\n8M8LOHzNw5lx9gzO3/98Fl5o4azDU4lZA1mSyuCbmd9w4mMncsQaR9C1U9esw5mvU/Y4hb88+xf2\nHLkntx57a9bhSJIqSES0B0YCZ6SUPmtquwEDBrDSSisB0LVrV9Zcc0023XRT4D+/PPva1819PXHi\nxIqKx9e19dr88nVbz6911luHw684nNEPjWahuoX4w45/4IRdT+Dhhx/m0UcezTw+X//n9cSJE5k+\nPXcvwNtvv01rsgZynrXZJJXSDn/egQc/fJDPRnxGu3btsg5ngcY/PZ5tbtqGFw54gTVW9rlIkkqn\nymsgfwxcmFI6vdH+GcDJKaXh82h3PbBESmnzRvvvAFJK6TcRsRrwIrBJSumRBuesDTwF9EkpPZtB\nXwcCxwK/BGaTu4P5InIlLjYDvk4pzWnU3nm2JElV5suvv+Sgyw7i+vevp9OcTpyw8QkM2X5IVfw+\nqxxrIEtSFXnx7Re5ZeotXLXbVVXzYbt1n635ZftfssPFO2QdiiRVsldoVBs4IpYHOlK4lvA82+U1\nrEH8JrkF2sbnrQbMBV7LqK9VgeWBT8iVtpgK7A78T/6/dynQXpIkVYmpX05lp7N3ovsp3bnnnXsY\nuelIpp03jaE7DK2a32fV+vw/L5VB/VcL1Db1v6g/a7Vbi+3W264k/Zcqv24+4mbejDcZM2FMSfpX\ndfD6Jc3XeGCriFiswb7dyD1E76EFtFs6Ijao35G/G3gV4C6AlNIs4AFg50ZtdwWeSCl9lUVfwAXA\nr4BNG2z3AK/m/3vCfH5uqST8rFIpmV8qpUrKrylTp7DtWdvS44wePPbhY1yx5RV8eu6nHLTtQVmH\npgpgDWRJKqHR/xjNm/Em7wx5J+tQirZ8j+XZd4V9OeSuQ9hzsz3p0N6PDElq5C/AocDNETEM6Amc\nDAxPKf37SaQR8QbwQEppP4CU0v+LiAnA1RFxNLkH3P0JeDil9ECD/k8HHoiIc4FbgG2AvsBW9SeU\nu6+U0lvAWw0HISL2BRZvWB5DkiRVh3c/eZdBlw7ivhn3sfzs5blu++vYZWO/UKTvswZynrXZJLW2\nOXPn0PWoruz+49257ODLsg6nWep/hj1+vAejDh6VdTiSalA110AGiIhewIXA+sB04DLg1IYTy4h4\ni9wC8qAG+zoD5wL9yX0r8Hbg8JTS1Eb9bwecAfwEmEyutvKNjc4pe1+N2owGfpZSWmcex51nS5JU\nYV5//3UGXTaIR797lJXnrMzInUey7brbZh2WWlFrzrNdQM5zYiuptQ04fwA3Tb6JaedMq+q7d6+4\n5wr2v29/3jvmPZZdfNmsw5FUY6p9AVkL5jxbkqTKMWnyJAZePpBn6p5h1TmrcvGeF7PZmptlHZZK\nwIfoSVWmkuoaqTze+fgd/vrBX7lk20tKvnhc6vwatNUgVq5bmf7n9i/p+6gyef2SJFU6P6tUSuaX\nSqmc+fXMa8/wP8f9D71H9Wbm3Jk8tvdjvHL2Ky4eq0lcQJakEuh3Xj9WTauy1+Z7ZR1Kqxh34Die\nnvM0E5712UiSJEmSVC0enfQoq/9+ddYZvQ7toz3PDX6OF/70Auuvvn7WoamKVHwJi4joCfweWA/4\nGbmHeMz3n0ciYkVydd0aG5tS2mMebfxqnaRWcfNjN7Pj7Tvy4kEvstoKq2UdTqvZ5sxteObTZ/j4\n3I+zDkVSDbGERe1zni1JUvlNeHYCB113EG92eJP1FlqPK/a7oqZ+P9WCtbUSFj8j94ToV4BXi2x7\nJLmF5/rthNYNTZK+r66ujoE3DmSnJXaquQ/n64dcz7R20zjl2lOyDkWSJEmSVMCtj9/KSkNXYqsb\nt+JHP/wRbx7+Jo+f/njN/X6q8qr4BeSU0m0ppRVTSrsCLxXZ/LWU0lMNtrdKEaO0INbNajuGXjmU\nmTGTqw+7umzvWa786rRoJ05d91T++Pwfmfrl1LK8p7Ln9UuSVOn8rFIpmV8qpdbMr2vvv5bljlyO\n/rf2p1e3Xrw79F3uP/l+Vl5m5VZ7D7VdFb+ALEnV4pNpn3DBqxfw51/9mUUWXiTrcEriuF2OY6m6\npdhhxA5ZhyJJkiRJbd7ld1/OUkOW4rd3/5Z1llqHKcdO4e4T7mb5HstnHZpqSMXXQG4oIm4EFi+i\nBvKnwOLAJ8B1wPEppZnzaGNtNkktstFJG/H+1+/z9vC3sw6lpJ58+UnWH7M+9+52r0/sldRi1kCu\nfc6zJUlqfSNvHclpD5zGFwt9wc5L78wlB1xC105dsw5LFaQ159kdWqOTCvQdcCHwD+BLYFPgWGAV\noH92YUmqVfdPvJ/HZz3OUwc8lXUoJbfuauvS94d92W3MbkzpPYV27fwyiyRJkiSVWl1dHcPGDeNP\nj/+Jb9p/w16r7MUF+11Ap0U7ZR2aalxN/tafUpqSUjospXRHSunhlNJp5B6ot11E/Dzr+NT2WDer\nttXV1bH7mN3ZstOWrL3q2mV//yzy64Yjb+CL+IITrvHZpLXO65ckqdL5WaVSMr9USk3Nr7q6Ok78\n64l0HtqZU/7fKezZa0++OusrRh822sVjlUWt3oFcyDjgYuCXwL8KnTBgwABWWmklALp27cqaa67J\npptuCvznL7Wvfd2c1xMnTqyoeHzduq/3Pn5vpr4/lXFnjsvk/bPKrzM3OpNjHj+Gde9Yly6dulTM\n/w9ft+5rr1++bu3XEydOZPr06QC8/fbbSJIkqbA5c+dwzFXHcPGkiwE4aI2DGDZgGB3at6XlPFWC\nmqyBPI+2i5OribxvSmlMgePWZpNUtE+mfcKyZy7LsPWHMXSHoVmHU3YrDl2RHy32Ix497dGsQ5FU\npayBXPucZ0uSVJyZs2Zy5JVHcsVrV7BQWoghaw3h1D1PtXygimIN5ObZGUjAs1kHIql29BvRjx/V\n/ahNLh4D3HTATaxz5Trc88w9bLX2VlmHI0mSJElVa8a3Mzj0skO59p1rWXTuopyywSkcs9MxLhwr\ncxWfgRGxaETsGBE7AcsBPfKvd4yIRfLnvBERlzVoc3JEnBMR/SNi84g4DRgB3JRSmpTNT6K2rP4r\nvKott/2/23hy9pPcevCtmcaRZX6tvera/Kbrb9jzmj2pq6vLLA6VjtcvSVKl87NKpWR+qZTq82v6\njOnsNnw3up7Uldveuo3hmwzni/O+4LhdjnPxWBWhGu5AXhK4kdzdw/VuyP+5MvAuuYXwhn+jXgGG\nAoOARfPnDAPOLHWwktqGuro6fjv2t+y43I70XqV31uFk6rojrqP7H7pz9OijGT5oeNbhSJIkSVJV\n+GLGF2z/p+25fertLDF7Cf6y1V8Y3Hdw1mFJ/6WqaiCXkrXZJBXjwEsOZMxrY5j+5+ksvNDCWYeT\nuQtuu4Ahjwzh/ePeZ+nuS2cdjqQqYg3k2uc8W5Kk73v/0/cZfOlgJnw1gaVnL805253D7pvunnVY\nqjGtOc92ATnPia2kpnrn43dYZfgqXL7F5ey75b5Zh1MxVhm6Cj0W7cGTZzyZdSiSqogLyLXPebYk\nSTmTP5rMwFEDeejbh1hh9gqM3HEk/Tbol3VYqlGtOc+2kIpUBtbNqi3bjtiWXtGrYhaPKyW/bjn4\nFp6e8zR3PHlH1qGoFVVKfkmSNC9+VqmUzC+1hpfffZkNTtyAniN78sGMDxi/03jeHv42XWZ1yTo0\nqUmqoQayJFWMq++9mpd4iTeOeCPrUCpO71V60797f/a+bm8+7/O5D3uQJEmS1KZNfHMig68YzHPp\nOVarW42H936YjdbYKOuwpKJZwiLPr9ZJWpBZs2fR7ffd2OPHe3DZwZdlHU5FmjlrJt2O6cbAnw7k\not9dlHU4kqqAJSxqn/NsSVJb88RLT3DA1QcwKSbxC37BqAGj6PPTPlmHpTbGEhaSlIF9zt+HDqkD\nlx54adahVKxFFl6EC7a6gL+89RcmfzQ563AkSZIkqWzun3g/Pz36p2z41w1ZtMOivLD/Czx/1vMu\nHqvquYAslYF1s6rfpMmTuP7T67lq56sqrjRDpeXX4L6DWT1W59fn/jrrUNQKKi2/JElqzM8qlZL5\npaa448k76HlUT7YYuwVLdVyKVw9+lSfPeJI1Vl5jvu3ML1WLyloFkaQK1e+ifvyy/S/pv2H/rEOp\nCncOvZPXeI1L7/JubUmSJEm1adwj4/jRkT9iu79vxypdVuHtI9/m4VMf5ifL/yTr0KRWZQ3kPGuz\nSZqXkbeOZOijQ3n/uPdZuvvSWYdTNYZcPoRLXrqEqWdOpeMiHbMOR1KFsgZy7XOeLUmqNWMmjOHY\nu47lk4U+YetuW3P5AZf7u6IqTmvOs11AznNiK6mQL7/+kh4n9uCwNQ7j7IFnZx1OVamrq2PpoUuz\n1uJrcfcJd2cdjqQK5QJy7XOeLUmqFZfceQkn3nsi0ztMp/+S/bn0gEvp3rl71mFJBfkQPanKWNeo\nem1/zvZ0S90YNmBY1qHMU6XmV7t27Ri37zj+MeMfPPjPB7MOR81UqfklSVI9P6tUSuaX6urqGP73\n4XQ7ohuHPXAYfVfsy9RTpnLj0Te2ePHY/FK16JB1AJJUqcY/PZ4Hv32QJ/Z/ouIenFctNu69MX1v\n78tOV+3EJ8M/cRwlSZIkVYW6ujpOH3s65zx1Dt+1/44BPxnAeYPOszyf2iRLWOT51TpJDc2ZO4cl\nhi7BZstuxt9///esw6lq38z8hsX/sDj79dqP8/c/P+twJFUYS1jUPufZkqRqMmfuHI7/6/Fc8M8L\nqIs6DljtAM4ecDYLL7Rw1qFJRbEGcgk4sZXU0IDzB3Dj5BuZ9udpThRawajxozjwgQN5a+hbrLjU\nilmHI6mCuIBc+5xnS5KqwazZszhq9FGMemUU7VI7Dl/zcE7f63Q6tPfL+6pO1kCWqox1jarLpMmT\nuPqDq7ly+yurYvG4GvJr/633p1f04tcjfp11KCpSNeSXJKlt87NKpWR+1b5vZn7DfhftR6djOzH6\npdH8oc8fmDF8Bmftc1bJF4/NL1ULF5AlqZFtL9yWPgv1YddNds06lJpy15F38Up6hVHjR2UdiiRJ\nkqQ27suvv2Sv8/aiywldGPf6OM7a8Cy+GPEFJ+1+ks9ukRqxhEWeX62TBHDadadx+rOn89GJH7FE\nlyWyDqfmHH7Z4Vz68qVMPXOqD5+QBFjCoi1wni1JqiRTv5zK/pfuzy2f3EK3Od04bYvTOHCbA7MO\nS2p11kAuASe2kj6Z9gnL/XE5TulzCsfvenzW4dSkuro6lhq6FGsvsTbjjx+fdTiSKoALyLXPebYk\nqRJMmTqFwZcOZvy08Sz5/9m78zidyv+P469rLE227CJZsn2lNInsma8lUVlK5EtZs1SWrJHQSFlC\nSmJQUSpa9JWyRcgkkSSSLUbyjSzTINMY9/X7Y279Jo39vue6l/fz8ZgH59znnPt9T1dzLp8553NO\nFWRU41G0a9DOdSwRv1EPZJEgo75GweHuF+6mqC0adMXjYBpfERERfNDhAxYfW8yqTatcx5GLEEzj\nS0REwpPOVeJPGl/Bb+/BvdSPqU+R0UXY9Nsm5tw7h/9N+F9AFI81viRY6FGSIiLA7OWz+SblG77v\n8b3rKCHvjop30PDjhjR/rTm/jf9N/cVERERERMTnduzbQcdpHYn7M46SKSVZ0HIBjW/XQ71FLoda\nWHjp1jqR8JWUnES+gflodUMrXuvxmus4YSEpOYm8A/PSpnQbpj02zXUcEXFILSxCn+bZIiKSkTbv\n3kzH6R1Z71lP2ZSyTG4zmbpRdV3HEslwamEhIuJDrSe0JqvNyvTHpruOEjYis0Yyo+kMZuydwebd\nm13HERERERGRILdu2zqiBkVRMbYiSaeTiHsojh/H/qjisYgPqIAskgHU1yhwxW2J479H/8u7bd8N\n2lYKwTq+Wke3pmrWqjR6uZHrKHIewTq+REQkfOhcJf6k8RX4Vm1axY0DEXGJKgAAIABJREFUbqTq\nG1XJHJGZDZ03sGnUJqrfWN11tAvS+JJgEZzVEhERH/B4PNw3/T7qZa9Hw8oNXccJS5/0/4QD5gCD\nZg5yHUVERERERILI4vWLKdOvDNFvR5MnMg9bum9h/cj1RJWKch1NJOSoB7KXerOJhJ/HpjzGjO0z\nODTyEDmuzuE6Tth6ef7L9P6iNz/1+4nihYq7jiMiGUw9kEOf5tkiIuJL8+Lm8cSHT7A3y16ir45m\nRpcZlCxc0nUskYDjy3m2CshemtiKhJcte7Zw86s383r912nXoJ3rOGHvpoE3keJJ4cexP7qOIiIZ\nTAXk0Kd5toiI+MLs5bPp/3F/DmQ9wJ257mRGtxkUyVfEdSyRgKWH6IkEGfU1CjyNXmpElSxVQqJ4\nHArja1G/RexkJ+M+HOc6ipwlFMaXiIiENp2rxJ80vtybvmg6hZ4oxMOLHqbqtVX535P/Y+FTC0Oi\neKzxJcFCBWQRCTsD3xjIr+ZXFg5Y6DqKeBUtUJQhUUN48ssnOfT7IddxRERERETEsYn/nUje3nnp\ntqwbdYvW5fDQw8wbOI+CeQq6jiYSdtTCwku31omEh137d1H2xbK8fMfLPHrPo67jyFlK9C1B/sj8\nrB+53nUUEckgamER+jTPFhGRi+XxeBj9/mhGfTmKPzL9QdvibXmlyytki8zmOppI0FEPZD/QxFYk\nPJTuV5rsmbPz3ajvXEeRdGzdu5WbXrmJ1+q/FhLtRUTkwlRADn2aZ4uIyIV4PB6GzR7GhA0TOGVO\n0alsJ8Z3HE9k1kjX0USClnogiwQZ9TUKDDHvxBBPPIsHLHYdxadCaXyVL1aebiW70W1hN46fPO46\njhBa40tEREKTzlXiTxpf/pVyOoU+M/qQvW92xm0YR/ebu3Ni7Akmd5scFsVjjS8JFiogi0hY2Pfb\nPmI2xDCy2kiuzXut6zhyHi93eZlcNhfNxjZzHUVERERERPwgKTmJ7q92J3v/7MRujqX/bf05Pu44\nYzuOJXOmzK7jichZ1MLCS7fWiYS2CgMrkOJJYdvYba6jyEVYu3Ut1WdWZ37z+dxT9R7XcUTEj9TC\nIvRpni0iImccP3mcx6c9zuz42WQ7nY1BNQcx4P4BRETo+kYRX/PlPFu/1hGRkPfiRy/yo+dHfur3\nk+socpGqlq9KqwKtePDdBzlS6QhZs2R1HUlERERERC5TwvEEuk3txvu/vs81p65hfN3x9GjSw3Us\nEblI+hWPSAZQXyN3Dv1+iP5f9GdwxcEUL1TcdRy/CNXx9WbvN8lqs9JsjFpZuBSq40vEV4wx5Y0x\ny4wxJ4wxvxhjnjHGXPBKD2NMLmPM68aYI8aYBGPMW8aYvOls19QYs8kYc9IYs8UY09L1sYwxw73H\n+d0Yk2iMWZfesUQyis5V4k8aX1fm4NGDNBvVjHwx+fh83+dMqTeFwy8eVvHYS+NLgoUKyCIS0u4a\nfRfXcR0jHhrhOopcosyZMjO/83wWHVvE/K/mu44jIvIPxpjcwGdACtAEeAbo6/3zQt4D7gA6Au2A\nKsC8s45fC3gfWAbcBSwA3jHG1Hd5LCAn8DrQErgP+AZ41xhz30V8bhERCQP7ftvHXc/eReFRhVl/\ncD1vNX6LAxMO0Pmuzq6jichlUA9kL/VmEwk90xdNp+vyrvzQ4wfKXV/OdRy5TG0mtOGjvR9xePTh\nsHgSs0i4CeYeyMaYQUA/oJi19oR3XX9gGHCttfb4OfarDsQBta21cd51VYC1QH1r7XLvusVAJmtt\n/TT7fgLktNbe4epY5/hMq4FD1tp/3DaiebaISPjY/b/ddIztyMqTKyl2qhgT759I0xpNXccSCUu+\nnGfrCmQRCUkJxxN4bMlj9CzbU8XjIPdmrzeJtJE0HaOJp4gEnLuAxWeKx17vAtmAOhfY79czRVoA\na+06YDfQCMAYkxWIBuaete+7QHVjTE4XxzqPw4Aa1ouIhKmte7dS4+kalJpYil+O/8LCFgvZM26P\nisciIUIFZJEMoL5GGa/hqIbkJz/jOo5zHcXvQn18RUREsKDLApYeX8oHqz9wHSfshPr4ErlC/wJ+\nTLvCWvsz8If3tYvez2trmv1KAVnS2W4rqXP4so6O9RdjTCZjzDXGmDZAA+DVdPYV8Tudq8SfNL7O\nb+Oujdw2+DYqvFqB35N/Z1XbVWwfu52GlRu6jhYUNL4kWGR2HUBExNemfjqV9cnr2dx7MxER+j1Z\nKKh+Y3UeKvwQD33wEI0qNyJbZDbXkUREAPIACemsP+p97XL2K5lmG5vOdkcBk+b4GX0sAIwxVYE1\n3sVTwOPW2o/T2VdERELQmh/W0GVmF7ZEbOEWbuHrDl9TuWxl17FExE9UQBbJANHR0a4jhI2DRw/S\nY1kPBlQcQPli5V3HyRDhMr5e7/E6n/T9hHvH3Muyoctcxwkb4TK+ROSSbQIqA7mBu4FXjDGJ1to5\n6W3cvn17SpQoAUDu3LmJior66+fLmauvtKzlK1k+I1DyaDm0ls8IlDwulzfs3MDUbVPZkXkH5faX\nY0bjGXRo3SFg8gXj8hmBkkfLwbu8ceNGEhJSrwXYs2cPvqSH6Hnp4R4ioaHikxU5kXKCXS/sch1F\n/GDdtnVUfb0q7zZ+l5Z3tHQdR0R8IMgfoncAmGStHXHW+uPAMGttun2UjDFzgPzW2npnrV8AWGvt\nvcaY8sAWoI619os021QGvgaqWGu/yehjned7MQOoZ60tkc5rmmeLiAS5BWsX0HNuT/Zk2UPtyNpM\n7zydMkXLuI4lIuehh+iJBJmzf7Mo/jH2g7H8cPoHPuv7mesoGSqcxleVclVof1172s9rz/GTx13H\nCQvhNL5ELsOPnNUb2BhTlNSH6KXXS/ic+3ml7UG8i9TWEGdvVx44DWx3dKxz2QBcb4zRvy8kw+lc\nJf4U7uNr7qq5XN/nepp82ITSuUuzp88eVg5fqeKxj4T7+JLgoQmeiISEvQf3MuirQQy/bTglC5e8\n8A4StKY/Np0c5OCe0fe4jiIishBoaIzJnmbdg6Q+RG/lBfa71hhT48wK79XANwCfAlhrk4HPgQfO\n2rcVsMZae8zFsc6jFrDPWuu5wHYiIhIEZi6dSeEnCtN6QWuiCkSxf+B+ljy9hGIFi7mOJiIOqIWF\nl26tEwluZfuXJZPJxNYxW11HkQywfvt6bp9xO282fJM2ddu4jiMiVyDIW1jkJrU1xBZgNFAKGAeM\nt9YOS7PdTuBza+0jadYtAkoD/Ul9wN0o4FdrbXSabWqSWvh9BfiI1F7DfYCG1tplLo5ljCkGvAa8\nS+qVzTmA+4CHgW7W2mnpfJ80zxYRCRKTF0xm6LKhJGROoHnB5kztOpW8ufK6jiUil8GX82w9RE9E\ngt7Qt4ay2+4m/sl411Ekg1QuW5kuxbvQeUFn7q16L7my53IdSUTCkLU2wRhTD5gEzAcSSC0gP3PW\nphH8886/lsAEYIb3tY+BXmcdP84Y0wJ4FugG7AZapy34OjhWAvALMAgo7F3+AWhsrV2MiIgEHY/H\nw7h543jui+c4nvk4rYu3ZnLXyeS4OofraCISIHQFspeujBB/WrFixV9PxhTf2rFvB/966V+Mrzme\nXk17XXiHEBSu48vj8VCkbxFK5SxFXEyc6zghK1zHl2ScYL4CWS6O5tnibzpXiT+F8vjyeDyMeHcE\nL3z9AsmZkml3Qzte7PQi2SKzuY4WNkJ5fIl7eoieiIhX/Qn1uSXTLWFbPA5nERERLHp8EV/9+RWx\nC2NdxxERERERCQopp1MY+MZAcvTNwfPrn6djhY4cG3WM2MdiVTwWkXTpCmQvXRkhEnyemP4Ek7dO\n5n/D/qe+XGFswOsDmLB5Aj8/9TPX5r3WdRwRuUS6Ajn0aZ4tIhIYkk8l0+/1fsT+GEsmm4meUT0Z\n0XYEmTOpu6lIKPLlPFsFZC9NbEWCy6afNhE1NYppdafRqWEn13HEsTL9ypA5IrMeoigShFRADn2a\nZ4uIuPVH0h/0mtGLmT/N5KrTV9G/an+GtBpCRIRuShcJZWphIRJkVqxY4TpCSPF4PDR8uSHVr6qu\n4jEaXwCfD/ycnZ6dPP3m066jhByNLxERCXQ6V4k/BfP4SjyRSJsJbbhmyDW8v+N9RtUaxe/jf2do\n66EqHgeIYB5fEl50n4KIBJ1OkzqRQAILBy50HUUCRNECRZlQZwK9vujFg3sepEKJCq4jiYiIiIg4\ncSTxCF2mduGjgx+RJyUPL9V/ie53d3cdS0SCmFpYeOnWOpHgsOzbZTSY04AP7v2A5jWbu44jAaba\nkGrEn4jnl3G/6KoKkSChFhahT/NsEZGM8euRX+k0pROLEhZR8FRBRt89mofrP+w6log4oh7IfqCJ\nrUjg+yPpDwoOKsidhe/kwwEfuo4jASjxRCKFhhSiVclWvNHzDddxROQiqIAc+jTPFhHxr70H99Jx\nSkeWn1hO0VNFGd98PC1qt3AdS0QcUw9kkSCjvka+0XhUYyJtJHP7znUdJaBofP2/XNlz8XaLt5m1\nfxbLNy53HSckaHyJiEig07lK/CmQx9eOfTuoPaw2JcaXYHfibhbcv4C94/eqeBxEAnl8iaSlHsgi\nEhRiF8byxckvWPfYOjJn0o8uObfmNZvT5IsmNH2jKb+N+Y3IrJGuI4mIiIiI+MymnzbReUZn1p9e\nTzlPOT77z2fUjarrOpaIhDC1sPDSrXUigWv/4f0Uf744fW7qw+j2o13HkSCQcjqFgn0LUjlfZZY8\nvcR1HBE5D7WwCH2aZ4uI+Ma6bet45I1H2MQmbrI3MfXhqVS/sbrrWCISoNQD2Q80sRUJXGX7l8Vg\n2DZ2m+soEkTWbl1L9TeqM+vOWbSt19Z1HBE5BxWQQ5/m2SIiV2bVplV0e6sbP2b6kUoRlZjecTpR\npaJcxxKRAKceyCJBRn2NLl//1/qz27OblYNWuo4SsDS+0le1fFW6l+xOp086cSTxiOs4QUvjS0RE\nAp3OVeJPLsfX4vWLKdOvDNFvR5MnMg9bH93K+pHrVTwOIfr5JcFCBWQRCVjrt69n3LZxTLlzCtfm\nvdZ1HAlCr3R7hWvNtfz7uX+7jiIiIiIiclHmxc2jRN8SNHq/EdfnvJ5dvXYRFxNHuevLuY4mImFK\nLSy8dGudSGA508M2Kk8Uy4ctdx1Hgtju/+2mzPgyDIkawvA2w13HEZGzqIVF6NM8W0Tk4sxePpv+\nH/fnQNYD3JnrTmZ0m0GRfEVcxxKRIBVULSyMMZmMMTm9f69pjKnq7/cUkeDXYmwLTnGKTwd96jqK\nBLmShUsyofYERnw3go27NrqOIyIiIiLyN9MXTafgEwV5eOHDVCtcjQODD7DwqYUqHotIwMiIFhav\nAW8bY1YCjYF7MuA9RQKK+hpdmve/eJ/5CfP5uMPHRGaNdB0n4Gl8XViPJj2ona029V6uR8rpFNdx\ngorGl4iIBDqdq8Sf/DW+PB4PE/87kby989Lts27Uv74+h4cd5sMBH5L/mvx+eU8JPPr5JcEiIwrI\n71lr7wX+DSwFlmTAe4pIkEo4nkDbeW3pWLQj0bdEu44jIWTR4EWc4hRNRjVxHUVEAogx5l9p/n69\nyywiIhL6PB4PI+eMJE+fPPRf1Z9mpZqR+Gwib/d5m9w5cruOJyKSLr/3QDbG/BswwOeX0/zMGFMK\nGABUAyoAq6y1dS9iv1zARKApqYXyBUBPa+2Rc2yv3mwiAaDikxU5knyEvS/sJSJCz/kU34rbEkft\nWbWZUW8GHe7s4DqOiOCuB7IxZjKwEchlrX3Bu64kcKO19pOMzhPKNM8WEUktHD89+2kmbphIikmh\nU9lOjOs4Tndciojf+HKendkXB7mANsAJYKAxJonUQvKLl7B/BeAu4CsuLe97QGmgI2CBMcA8oM4l\nHENEMtCgmYPYmrKVnQN3qngsflGzQk2eKPsEXRZ3ocGtDShaoKjrSCLiZ8aYttbat9J5aTipd8i1\nM8Y0AhKBr4FcgArIIiLiEymnUxjwxgBe3fwqBsNjNz/G8+2eJ3OmjCjHiIj4RkZUaD6x1vay1jYE\nHgTWXMrO1tr51tri1tpWwA8Xs48xpjrQAHjYWvuRtfa/QFugtjHmglcvi/ia+hpd2Jof1jD6h9G8\nWv9Vihcq7jpOUNH4ujTjOo2jdKbS3DHqDtdRgoLGl4SAp8880Dkta+1Ba+0c4BlrbT2gHbAOSK/Y\nLCIBTOcq8afLHV9JyUl0f7U72ftnJ3ZzLP1v68/xcccZ23GsisfyF/38kmCREQXkHMaYh4wxuay1\nJ621azPgPe8CfrXWxp1ZYa1dB+wGGmXA+4vIJUhKTqJhbEMa5mpI57s6u44jYWDloJX8Yn+h2+Ru\nrqOIiP9lAR4zxjRL70Vr7RLvn4nW2s+stVsyNJ2IiISU4yeP0/6l9uQclJN3tr3DM9WfIXFcIjFt\nY3SXpYgErSvugXye2wLPvD4SOArUBK4BvrbWPnmZ7/UekO9CPZCNMXOAAmdvZ4xZAFjvQ/3O3ke9\n2UQcqT2sNj/8/gMHxh3Qb+Mlw8xdNZcHP32Qpa2WUu/Weq7jiIQtf/dANsY0sdbO9z5XozmwJu1F\nBuJ/mmeLSDhIOJ5At6ndeP/X97nm1DUMrzucHk16uI4lImEs0HogP22M+a+19tg5Xv8YuMpa+4Ix\nxgAlffCeF5IHSEhn/dEMen8RuUjjPhzHlye/5Lte36l4LBmq5R0tmfPVHJrMasKBfx0gx9U5XEcS\nET+w1s73/rkLeMEYU9UY0xeYb63d4TadiIgEu4NHD9Jlahc+PvIx+U/lZ0rDKbqrUkRCji/un7jQ\nbYFfWWtXev9urbU/+eA9RYKK+hqlb8ueLQz4agAjq4zkppI3uY4TtDS+Lt97/d4jBzmoN1JXIJ+L\nxpcEO2NMibTL1tq11tpxQHljTE9jTH4nwUTEZ3SuEn861/ja99s+Gj7bkGtHXcv6g+t5q/FbHJhw\nQMVjuST6+SXBwheX+/U+c1ugMaYfgXFb4FEgvX8M5PG+lq727dtTokQJAHLnzk1UVBTR0dHA//9P\nrWUtX87yxo0bAypPICyfPn2a1gtac3uO26lWoBorVqwIqHzBtKzxdfnLERERjKk2hg4fdmDUe6N4\n8oEnAypfICxrfGnZ18sbN24kISH1RrE9e/aQAYYCHc8sGGOuAfICvwApwExjzBfABGvtnxkRSERE\ngtfu/+2mY2xHVp5cSbFTxZh3/zya1mjqOpaIiF9dcQ/kfxzQmKpALfxwW+Al9EB+Buhsrb3urPU7\ngXnW2v7p7KPebCIZ6N7n72XFgRUcGHWAbJHZXMeRMDf2g7E8ufZJvu32LRVvqOg6jkhYyYAeyElA\nPKlF49z8/Q48Q2oR+Qiw1lqrCoAfaJ4tIqFg696tdJzWkbWn1lL6dGleefAVGtzWwHUsEZFz8uU8\nO+LCm1wwTIm0ywFyW+BC4FpjTI0zK4wxlYEbgE8d5BGRNGYuncknCZ+wsPNCFY8lIPS/vz81rq5B\n9EvRJJ9Kdh1HRHzrJ+BtYCMwGWgMVAVKA7mttVmttdeqeCwiIunZuGsjtw2+jQqvViAxOZFVbVex\nfex2FY9FJKxccQGZ1NsC/2KMucYYU5LU2wJ3knpb4JPGmKsu5+DGmKuNMfcbY1oA1wEFvMv3G2Mi\nvdvsNMZMO7OPtfYrYCkwyxjT3Nuf+S1glbX288v6lCJX4MwtvAJ7D+6l8+LO9C3Xl1o31XIdJyRo\nfPnGsiHLsFjuHHmn6ygBReNLQsBIa+0z1toGwEogGjhsrd1trU10G01EfEHnKvGHNT+s4eaBN3Pr\n07direXrDl+zZfQW/RtGfEo/vyRY+KIH8n+MMTU5/22BtwHVgcu5sqMg8B6Q9r63ud4/SwJ7ve95\ndjG8JTABmOF97WOg12W8v4j4iMfjodaoWpTLUo6xHce6jiPyN1mzZOXzHp9z29Tb/uqHLCLBz1o7\nO83fPzTGfAL0MsbkBMZba8/5fAwREQk/yzcup/vs7uzItIPbs9zOa/e+RofWHVzHEhFx6op7IBtj\nfgDmkNr3+AdSW0QcPvMVLFd2qDebiP89/OLDvLf3PX4Z/gt5c+V1HUckXeM+HMeArwaw7pF1VCpT\nyXUckZCXAT2Qu1lrp6Sz/jpgELAHeMlaq/41fqJ5togEgwVrF9Bzbk/2ZNlD7cjaTO88nTJFy7iO\nJSJy2Xw5z/ZFAbnNmSs7jDH3AVWAWGvtbh/kyzCa2Ir41zsr3qHNojYsuG8BjW9v7DqOyHnVi6nH\n+iPrOTDmAJFZI13HEQlpGVBA3g+8Q+qdcf94GahB6h1vg6y17/orRzjTPFtEAtncVXPp+1Fffsny\nC/Vz1Gd61+kUK1jMdSwRkSsWUA/RO/u2QGA48IAxZoQxJs+VHl8kFIR7X6P4A/E8vOBhepTqoeKx\nH4T7+PKHhYMWkpnM1B1R13UU5zS+JARcCzwBPALcD9QFooBiQHZgBTAFuKzndYiIezpXyeWYuXQm\nhZ8oTOsFrYkqEMX+gftZ8vSSfxSPNb7EnzS+JFhccQ/ks28LtNb+CYzx3hY4whizB90WKBK2PB4P\n1UZV48arbmTiIxNdxxG5KFmzZGVV71VUfKUiMe/EMLT10AvvJCKBah7woLX2lOsgIiLi3uQFkxm6\nbCgJmRNoXqQ5U7tOVXs9EZEL8EULi5C4LVC31on4x93P3c3K31by63O/kuPqHK7jiFySl+e/TO+4\n3nzZ/kuqlq/qOo5ISMqAFhaFrLUH/HV8uTDNs0XENY/Hw7h54xj5xUhOZD5B66Ktmdx1sv59IiIh\nLdB6IHu8fz0BHAGOev88ctbyr9bamVf0Zn6kia2I750pvq1ut5rqN1Z3HUfkstz17F3EHYrjwKgD\nZIvM5jqOSMjxdwFZ3NM8W0Rc8Xg8jHh3BC+se4HkiGQ6lOrAi51f1DMuRCQsBFQPZFJvC7zKWpvT\nWlvcWhtlra1rrW1hre1irR1orR0dyMVjEX8Lx75Gm37aRO/VvXnm1mdUPPazcBxfGWnBoAVcba6m\nzog6rqM4ofElIiKBTucqOVvK6RQGvjGQHH1zMGr9KDpX6MyxUceY8uiUSy4ea3yJP2l8SbDwRQH5\nUfWUE5G0kpKTqPNSHWpeXZMhDw5xHUfkimTOlJm4vnF8m/wtT735lOs4IiIiInIOyaeS6TG1Bzn6\n52DSd5PofWtvjo09xoTOE8iaJavreCIiQeuKW1iECt1aJ+I7NYfWZGviVn4d+6smahIyYhfG0m1F\nN5b/ZznRt0S7jiMSMtTCIvRpni0i/vZH0h/0nN6TWbtnEXk6kgHVBjC45WAiInxxzZyISHDy5Tw7\nsy8OIiJyxvDZw1l7ci3f9f5OxWMJKV0adWHx94tp/EZj9g7fS/5r8ruOJCIiIhLWEk8k0j22O3N/\nmUuOlByMqjOK3k17q3AsIuJj+qkqkgHCpa/R6s2ridkUw8vRL1OhRAXXccJGuIyvQPBev/coFFGI\nqiOq4vF4LrxDCND4EhGRQKdzVfg59Psh7h9zP3mH52Xp3qW89O+XOPriUfo07+Pz4rHGl/iTxpcE\nCxWQRcQnEk8k0nBGQ5rkaUL3u7u7jiPiFxEREawbuo79p/fTalwr13FEREREwsqvR37l7ufuptDI\nQqz53xpea/gaBycc1L8/RET8TD2QvdSbTeTK3DjgRn5P+Z2fX/hZt4xJyFu+cTn136nPpNqTePSe\nR13HEQlq6oEc+jTPFpErFX8gnk5TO7H8xHKKnirK+ObjaVG7hetYIiIBTT2QRSSgtJvYjl0pu9g1\naJeKxxIW6kbVZfjW4fRY1YMa5WsQVSrKdSQRERGRkLPt5210nt6ZuD/jKJlSkgUtF9D49sauY4mI\nhB1VekQyQCj3NZq+aDpv7n+Tj1p/RNECRV3HCUuhPL4C2dDWQ4nOEc0dL9/B8ZPHXcfxG40vEREJ\ndDpXhZ5NP22iylNVKD+5PIdOHmJ56+XsemGXk+Kxxpf4k8aXBAsVkEXksm3ctZGuy7ry1E1P0ahK\nI9dxRDLc4qcWk81ko8YzNVxHEREREQl667atI2pQFFHTovjz9J+seXgNW8dsJfqWaNfRRETCmnog\ne6k3m8ilOX7yOEUGFaFynsosH7bcdRwRZ/Ye3Evp0aVpf0N7Yh+LdR1HJOioB3Lo0zxbRC5k1aZV\ndH2rK9sybaNSRCWmd5yuFmEiIlfIl/NsFZC9NLEVuTQVBlbgSPIRfn7hZzJnUjt1CW/z4uZx/8f3\nM/uu2bSObu06jkhQUQE59GmeLSLnsnj9Yh579zF+yvwT1bNW57VHXqPc9eVcxxIRCQm+nGerhYVI\nBgi1vkbtJrZj56mdrBu8TsXjABBq4ysYNa/ZnF6le/HwgofZsW+H6zg+pfElIiKBTueq4DMvbh7F\n+xan0fuNKJ6rOLt67SIuJi4gi8caX+JPGl8SLFRAFpFLoofmiaRvQucJ3HLVLVR/oTrJp5JdxxGR\nDGKMKW+MWWaMOWGM+cUY84wx5oJXehhjchljXjfGHDHGJBhj3jLG5E1nu6bGmE3GmJPGmC3GmJYu\nj2WMiTDGDDTGrDLGHPJ+LTbGVL6475iIhLPZy2dT5IkitJjfggp5K7Cv/z6WDV1GycIlXUcTEZHz\nUAsLL91aJ3JhG3dt5LYptzG44mBGPDTCdRyRgJOUnEThAYUpm6Msa59d6zqOSFAI5hYWxpjcwBZg\nMzAGKAWMB8Zba4deYN/FQGmgL2C9+/9qra2TZptawOfAJOAjoDHQD2horf3MxbGMMdmBn4EZwDLv\nNj2A+kB1a+236XxWzbNFwlzswliGLBnC4cyHaVqgKbFdY8l/TX7XsUREQpp6IPuBJrYi56eH5olc\nnB37dnDjizfS6YZOTHl0ius4IgEvyAvIg0gtwhaz1p7wrusPDAOutdYeP8d+1YE4oLa1Ns67rgqw\nFqhvrV3uXbcYyGStrZ9m30+AnNbaO1wcyxgT4d3n9zTHyQJsB5ZERPGyAAAgAElEQVRbazul83k1\nzxYJQx6Ph5fmv0TMihgSMyfSskhLJneZTO4cuV1HExEJC+qBLBJkQqGvUdXhVclhcrBkyBLXUeQs\noTC+QkmZomV4/773iY2PZfqi6a7jXDGNL5HzugtYfKZ47PUukA2ok/4uf+3365kiLYC1dh2wG2gE\nYIzJCkQDc8/a912gujEmp4tjWWs9aYvH3nWnSL0Su8h5PrOI3+hcFVg8Hg8j54wkT588DPhiAM1K\nNSPx2UTe7vN2UBaPNb7EnzS+JFiogCwiF3TmoXlfD/5aD80TuQhNazRlyM1D6Lq8K+u2rXMdR0T8\n51/Aj2lXWGt/Bv7wvnbR+3ltTbNfKSBLOtttJXUOX9bRsf7BW6CuBGw71zYiEvo8Hg9PvfkUufrm\nYsTaEbT9V1sSn0/ktR6vkS0ym+t4IiJyBVRAFskA0dHRriNcton/naiH5gW4YB5foSymbQx3XnMn\ndabU4dDvh1zHuWwaXyLnlQdISGf9Ue9rV7JfHlL7C5+93VHAnLVdRh4rPUO8r79ynm1E/EbnKreS\nTyXTZ0YfsvfNzoQNE+h+c3eOjz3OK91eITJrpOt4V0zjS/xJ40uChS4lFJFzWr5xOX2+7MOoKqNo\nVKWR6zgiQeeTQZ9Qun9pKj1TiT0v7CEiQr+3FZHQYoy5GxgMPGGt3XGu7dq3b0+JEiUAyJ07N1FR\nUX/9o/nM7bta1rKWg2s5KTmJlgNbsnDfQq4qehUDKw/kjsJ3EBER8dddi4GUV8ta1rKWQ31548aN\nJCSkXguwZ88efEkP0fPSwz3En1asWPHX/9TBYu/BvZQZVYb7rruPd/q+4zqOnEcwjq9wknA8gaJP\nF6Vanmp8NvQz13EumcaX+FuQP0TvADDJWjvirPXHgWHW2nHn2G8OkN9aW++s9QsAa6291xhTntS+\nwnWstV+k2aYy8DVQxVr7TUYf66z1VYDlwOvW2p7n+T5pni1+pXNVxjp+8jiPT3uc2fGzyX46O4Nr\nDabfff1C9hflGl/iTxpf4k96iJ6I+FVSchKVnq9EuazlVDwWuUK5c+Rm9eOrWXFsBQPfGOg6joj4\n1o+c1RvYGFOU1IfopddL+Jz7eaXtQbwLOJXOduWB08B2R8cCwBhTFlgALAV6pbOPiISYhOMJtHyh\nJbmH5ubjnz5mfJ3xJLyYwIAWA0K2eCwiIql0BbKXrowQ+X9Rg6LYd3If+8bsC4m+ZSKBYNZns2j/\nWXvm3j2XFrVbuI4jEjCC/ArkJ4F+QHFr7Qnvun7AcOBaa+3xc+xXDYgDaltrv/SuO3M1cD1r7efe\ndYuACGvtnWn2XQDkstbe4fBYhb3b7QfqW2uTLvB90jxbJIgdPHqQR6Y+woIjCyhwqgAj7xpJp4ad\nXMcSEZEL8OU8WwVkL01sRVL9Z/x/+GDfB2wfuJ3ihYq7jiMSUnpN68XkbZPZ+PhGKpSo4DqOSEAI\n8gJyblJbQ2wBRgOlgHHAeGvtsDTb7QQ+t9Y+kmbdIqA00J/UB9yNAn611kan2aYm8DmpD6f7CLgb\n6AM0tNYuc3EsY0wk8BVQDGgDHEnzLfnTWrsxne+T5tkiQWjfb/voNLUTS48tpcipIoxrOo5WdVq5\njiUiIhdJLSxEgsyZ5uaBbsz7Y5hzYA6fPvSpisdBJFjGl8DERyZSNVtVarxYg+Mn070wMeBofImc\nm7U2AahH6px6PjCM1ALy8LM2jeCf8+6WwEpgBvAGsA6476zjxwEtvO+xCLgHaJ224OvgWIWAm4Fr\nSG1h8WWarw8RcUDnKt/atX8X0cOjKTauGNuPbmde03nsG78vbIvHGl/iTxpfEiwyuw4gIoFh8frF\nPLnuSSbUmEC9W+tdeAcRuSwrhq2gWL9iRD0dxfYx29UzUCTIWWt/BOpfYJsb0lmXCHTyfp1v3/mk\nFqfPt02GHctaGw9kOt8xRCQ4bdmzhc4zOrP21FpKny7N4gcX0+C2Bq5jiYhIAFALCy/dWifhbNf+\nXZQfV57/FP8Pb/R8w3UckZB38OhBSsaUpFqeaiwbevbFfyLhJZhbWMjF0TxbJLBt2LGBR15/hG/t\nt9zouZEpD02h1k21XMcSEZErpB7IfqCJrYSrP5L+oOiTRbkh+w2sH7nedRyRsLHpp01UmlyJLqW7\nMLnbZNdxRJxRATn0aZ4tEpjitsTRbVY3tkRsIYooYjvEUrlsZdexRETER9QDWSTIBGpfI4/HQ+Wh\nlclisvDl8C9dx5HLFKjjS86v4g0V+eC+D5iyewoT/zvRdZxz0vgSEZFAp3PVpVn27TLK9S9H7Tdr\nkz1Ldr7v+j0bnt+g4vE5aHyJP2l8SbBQD2SRMHbP8/ew+9RudgzeQdYsWV3HEQk7TWs0ZdT+UTzx\n5ROULVKWRlUauY4kIiIiIWrB2gX0nNuTPVn2UDt7bT7t8imlipRyHUtERIKAWlh46dY6CTe9pvXi\nle2vsKbzGqqUq+I6jkhY6/hyR96Mf5NNPTdRvlh513FEMpRaWIQ+zbNF3Jq7ai59PurD/iz7qZ+j\nPtO7TqdYwWKuY4mIiJ+pB7IfaGIr4WTygsk8/sXjzL1nLi1qt3AdR0SAWkNr8d2x74h/Jp68ufK6\njiOSYVRADn2aZ4u48fqS1xm8cDAHsxykcZ7GzOg2g4J5CrqOJSIiGUQ9kEWCTCD1NVq4biE9vujB\niEojVDwOEYE0vuTyrRq+inwR+ag4vCIpp1Ncx/mLxpeIiAQ6nav+bvKCyeTvnZ9Hlj5CrSK1+G3I\nb3w86GMVjy+Txpf4k8aXBAsVkEXCyJY9W2gypwkPXfcQT7V6ynUcEUkjIiKCTTGbOOY5Rs1hNV3H\nERERkSDi8XgY+8FYcvfOTa8Vvbi75N0kxCTwXv/3dGeTiIhcMbWw8NKtdRLqDv1+iBLDSxCVM4rV\nMatdxxGRc9ixbwcVJlTggaIPMPuJ2a7jiPidWliEPs2zRfzH4/EQ804M49aPIzkimQ6lOvBi5xeJ\nzBrpOpqIiDimHsh+oImthLLkU8mUGFCCqyKuYtfYXURE6OYDkUC27Ntl3PnunQyLGsbQ1kNdxxHx\nKxWQQ5/m2SK+l3I6hcGzBjNp0ySssXS7sRuj240ma5asrqOJiEiAUA9kkSDjsq+Rx+PhtiG3cdKe\n5LuY71Q8DkHqmxV66t1aj8l1JjP8u+G8vuR1p1k0vkREJNCF07kq+VQyPab2IEf/HLyy6RV639qb\nY2OPMaHzBBWP/SScxpdkPI0vCRaZXQcQEf9qOLIhO5N3sm3wNnJlz+U6johcpK6Nu7L38F46f9aZ\nInmL0LByQ9eRRERExJE/kv6g5/SezNo9i8jTkQypNoTBLQfr4hAREckQamHhpVvrJBS1f6k9s/fO\nZl33dUSVinIdR0QuQ7uJ7Xj757f55tFvqHhDRddxRHxOLSxCn+bZIpcv8UQi3WO7M/eXueRMycmQ\nOkPo3bS3CsciInJB6oHsB5rYSqh5+s2nee775/i05ae6clEkyNWLqceao2vYPng7RQsUdR1HxKdU\nQA59mmeLXLpDvx+i69SufPTbR+RLyceIBiPo2rir61giIhJE1ANZJMhkdF+j2IWxjNwykth6sSoe\nhwH1zQp9S4cspWTWklQcWZHEE4kZ+t4aXyIiEuhC6Vy1//B+7n7ubgqNLMSa/61h5l0zOTjhoIrH\nDoXS+JLAo/ElwUIF5AsoUaIExpiw/SpRooTr/wRyiRasXUC3Fd0YfstwOjXs5DqOiPhAREQE3z77\nLVdHXM1NT99EyukU15FERETEh+IPxFMvph5FxxTl+0PfM7fJXPZP2E/bem1dRxMREVELizPOdWud\n93JvB4kCQ7h//mCzbts6qk+rToeSHZj22DTXcUTExxKOJ1B8SHFKXl2SDSM3qP+hhAS1sAh9amEh\ncm7bft5Gp2md+DL5S25IuYGXW71MoyqNXMcSEZEQoB7IfqACcvrC/fMHk617txL1YhQNCjRgwaAF\nruOIiJ/EH4jnX6P+RbXc1fh82Oeu44hcMRWQQ58KyCL/tOmnTXSa0YlvPN9Q7nQ5Xm3zKtG3RLuO\nJSIiIUQ9kEWCjL/7Gu37bR+VJ1Smco7KKh6HIfXNCi/FCxVnXc91xCXGcf+Y+/3+fhpfIiIS6ILp\nXLVu2zpuefIWoqZFkXw6mTUPr2HrmK0qHgewYBpfEnw0viRYqIAsEuSOJB7hppE3UTJrSb4Y/oXr\nOCKSAW4qeROfd/yc//72X7q80sV1HBEREbmAVZtWUX5Aeaq+UZWsmbKy8ZGNfDfqO6qWr+o6moiI\nyAWphYWXWlikL9w/f6D7I+kPbhh0A5ERkewcs5PMmTK7jiQiGWjB2gU0/bApAysM5LmHn3MdR+Sy\nqIVF6FMLCwlnC9ctpMecHvyU+SdqZK3BjEdmUO76cq5jiYhIGPDlPFvVJpEglXI6hZueugkPHn4Y\n+YOKxyJh6J6q9/D6sddpv7Q9+eflp0/zPq4jiYiICDAvbh69P+zNz1l+5t+5/s2yrssoXqi461gi\nIiKXRS0sglh8fDxNmjQhZ86czJkzB4BZs2YRGRnJ008/zZEjRwDo06cPdevW5euvv3YZN6z5uq+R\nx+Oh0lOVOHz6MD8M/4Fskdl8enwJLuqbFd4erv8w46uPp99X/Zi5dKbPj6/xJSIigS6QzlVvLXuL\nIk8UocX8FlTIW4F9/fexbKiKx8EskMaXhB6NLwkWumQxiBUvXpwWLVqwefNmWrVqBcB9991H9+7d\n6dy5M3nz5gWgRo0ajB49mixZsriMKz7i8XioOawmO//cyY+DfyT/NfldRxIRx3o3683hY4fp+FlH\nskdmp0XtFq4jiYiIhJXYhbEMWTKEw5kP07RwU2K7xmqeLiIiIUMF5CCXP//fJyWzZ8+maNGiHDp0\niOLFi7Nnzx6uu+46FY8di46O9tmx6o+oz7cnvuW7Pt9RrGAxnx1Xgpcvx5cErxEPjeD49OO0+rgV\nH1/1MY1vb+yT42p8iYhIoHN1rvJ4PLw0/yViVsSQmDmRlte3ZErXKeTKnstJHvEPzYXEnzS+JFio\ngBzk8uXL99ffv/zySypWrEjBggU5dOgQAHFxcbRp08ZVPPGxu5+7m7jEOL7p8Y0eviEi/zCh8wRO\nvHKCJu81YUnWJdSNqus6koiISMjxeDw8/97zjFkzhpOZTtK2VFsmPTJJbeVERCRkqQfyFTLG+OTr\ncp25Ajk5OZnVq1dTvXp18uXLx6FDh1i+fDl166p4EAh80dfogbEPsOTIElZ3Wc1NJW+68lASMtQ3\nS9KKfSyWB697kDvfupO4LXFXfDyNLxERCXQZda7yeDw89eZT5OqbixFrR9D2X21JfD6R13q8puJx\nCNNcSPxJ40uCha5AvkLWWqfvnz9/fqy1TJo0iU6dOgGpVyXHx8dz9dVXU7hwYaf5xDfaTWzHvIPz\nWNF+BVXKVXEdR0QC3Fu93+LkmJNEvxbNmq5rqFy2sutIIiIiQSv5VDJPznqSVze/isHweMXHee7h\n58icSf+cFhGR8GBcF0ADhTHGpve9MMY4LxJfSJ48eRg/fjwdOnQAYODAgXz11Vd8/vnnRESkXmS+\ncuVK5s+fT7NmzViyZAk1a9bkyJEjZMmShQceeOCcxw6Gzx/qur/andifYln04CIa3NbAdRwRCSKN\nRjZi+eHlbOi5gQolKriOI5Iu71zj8m/HkoB3rnm2SKBLSk7iiRlP8NqO18his9Cvcj+Gth7617+x\nREREApkv59k684WAunXr/lU8BihcuDAjR47828SmbNmyJCYmUrt2bZKSkrj99tspWrQohw8fdhFZ\nLlKvab2I3R3LvPvnqXgsIpds4VMLqZ6rOpVfqszWvVtdxxEREQkKx08ep93EduQcnJN3tr3DiBoj\nSByXyPA2w1U8FhGRsKSzXwj44IMP/rbcu3dvatWq9bd1ycnJlC5dGoDExETy5s3LwoULuf3220lK\nSsqwrOHqcvoa9ZrWi0k7JjHn3jk0qdbE96EkZKhvlpzP8qHLqZyzMpVerHRZRWSNLxERCXS+Olcl\nHE+g5QstyT00N5/s/oTxd4wn4cUEBrQYoMJxGNNcSPxJ40uChc6CYWL9+vXUrVuXlJQUChQoAEDm\nzJlJSEggMjLScTo5W9ricYvaLVzHEZEgFhERwcphK6+oiCwiIhLKDh49SNNRTckXk49Vv6xiav2p\nHHrxED2a9HAdTUREJCAEfA9kY0x5YBJQDUgApgPDz9dIzRhTHNidzkvvWmv/c459grYHsj+F++d3\nQcVjEfEHj8dDnWfqsP7Yejb03kD5YuVdRxIB1AM5HKgHsgSqfb/to9PUTiw9tpQip4owruk4WtVp\n5TqWiIiIT/hynh3QBWRjTG5gC7AZGAOUAsYD4621Q8+z35kCch/gyzQvHbLW/nSOfVRATke4f/6M\npuKxiPiTisgSiFRADn0qIEug2bV/F51iO7EqaRXFTxVn4gMT1TJORERCTjg9RK87EAncZ61dZq2N\nBZ4B+hhjclzE/tuttV+n+Uq3eCzibxfT10jFY7lc6pslF+ty2llofImISKC72HPVlj1bqP50dcq8\nVIb9J/aztNVSdo/breKxnJfmQuJPGl8SLAK9gHwXsNhaeyLNuneBbEAdN5FEfK/H1B4qHotIhkhb\nRL71xVvZvHuz60giIiJ+tWHHBm4bfBs3T72ZY8nH+OKhL9g+djv1bq3nOpqIiEhQCPQWFgeAV6y1\nMWetPw4Ms9aOO8d+Z1pY/AbkAw4C7wBPWWuTzrGPWlikI9w/f0bo8FIHZu2bxftN36d5zeau44hI\nmPB4PNQfUZ+4xDjiusZRuWxl15EkTKmFRehTCwtxJW5LHN1mdWNLxBZuNbcyrcM0KpWp5DqWiIhI\nhginFhZ5SH1w3tmOel87lz9JffBeJ6AuMIXUdhjv+DqgyJVo+UJL3vz5TT5t+amKxyKSoSIiIlg+\nbDn18tajemx1Vm9e7TqSiIiITyz7dhll+5el9pu1yZ4lO993/Z5vnvtGxWMREZHLFOgF5Mtirf3V\nWtvTWrvAWrvKewVzH6CJMeZm1/kk/KTX1+je5+9l3q/zWP7QchpWbpjxoSRkqG+WXIlPn/qUZoWa\nET0zmqXfLP3H6xpfIiIS6M6cqxasXUDJviVpMKcBRbIXYUfPHXz17FdUKFHBbUAJapoLiT9pfEmw\nyOw6wAUcBa5JZ30e72uX4n1gMnAb8H16G7Rv354SJUoAkDt3bqKioi7xLULXmR9q0dHRWr6M5Y0b\nN/617PF4qNypMptObGLNiDVUKVfFeT4tB/dy2vEVCHm0HHzLj1V5jGzfZeOud+8iZm0MNW+sqfGl\nZb8tb9y4kYSE1BvM9uzZg4SvEiVKEB8f7zqGU8WLF9f/Bz7w+Xef03Z+W/Zn2U+DPA1Y2WUlxQoW\ncx1LREQkZAR6D+SVwD5rbZs064oCe4F7rbWfXMKx8pHaE7mDtXZmOq+rB3I6wv3z+5rH46HWsFps\nOLGBrx//moo3VHQdSUTkL49OeZSpP03l7bvfplWdVq7jSJhQD+TQp3n2uel7cGVeX/I6gxYO4rcs\nv3FP3nuY1nUaBfMUdB1LREQkIPhynh3oVyAvBPoZY7Jba0941z0I/AGsvMRjPQBY4Bsf5hO5aCmn\nU6j0VCV2/rmT7/p8R7nry7mOJCLyN5O7TSb7a9lp/WlrEk4k0LVxV9eRRERE/mHSx5MYvmw4CVkS\nuO+6+5jSZQp5c+V1HUtERCRkRbgOcAFTSH0g3jxjTD1jTBdgGDDOWnv8zEbGmJ3GmGlplocZY14w\nxjT37hcDjAc+sNZuzugP4S/x8fE0adKEnDlzMmfOHABmzZpFZGQkTz/9NEeOHAGgT58+1K1blzFj\nxtCuXTumTZvGpEmTXEYPO4uWLKLMgDLEJ8Xz46AfVTwWnzpzi7iIL4ztOJaYW2PovrI7z777rMaX\niIgEBI/Hw5j3x5C7d26eWPkEd5e8m4SYBOb2m8umDZtcx5MQprmQ+JPGlwSLgL4C2VqbYIypB0wC\n5gMJwDjgmbM2jeDvxfAfgb5AJ+BqUltejAae83fmjFS8eHFatGjB5s2badUq9Vbj++67j+7du9O5\nc2fy5k39LXyNGjUYPXo0SUlJ9OvXj0ceecRl7LBz6PdDtI5tTdbrs7LrmV3kvya/60giIuc15MEh\nFMhVgO4ru9P8dPO/+teKiIhkNI/HQ8w7MYxbP47kiGQ6lO3Ai51fJDJrpOtoIiIiYSOgC8gA1tof\ngfoX2OaGs5bnAHP8mStQ5M//92Lk7NmzKVq0KIcOHfrroRzXXXcdWbJk4dixYxQoUMBR0vAUfyCe\nis9XJG/xvGwZuYVskdlcR5IQpOKe+EPXxl3JlzMfrT5uxX/G/4e3+7ztOpKIiISRlNMpDJ41mEmb\nJmGNpVuFboxuN5qsWbL+Y1vNhcSfNL7EnzS+JFgEfAFZzi9fvnx//f3LL7+kYsWKFCxYkEOHDgEQ\nFxdHmzapzyD85ptv+Pe//+0kZzjavHszVV6qQtnIsnwz8hsyZ9L/biISXFrUbsHSnEtp+FZDfhvx\nG4ufWkxERKB3vxIRkWCWfCqZvq/1Zdq2aWSymeh9a29i2sRoLi0iIuKQ/hV4hcwzxidfl+vMFcjJ\nycmsXr2a6tWrky9fPg4dOsTy5cupW7fuX9s2aNCAevXqXfFnlgtbvXk1lSZVolruanz73Les/mK1\n60gSwtQ3S/wpIiGCdd3X8cXRL6gypAopp1NcRxIRyRCX+ryRr7/+2mXcoPdH0h90ntSZHE/mYObW\nmQy5fQjHxh3juYefu2DxWHMh8SeNL/EnjS8JFvo17hWyw6zT98+fPz/WWiZNmkSnTp2A1KuS4+Pj\nufrqqylcuLDTfOHonRXv0PbTtjS/tjnv93/fdRwRkSsWVSqKrf23csvoWyg9oDSbYjaRK3su17FE\nAoIxpjypz+uoRurzOqYDw621550kGmNyAROBpqRe1LEA6GmtPXLWdk2BEUAZ4CfgGWvtXJfHMsbU\nBzoC1YHi3s8bc77PG4wu9XkjWbJkcRk3aCWeSKR7bHfm/jKXnCk5GRM9hp5NeuqOFxERkQCis3KQ\nu+aaa0hISCBPnjx/tbPInz8/ixcvplmzZo7ThZ9R742izcI29PlXn78Vj9XXSPxJ40v86cz4Klm4\nJHti9pDsSabEUyXYe3Cv22AiAcAYkxv4DEgBmpD6oOe+/POBz+l5D7iD1EJsO6AKMO+s49cC3geW\nAXeRWsx9x1vAdXYs7/43ez/7iYv4rEHrfM8bAf72vBG5NId+P8T9Y+4nz/A8LN27lEl1J3HkxSP0\nbtb7kovHmguJP2l8iT9pfEmw0BXIIaBu3bp06NDhr+XChQszcuTIv028Vq5cyfz582nWrBlLliyh\nZs2aHDlyhCxZsvDAAw+4iB1yuk3uRmx8LC/VeonH733cdRwREZ/Lmysve8bs4bYht1Hu+XJ80f0L\nKpet7DqWiEvdgUjgPmvtCWCZMeYaYJgxZoy19nh6OxljqgMNgNrW2jjvuv3AWmNMXWvtcu+mTwMr\nrbVPeJdXGmNuAoaSWrx1cixrbT+gn/f1kL5i4VKeNyIXZ//h/XSe0pnFiYsplFyImffMpG29tq5j\niYiIyHnoCuQQ8MEHH/xtuXfv3tSqVetv68qWLUtiYiK1a9cmKSmJ22+/naJFi3L48OGMjBqSPB4P\nDZ9tyPTd0/mo+UfpFo/V10j8SeNL/Ons8ZU1S1a+e/476uSrQ7Vp1Zj/1Xw3wUQCw13AYm/x+Ix3\ngWxAnQvs9+uZIi2AtXYdsBtoBGCMyQpEA3PP2vddoLoxJqeLY2U0Y4xPvi7XpTxvZMKECRw4cOCK\nP3Ooij8QT72YehQdU5QtR7Yw99657J+w3yfFY82FxJ80vsSfNL4kWKiAHCaSk5MpXbo0AImJieTN\nm5eFCxdy++23k5SU5Dhd8Eo+lUzFQRVZdXgVX3f5mibVmriOJCLidxERESwasojOJTvTbF4zJn08\nyXUkEVf+BfyYdoW19mfgD+9rF72f19Y0+5UCsqSz3VZS5/BlHR0rQ1lrffJ1uc73vJGEhIS/PW9k\n+/btFCpU6Io/c6jZ9vM2ag2tRckJJYlPjOeTFp8QPy6e+2vd7zqaiIiIXCQVkMPE+vXrqVu3Likp\nKRQoUACAzJkzk5CQQGRkpON0wenXI79SfEBx9v+5n22DtlGpTKVzbqu+RuJPGl/iT+cbX1MencJz\nlZ+j5+qe9IztmXGhRAJHHlIfnHe2o97XrmS/PIBNZ7ujgDlru4w8Vli52OeNrF69mvj4eL766itX\nUQPOpp82UeWpKpSfXJ7DSYdZ3no5O1/YSaMqvr+YXXMh8SeNL/EnjS8JFuqBHCbuv///f8P/7LPP\nAjBixAhXcYLeum3rqDOlDkUzF2VHzA5yXJ3DdSQRESeefOBJbih0A//5+D9sidnC0iFLL/nhRyIi\ngexinjdy/fXXEx0dTbVq1VxEDChrt66ly8wufG++52Z7M2varaFq+aquY4mIiMgVUAFZ5BK9s+Id\nHvrkIernq8+ngz+9qELJihUr9JtF8RuNL/GnixlfLe9oSdnrylJzUk3KDCjDt898S67suTImoIhb\nR4Fr0lmfx/va+fbLf4H9zlwdfPbx86R53cWxLkv79u0pUaIEALlz5yYqKupKDpeh0nveyNni4uKo\nWbMmP//8M9dff/0lHf9M/8szP2uDdZk80H12d37c9yNlI8qyMWYjFW+oyIoVK1hxYIVf33/jxo1/\n/XcJlO+HlkNnWeNLyxpfWg6W5Y3/196dhkdRpX8f/54kMGGXkACBQNjVYdwQkJ0AMjguqAwObg+M\nAu6KgqggyKIiooALjiMKiiCKDKOiyKLIlgAj4AqiiEAEWQOGsBiT0Od50Q3/EBNIQldXL7+PV66Q\n6jrVdxU3p29Pqs756isyM70Pk23btg1/MmcyJ1g4McbYwl0Mks8AACAASURBVK6FMeaM5k0LdZF+\n/gU9Ov1RntrwFA80foDxfccXu93Spf9XNIv4m/JLnFSS/Mo4mMEFoy7gsOcwnw/6nLPrnO1scBIW\nfLVG6Vc5c5ExZhmww1p7U75tScDPwFXW2nlFtBsF9LPW1i6wfTPwnrV2sG/hu0PAPdbaV/Pt8/+A\nqUCctfZQoI9VyLnsA1601o4+xXUK+zr7gw8+IDc3lxYtWpCcnFzsduFwDeavmc+9s+5lS8wW2pRt\nw5T+UwLe/6sWEicpv8RJyi9xkj/rbA0g+0RCYVsakX7+x3k8HnqM68GHBz7ktUtf45a/3nL6RiIi\nESjvWB7tRrRj3W/rmHv9XEfmupTwEuIDyI8ADwLJ1tojvm0PAiOBmtbaw0W0awWkAe2ttSt925oD\nnwNdrLVLfNsWAFHW2r/ma/sRUNla28GtYxU4Fw0gn4FQvgZzUucw8L2BbI/ZTqcKnZh6+1SSaxR/\n8FxEREScpQFkB6iwLVyknz/A4d8O03JES7bkbWFx38W0bdrW7ZBERILeP1/4J2/+8iZjW4zloZ4P\nuR2OBLEQH0A+C9jg+3oaaAiMByZYa0fk228zsMRa2z/ftgVAI2Aw3gXuxgK7rbUp+fZpCywBXgLe\nB64ABgLdrLWLXTxWXaAF3mkxpgALgHeBI9baBYVcJ9XZRQjFazBj8Qwe+ugh9pTdQ7fK3Xjtjteo\nVa2W22GJiIhIAf6ss6P8cRCRcLVh2wZqD6nNgdwDbB6yudSDx8fnphFxgvJLnFTa/HrjvjeY0HoC\nQ9YMoce4Hng8Hv8GJhIErLWZQBe8NfVcYATeAeSRBXaN4o919z+AZXgHYN8A1gA9Chw/Dejpe48F\nwJXADfkHfN04FtAJmA3MAir6jvsu8C8kbE2eP5nqD1Snz4I+tE5szZ6he/j40Y+DYvBYtZA4Sfkl\nTlJ+SajQInoiRZi1bBY3fXgTrSq3YumIpcRE65+LiEhJ3H/N/TRr0Ixur3fjnIfPYe3ItVpcT8KO\ntfZ74NLT7NOgkG1ZQF/f16nazsU7OH2qfQJ6LGvtNGDaqY4j4cHj8fDC3BcYvXQ0h2IO8Y+6/+Dl\n215WXy4iIhJhNIWFjx6tK1yknv8Drz3A85ufZ0CjAUzsN9HtcEREQtruA7tp/kRzDtqDrLhnBRc2\nvNDtkCSIhPIUFlI8qrOLFqzXwOPxMObdMYxbPY7s6Gx61+/NC/1eoHxsebdDExERkWLSHMgOUGFb\nuEg7/7xjeaSMSmH10dW8ddVb9OrYy+2QRETCQt6xPLo92Y1lh5Yx5a9T6NO1j9shSZDQAHL4U51d\ntGC7Bh6Ph2EzhvH8l89zzByj/9n9GX/reMqWKet2aCIiIlJCmgNZxAHpe9Kp/WBt1met59v7vvXr\n4LHmNRInKb/ESf7Kr5joGBY/tpjBfxnMLYtvod+kfn45roiInLmc3BweeO0BKgyqwHNfPsfd59/N\n4WcO8+LtL4bE4LFqIXGS8kucpPySUKEBZBHg3eXv0mhcIxJjE9nx1A7OrXuu2yGJiISlp/o8xfvX\nvM/0rdM596FzyTqS5XZIIiIRKzsnmzv+dQeVHq7Eqxte5eHmD3N4/GHG3TJO63+IiIjICZrCwidS\nHq1btWoVAK1bty7W/uF2/oXp/1J/pmyfwt317+bF2190OxwRkYiwY98OLnnqEjJtJov6LaJt07Zu\nhyQu0RQW4S9S6uzScOsaHP7tMHdPvpuZ22dSIa8CQ9sN5cEeDxIVpfuLREREwoXmQHaACtvChfP5\nZx3JotWoVmw+tpnZf5/N1W2udjskEZGI4vF46P50d+ZnzueJi59gyD+GuB2SuEADyOFPdXbRAn0N\nMg9nctu/b+O/e/7LWblnMbLLSO656p6Avb+IiIgEjuZAFgDS09Pp3r07lSpVYtasWQC8+eabxMbG\nMnz4cA4cOADAwIED6dy5M6mpqQwZMgRrLcuWLaNPnz68+uqrTJo0yc3TcMWq71ZRa1gtDuYdZMsj\nWxwfPNa8RuIk5Zc4ycn8ioqK4qMhHzGhzQSGrR1Gl9FdyDuW59j7iYiURElr7XHjxgVtfb33171c\nPfZqqo2uxvJflvPKpa+Q8VxG2AweqxYSJym/xEnKLwkVGkAOYcnJyfTs2ZOEhAR69fIu+NajRw+i\no6Pp168fcXFxALRp04aFCxfSoEEDsrKyMMbQrFkzYmNj6d+/P/fcEx6FY3E9NuMx2k1rR8eEjmx/\ndjtJCUluhyQiEtEGXD2ANf3XsObAGhIfTGTDtg1uhyQiUuJa+8477wy6+nrHvh10e6IbNcfWZN3e\ndcy8Yia7J+6mb7e+bocmIiIiIUQDyCEuPj7+pJ/feustkpKSyMjIAGDbtm3Url2bMmXKkJOTQ716\n9di5cye5ubkkJCS4EbJrDv92mOZDmzPm6zFM6jCJeUPnBWyet5SUlIC8j0Qm5Zc4KVD51axxM3aP\n3U2jio04/6Xzee795wLyviIip1KSWjuY6uufdv5EysgU6o6vy6ZfN/H+te+zY8IOenXs5XZojlAt\nJE5SfomTlF8SKjSAHOKqVat24s8rV67k/PPPp3r16ieK2rS0tBML5u3bt48KFSpgjGHdunV06tTJ\nlZjdsPTrpdQcWpMdv+1g44CN3HnFnW6HJCIiBZSPLc+qx1cxuvloBq0cRMrIFHJyc9wOS0QiWElq\n7WCorzds20CrYa1o/EJjdh7ZySe9PmHr+K10b9Xd1bhEREQktMW4HUCoM35a8qW0a2ccvysiJyeH\n1NRUHnroIapVq0ZGRgafffYZnTt3PrFvixYtaNGiBQCJiYlnHHOoGDhlIM9teo6ra17NnMFzXFld\neunSpfrNojhG+SVOciO/Hu31KFc0v4LOL3amxuAaLL57Mc0aNwtoDCISHMwo/xTbdkTpiu2S1Npd\nu3b1S6yl8cWPX9Bvaj++4iuaepqyovcK2jZt61o8gaZaSJyk/BInKb8kVGgA+Qy5vXB0fHw81lom\nTZpE377eucyqVatGeno65cqVi6iB4oIyDmbQcUxHNh3bxBuXvUHvS3u7HZKIiBTThQ0vZPczu/nb\nU3+jxWstGH7+cEbeNNLtsEQkwEo78OsvJam1J06cyI033kiNGjUCFl/ahjRuf/N2vov6jovMRay9\nZa1+4SYiIiJ+pyksQlyVKlXIzMykatWqJx6xi4+PZ+HChVxzzTUuR+eeGYtnUHt0bY7kHeGnh35y\nffBYv1EUJym/xElu5lfZMmVZ/Nhinmv7HE989QTnPXweGQczXItHRCJPSWrtTZs2BWzwePGXi2ky\nuAntp7enUtlKfHv7t6wbsy5iB49VC4mTlF/iJOWXhAoNIIeBzp07c8stt5z4OTExkSeffPKkqRqW\nLVvGoEGDWLFiBcOHD2fBggXMnDmT2bNnuxGyY3Jyc+j2RDd6L+rNbU1uY9v4bdStXtftsERE5Azc\n2/1efnrwJw7nHab26NrMWDzD7ZBEJIIUp9ZOTU0lPT2d1atXOxrL3NVzqT+oPl1ndaVWhVr8eN+P\nrHp8FU3rNXX0fUVERCSyaQA5DMyZM+ekn++//37atWt30rYmTZqQlZVF+/btyc7OpmXLliQlJbF/\n//5Ahuqo1PWpJAxO4POMz0nrk8aLt7/odkgnLF261O0QJIwpv8RJwZJfyTWS2Tp+K7c1uY3ei3rT\n7YluWmBPRAKiOLV2nTp1SElJoVWrVo7EMGvZLJIGJnHNe9fQpGoTfh70M0tHLqVhrYaOvF+oCZbP\nKglPyi9xkvJLQoUGkCNETk4OjRo1AiArK4u4uDjmz59Py5Ytyc7Odjm6M+PxeLjjX3fQYXoH2tdo\nz77x+2j959ZuhyUiIg548fYXSeuTxucZn5MwOIHU9aluhyQiQlpaGm3btmX79u1+Pe7ri16n5gM1\nuXHejVxc/WJ2P7KbhcMWkpSQ5Nf3ERERETkVDSBHiLVr19K5c2fy8vJISEgAICYmhszMTGJjY12O\nrvS+2fINSYOSeGPzG7x9+dt8NOQjYqKDb21IzWskTlJ+iZOCMb9a/7k1+8bvo131dnSY0YGbJt6E\nx+NxOywRiWAVKlRg165dfuuLJn04ifj74+n/SX861O7A/sf288EjH1C9anW/HD/cBONnlYQP5Zc4\nSfklocJY6+7KxsHCGGMLuxbGGCL5GgXr+Xs8Hu6dfC8vb32Z1n9qzfyH51O5QmW3wxIRkQCbu3ou\nN866kbK2LB/2/5C2Tdu6HZKUkK/WMG7HIc5RnV20/NfA4/Hw7H+fZUzqGI7EHOHGOjfy0m0vUbFc\nRZejFBERkVDkzzpbdyBLyDl+1/GUTVN4o+sbpI1OC/rBY81rJE5SfomTgj2/urfqTsbTGVyScAnt\np7fX3cgiEnI8Hg8j3xpJ5UGVGb5yODecfQOHxhxi2oBpGjwupmD/rJLQpvwSJym/JFQE37P+IkU4\n6a7jSq35/uHvg37gWEREnBdbNpb5j84/cTdy/MB45vabS7u/tDt9YxERl1UcVBGL5c6/3MnY3mMp\nW6as2yGJiIiInERTWPjo0brCBcv5p65PpceUHmSZLCZfPpnel/Z2OyQREQlC2TnZXDPuGhYdXsRV\nZ13FrIGziC0bunP9RwJNYRH+VGcXzRjD0DeHMurGUUG5joeIiIiELn/W2RpA9lFhWzi3zz87J5ue\n43vy8cGP6VqxK3MGzdGjfCIiclqfrPuEXtN7kW2ymXzlZG7ucrPbIUkRNIAc/lRnF03XQERERJyi\nOZAlIkz7ZBpxD8exas8qPun1CQuHLQzZwWPNayROUn6Jk0I1v7pe3JWMCRnc3Ohm+izqQ7Mhzdh9\nYLfbYYmIiANC9bNKQoPyS5yk/JJQoQFkCTo79u3gwiEXcuunt9KnSR/2TdhHl4u6uB2WiIiEmKio\nKCbfPZnv7vmOrNwskp5K4tHpj2qRPRERERERkRLQFBY+erSucIE8/7xjedz177uYkj6FhrYh8wbM\no3FS44C8t4iIhL/x/x3P0LShVPFU4a2b36LrxV3dDknQFBaRQHV20XQNRERExCmaA9kBKmwLF6jz\nf3vp29z+/u0cM8d44bIX6Nutr+PvKSIikefwb4fpNbEX87Pm06pMK94f+D7Vq1Z3O6yIpgHk8Kc6\nu2i6BiIiIuIUzYEsYePHHT/S9OGm3DT/Jq6tfy0Hnz0YloPHmtdInKT8EieFW35VLFeReUPnsbbv\nWnYd3UWtMbUY8OoATWshIhLCwu2zSoKL8kucpPySUKEBZHHF0eyjXPfMdZz94tlEm2h+HPAj0wZM\nIyY6xu3QREQkAjRr3Iyt47cysf1EJn83mbiBccxYPMPtsERERERERIKOprDwibRH60aPHs15553H\ntddee8r9/H3+Ho+HR6c/yvhvxlPpWCVe+fsr9Gzf02/HFxERKansnGxunXQrs3bPop6nHjP7zuSS\ncy9xO6yIoSkswl8k1dmrVq0CoHXr1sXaPxyvgYiIiAQHzYHsgEgqbAGGDBnCwIEDSUhIOOV+/jz/\naZ9MY8C8AWRHZTOs5TCG/mMoUVG6CV5ERILDjn07uP7F61mZs5JWZVrx7n3vkpSQ5HZYYU8DyOEv\n0ursktA1EBEREadoDmQBID09ne7du1OpUiVmzZoFwJtvvklsbCzDhw/nwIEDAAwcOJDOnTvz+eef\nM2nSJBYuXMi333572sFjf1n+zXLqDarHrZ/cytX1ryZzbCbDrh8WUYPHmtdInKT8EidFUn4lJSSR\nOjqV/93yP/b+tpfkZ5O5fvz1HP7tsNuhiYgLSlprp6amMmTIEKy1LFu2jD59+vDqq68yadIkN08j\nIkTSZ5UEnvJLnKT8klAROSN4YSg5OZmePXuSkJBAr169AOjRowfR0dH069ePuLg4ANq0acPChQtJ\nT08nMTGRdu3a0aBBA8fjW7tpLU0fbkrKzBTqVarHL4/8wrQB04gtG+v4e4uIiJRWi7NbsPnZzcz4\n2ww+3f4pVYdX5baXbiMnN8ft0EQkgEpaazdo0ICsrCyMMTRr1ozY2Fj69+/PPffc4+ZpiIiIiJwx\nDSCHuPj4+JN+fuutt0hKSiIjIwOAbdu2Ubt2bcqUKcOSJUvo1KkTq1atolWrVuzevduRmDb+vJHm\nQ5vT8vWWlI8pz8a7N7J05FJqxtV05P1CQUpKitshSBhTfomTIjm/bki5gYznMpjQYQKzfpxFpYcr\nMXDKQPKO5bkdmogESElq7ZycHOrVq8fOnTvJzc0N2NN+EtmfVeI85Zc4SfkloUIDyCGuWrVqJ/68\ncuVKzj//fKpXr36iqE1LSzuxiMdll13GokWL+Pbbb9m1axeVK1f2ayzpe9JpP6I9Tf/VlBxPDmtv\nXcuaJ9dwdp2z/fo+IiIigXRv93v5dcKvDG85nFfWv0LlBysz8q2ReDwet0MTEYeVpNbet28fFSpU\nwBjDunXr6NSpkysxi4iIiPibBpDPlDH++Sql43dF5OTkkJqaSuvWralWrRoZGRl89tlndO7c+cS+\n3bt35/rrr+eBBx5g0KBBlC9f/oxPH2DDtg20Ht6a+hPrs+foHpbfvJxvxn5Ds8bN/HL8cKB5jcRJ\nyi9xkvLLKyoqimHXD+PQ+EPce8G9PL3uaSoOqsjgqYN1R7KIg1wutUtUa7do0YK77rqLxMREunbt\nSpcuXc709KWY9FklTlJ+iZOUXxIqNIB8pqz1z1cpxcfHY61l0qRJ9O3bF/DeKZGenk5mZiaJiYn+\nOtM/WPPDGi4cciHnvXIeh3IOsfzm5Wx6ZhPt/tLOsfcUERFxU1RUFE//82kOPXOI+y+6n5e/fZkK\ngytwx7/uIDsn2+3wRMKOy6V2iWrtiRMnsmfPnjM9ZREREZGgowHkEFelShUyMzOpWrXqiUfs4uPj\nWbhwIddcc40j77n4y8Wc+9C5XPLGJcRExfBFvy9Y//R6DRyfguY1Eicpv8RJyq/CxUTHMKb3GLLG\nZzGq9Sje2fQOlYZW4qaJN5F5ONPt8ETET0pSa2/atIkaNWq4EWbE02eVOEn5JU5Sfkmo0AByGOjc\nuTO33HLLiZ8TExN58skniYr6v7/eZcuWMWjQIFasWMHw4cNZsGABM2fOZPbs2cV6D4/Hw4tzXyTx\ngUS6zupKQrkEfrj7B9Y+uZYLG17o93MSEREJBVFRUTxy3SNkPpfJC51e4JOfPyHu8ThSRqawfut6\nt8MTET8oTq2dmppKeno6q1evdiNEEREREUdpADkMzJkz56Sf77//ftq1O/lu4CZNmpCVlUX79u3J\nzs6mZcuWJCUlsX///tMe/65/30XFQRUZtGwQnep0Yu+QvSwftZzGSY39eh7hTPMaiZOUX+Ik5Vfx\n3XnFneyduJe5Peay5+gezp98Pk0GN+G9tPfcDk1EzkBxau06deqQkpJCq1atAhma+OizSpyk/BIn\nKb8kVGgAOULk5OTQqFEjALKysoiLi2P+/Pm0bNmS7OxTz9n49vdv8+DFD3J03FFmDpxJfJX4QIQs\nIiISkq685Eo2jtvIxrs2UrdSXXp+0JP4++N5dPqj5OTmuB2eiDggLS2Ntm3bsn37drdDEREREfE7\nY89kVYkwYoyxhV0LYwzhcI3mzJlD3bp1ueiiixg5ciRPPPEEw4cPp1OnTietHl1QuJy/iIiIW7KO\nZDHojUG8vfltsmOy6VipI8/c8AzNGjdzO7Sg4Ks1jNtxiHPCvc4G+OCDD8jNzaVFixYkJycXu104\nXQMREREJLv6sszWA7BMJhW1pRPr5i4iI+NPbS99m9Mej+SH6B2r9Xov72tzHwGsHEhMd43ZortEA\ncvhTnV00XQMRERFxij/rbE1hIRIAmtdInKT8Eicpv/zrhpQb2DhuIz8P/Jn2tdszIm0EsQ/H0n5E\nexZ/udjt8EREQpI+q8RJyi9xkvJLQoUGkEVEREQCLCkhibcHvc2R8UeYfuV0juQcoes7Xal8f2Vu\nfu5m0vekux2iiIiIiIgIoCksTtCjdYWL9PMXEREJlKPZRxn7n7FM/WIqv/zpF2r9Xou/n/N3hvYc\nSs24mm6H5xhNYRH+VGcXTddAREREnKI5kB2gwrZwkX7+IiIibti6ayuP/+dxPtryEfvK7aNWdvgO\nJmsAOfypzi6aroGIiIg4RXMgi4QYzWskTlJ+iZOUX+6on1ifqfdOZe/EvWy+ZzPd6nfjnY3vkPhs\nIokPJHLzczezdtNat8MUEQkK+qwSJym/xEnKLwkVkbvkt4iIiEgIaFirIVPvnQrATzt/YsLcCcz7\ncR4zp84k9vdYmlduTp82fehzaR9iolXaiYiIiIiIfwX9FBbGmHOBSUArIBN4DRhZ6HNwJ7erDDwP\nXI33TuuPgPustQeK2F+P1hUi0s9fREQkWGXnZPPawteY/r/pfH30a3LK5lArpxbta7end4fedLu4\nG1FRwf+wWahPYeF0rWqMuRp4HGgMbAFGWWvfDYVj5dtXdXYRdA1ERETEKREzB7Ix5ixgA7AeGAc0\nBCYAE6y1j52m7UKgETAIsL72u621HYvYv9DCtl69eqSnR+5K6MnJyWzbts3tMEREROQ01vywhimL\np/Dplk/ZZrZhsdSz9Whftz09Wvbg8haXB+UdyqE8gOx0rWqMaQcswTtA/T5wOfAg0M1a+2mwHyvf\n/qqzi6BaW0RERJwSSQPIQ/AWo3WttUd82wYDI4Ca1trDRbRrDaQB7a21ab5tLYD/AZdaaz8rpM3p\nbhRxVN6xPJZ8tYTZq2eT9nMaW3K2kF0+m0pHKtGkQhOu/POV3HbZbdSqVsu1GKX0li5dSkpKitth\nSJhSfomTlF+hyePxsOTrJUxdOpWVO1ayw+zgWJljxGXHcd5Z5/HXc//KjR1vJLlGstuhhvoAsqO1\nqm8wN9pae2m+tvOAStbaDsF8rALnW2Sd/fqi1xkyfwj7yuzjqrirmHz7ZKpXrV7oviJF0WeVOEn5\nJU5SfomT/FlnB99tKCe7DFh4vCD3eQd4GugIzDtFu93HC18Aa+0aY8xW4G/AHwaQAynjYAYfrP6A\nJRuX8PWur0n/PZ1D5Q8RnRNNLU8tLq5xMQMvHEivDr2oWK6im6GKn3z11Vf6UBDHKL/EScqv0BQV\nFUWXi7rQ5aIuJ7at37qemStmsuTHJTy98mmGfj2U6N+jSTiWQJMqTbgk+RIuu+gyOpzXISjvVA5S\njtWqxpiyQApwb4G27wBTjTGVrLWHgvhYpzTpw0mMWDyCg2UO0jOpJ/++/d+cVfGs0zUTKZQ+q8RJ\nyi9xkvJLQkWw/9/BOcDi/BustduNMUd9rxVVlJ8DfF/I9o2+1xx3NPsoazetZcXGFXz585ds2r+J\nndk7ORhzkLzYPModKUft6NqcV/08+jbpy1Utr6JhrYaBCE1ckJmZ6XYIEsaUX+Ik5Vf4+Ev9vzCm\n/pgTP+cdy2PZN8v4+IuP+V/6/5j2zTQmfDeBY2WPUe5oOeJNPMmVkmlaoyktGragQ9MONKzVMCTm\nVQ4gJ2vVhkCZQvbbiHdu4ibAuiA+1h94PB6e/e+zjEkdw5HoI9zU4CYm9Z+kGybkjOmzSpyk/BIn\nKb8kVAT7AHJVvIuRFPSr77XStKtf2mDyjuWxfe92tuzewra929ixfwe7Mnfxy8Ff2JG1g32/7yPT\nZvJbmd/wxHqI/i2ayrmVqfWnWjSOa0yPpB60PactbZu2pXxs+dKGISIiInLGYqJj/nCXMkD6nnSW\nfrOUz3/6nA27N7DgpwVM/3E6R5cehSgok12GiscqEhcTR83yNalTpQ6JZyVSu2pt6sTXoX6N+jRM\nbEhc5TiXziygnKxVq+Kdg7jgfr8CJt/xg/VYf5C+J51RaaPofXZvJvadSGzZ2KJ2FREREZEgEuwD\nyAH3zxf+yftb3ifP5JFn8jgWdQxPlAdPtMd7r0UexOTEUPZYWcpRjopRFYkrG0fdKnVJiU/h3Nrn\nckG9C7ig4QUqiuUELY4iTlJ+iZOUX5EnuUYyfbr2oU/XPn94bef+nXyx+QvW/7yeH3b9QPqv6Xy1\n5yuW/7KcI54jZEdlkxuTi+dPHrBg8gxRx6KI8kQR44khxsYQQwxRRBFlojCE5NTHUkr1E+tzZOKR\n0+8oUkL6rBInKb/EScovCRXBPoD8K1ClkO1Vfa+dql18SdsZU7z/icnz/XeUo+xnP+mk8yVfFqut\nRK5p06a5HYKEMeWXOEn5JaVlsRzz/ZdLrtvhOMHJWvX4Hb0Fj1813+vBfKyTFLfOFiktfVaJk5Rf\n4iTll4SCYB9A/p4CcxYbY5KA8hQ+P1v+dv0K2X4O8F5hDUJ19W8RERERcY2TtepPQK5v24p8+5wL\nHAM2BfmxTlCdLSIiIhLagn0VlPlAN2NMhXzbrgeOAstO066mMabN8Q3GmOZAA+BjJwIVERERkYjj\nWK1qrc0BlgDXFWjbC1hlrT0U5McSERERkTBhrLVux1AkY8xZwAbf19N4V30eD0yw1o7It99mYIm1\ntn++bQuARsBgvAt9jAV2W2tTAnYCIiIiIhK2nK5VjTFt8Q7WvgS8D1wBDAS6WWsXB/uxRERERCQ8\nBPUAMoAx5hxgEtAa72rPrwKjbL7AjTFb8BblffNtqwxMBK7Fe6f1h8AAa+2BAIYvIiIiImHM6VrV\nGNMdeAJoDGwFRlhrZxfYJyiPJSIiIiLhIdinsMBa+7219lJrbQVrbW1r7UhbYNTbWtsgf0Hu25bl\n29YWWAf0AL41xowyxVjFwxhT2RjzujHmgDEm0xgzwxgT589zk9BnjDnXGLPYGHPEGPNLcfLLGJNs\njPEU8jUzUHFLaDDGNDTGvGKM+doYk2eM+ayY7dR/yWmVJr/Uf0lxGWOuM8Z8YIzZYYw5ZIxZa4y5\nvhjtQq7/OtNa1VobZ609y1r7/wq70cFaO9dae761tpy19s+FDdK6eSygGbALeKO4tRCE5t+1BJ5q\nbXGSam1xkmptcYpbdXawL6J3Roz3scJPgfVAd7yPCFU8+AAACSNJREFUFU7Au3L0Y6dpPhvv43u3\n4n18bxzexUM6OhWvhJYzzC/wPuq5Mt/PGf6OUUJeU+AyYDUl66/Vf0lxlDa/QP2XnN4DwBbgfrz5\ncTkw0xhTzVr70inaqf8KIaq1xUmqtSUAVGuLk1Rri1NcqbODfgqLM2GMGQI8CNS11h7xbRsMjABq\nWmsPF9GuNZAGtLfWpvm2tQD+B1xqrS3WbyYlvJ1BfiXjfdTzSmutFnWUYjHGzAaqWWs7n2Y/9V9S\nYiXIL/VfUizGmLhCpjx4C2hlrW1YRBv1XyFGtbY4SbW2BJJqbXGSam3xJ7fq7KCfwuIMXQYsPF5w\n+LwDlOfUI+yX4V0sJO34BmvtGrz/kP/mRKASkkqbXyJOUv8lIq4rYs2JL4Fap2im/iv0qNYWJ6nW\nlmCk/ktEXOVWnR3uA8jnAN/n32Ct3Q4c9b1W7HY+G0/TTiJLafPruNd9cyHtNMaMN8bEOhGkRBz1\nXxII6r+kNNoAm07xuvqv0KNaW5ykWluCkfovCQT1X1JSjtfZYT0HMlAV72rYBf3qe6007er7IS4J\nD6XNr9/xrta+CMgCUoBHgAZ4VzwXORPqv8RJ6r+kVIwxXYCrgX+eYjf1X6FHtbY4SbW2BCP1X+Ik\n9V9SYoGqs8N9AFkk6FhrdwP35du03BizF3jJGHOetfZbl0ITETkl9V9SGsaYesBbwHvW2unuRiMi\n4U6fVSISqtR/SUkFss4O9yksfgWqFLK9qu81f7eTyOLPPPkP3hWlLz7ToCTiqf+SQFP/JUUyxlQF\n5uOdX+3m0+yu/iv0qNYWJ6nWlmCk/ksCTf2XFCrQdXa4DyB/T4G5PIwxSXgXXihs7o8i2/kUNWeI\nRKbS5ldhbIHvIqWl/ksCTf2XFMoYUw6YB0TjXU08+zRN1H+FHtXa4iTV2hKM1H9JoKn/kj9wo84O\n9wHk+UA3Y0yFfNuux7vwwrLTtKtpjGlzfIMxpjneeWc+diJQCUmlza/CXIf3A2Gdn2KTyKX+SwJN\n/Zf8gTEmGu8dMw2By6y1+4vRTP1X6FGtLU5SrS3BSP2XBJr6LzmJW3W2sTZ8f4lhjDkL2OD7ehrv\nxR0PTLDWjsi332ZgibW2f75tC4BGwGC8/1jHAruttSkBOwEJaqXNL2PMCKASkIZ3YvyOwIPAR9ba\nfwT0JCSo+X6reDneR5YG4s2bkb6X51lrs9V/SWmVJr/Uf0lxGWMmA/3wzuO3psDLX1hrc9V/hT7V\n2uIk1driNNXa4iTV2uIUt+rssF5Ez1qb6VuNcBIwF++Kg+OBUQV2jeKPd2P/A5gITPG99iEwwNGA\nJaScQX59DwwC+gLlgJ/xFsVjnI5ZQk51YDYnP670ru97fby5o/5LSqs0+aX+S4qrK97cer6Q19R/\nhQnV2uIk1doSAKq1xUmqtcUprtTZYX0HsoiIiIiIiIiIiIiUXrjPgSwiIiIiIiIiIiIipaQBZBER\nEREREREREREplAaQRURERERERERERKRQGkAWERERERERERERkUJpAFlERERERERERERECqUBZBER\nEREREREREREplAaQRURERERERERERKRQGkAWEfEzY8x1xpg+hWxfYox5142YCsRR2xiTZYypX8z9\nLzbG7DfGVHI6NhERERGRU1GtLSISeMZa63YMIiJhxRgzG6hmre1cYPs5QK619id3IjsRx8tAZWvt\nTSVo8wmwwlo72rnIREREREROTbW2iEjg6Q5kEZEAsdZ+HwQFbSWgNzClhE3fAO4wxuhzQ0RERESC\njmptERHnqHMSEfEjY8zrwN+BjsYYjzHmmDHmMd9rS/M/VmeMGWmM2WeMaWmMWWOMOWqMWWGMSTbG\nJBhj3jPGHDLGfGeM6VTIe/Uzxqw3xmQbY7YZYwYXI8RewFFgSYFjDTHG/GiM+c0Ys9sY87Expnq+\nXeYC1YBuJb8qIiIiIiJnTrW2iIg7YtwOQEQkzIwG6gJVgDsBA+zwvVZwziALlAdeAcYBR4AXgBnA\n78DHwEvAw8C7xpg61tpsAF8B+yQwFlgGXAw8bow5Yq391yni6wx8bvPNX2SM6Q08AjwEfIe3eO0M\nVDgRqLWHjDEbgEuB+SW4HiIiIiIi/qJaW0TEBRpAFhHxI2vtVmPMAbxzzK8pRpNY4F5rbSp4F93A\nW8gOt9ZO8G37BdgAdAQW+h6NewwYba19wnecxcaYCsAwY8zLtugJ7i8G3i+wrQWwyFr7Sr5tBfcB\n+BpoWYxzEhERERHxO9XaIiLu0BQWIiLuyjle0Ppsxnu3xJIC2wBq+763wXs3xX+MMdHHv3xtagJJ\np3i/mkBGgW1fAVf4HvNrcYq51zJ87UVEREREQoFqbRERP9AAsoiIuw4V+DnH9z3z+AZrba7vj7G+\n79XwPq73HZCb7+szvAVxnVO8XyzeR/bymwoMAa4DVgN7jDGPG2NMgf1+zxeDiIiIiEiwU60tIuIH\nmsJCRCT0HPB9vxzYW8jrP5ym7Vn5N/gewXseeN73WN9NwBhgOzA5365n5XtvEREREZFwpFpbRKQA\nDSCLiPhfDs7ePbAK7+rOta21C0rY9gegflEvWmt/AcYZY24F/lzg5XrAphK+n4iIiIiIP6nWFhEJ\nMA0gi4j43/dAd2PM1XhXhd5prd1VgvYFH2c7ibX2oDFmFPCCMaYesBzvlERnAynW2h6naJ4GXHXS\nmxnzb7x3O6wGDuJdFboRsLhA2+Z4V6IWEREREXGLam0RkQDTHMgiIv73L2ARMAX4HOhfwvaFreps\n82+31j7jO+5leFdxngncgLfAPZX/An82xuRf/GMV0B7v/GzzgKuBftbaD4/vYIy5CIj3tRcRERER\ncYtqbRGRADPe6XhERCRSGGO+BGZYa8eXoM1TwMXW2r86F5mIiIiISGhTrS0i4UgDyCIiEcYY0xMY\nBzSy1nqKsX95IB3oYa1d4XR8IiIiIiKhSrW2iIQjzYEsIhJhrLX/McbUB2rjXf35dOoCo1TQioiI\niIicmmptEQlHugNZRERERERERERERAqlRfREREREREREREREpFAaQBYRERERERERERGRQmkAWURE\nREREREREREQKpQFkERERERERERERESmUBpBFREREREREREREpFD/HwlOaxQdMA94AAAAAElFTkSu\nQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "\n", - "#Get the data\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(outputfile_3, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time, T, Q, r = np.loadtxt(outputfile_3, usecols=(4,5,6,7), unpack=True)\n", - "Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt(outputfile_3, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(2, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{33}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{33}$\\,(MPa)', size = 15)\n", - "plt.plot(e33,s33, c='black', label='direction 3')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time t (s)', size = 15)\n", - "plt.ylabel(r'temperature $\\theta$\\,(K)',size = 15)\n", - "plt.plot(time,T, c='black', label='temperature')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 3)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "plt.plot(time,Wm, c='black', label=r'$W_m$')\n", - "plt.plot(time,Wm_r, c='green', label=r'$W_m^r$')\n", - "plt.plot(time,Wm_ir, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time,Wm_d, c='red', label=r'$W_m^d$')\n", - "plt.legend(loc=3)\n", - "\n", - "ax = fig.add_subplot(2, 2, 4)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_t$',size = 15)\n", - "plt.plot(time,Wt, c='black', label=r'$W_t$')\n", - "plt.plot(time,Wt_r, c='green', label=r'$W_t^r$')\n", - "plt.plot(time,Wt_ir, c='blue', label=r'$W_t^{ir}$')\n", - "plt.legend(loc=3)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Notebooks/Umats/Thermomechanical/Plasticity/Loop_kin/EPKCP.ipynb b/Notebooks/Umats/Thermomechanical/Plasticity/Loop_kin/EPKCP.ipynb deleted file mode 100644 index b92ae4302..000000000 --- a/Notebooks/Umats/Thermomechanical/Plasticity/Loop_kin/EPKCP.ipynb +++ /dev/null @@ -1,375 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plasticity with isotropic hardening" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import pylab\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import rc\n", - "import simcoon as sim\n", - "import os\n", - "from IPython.display import HTML\n", - "dir = os.path.dirname(os.path.realpath('__file__'))\n", - "\n", - "plt.rc('text', usetex=True)\n", - "plt.rc('font', family='serif')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The elastic-plastic (isotropic with kinematical hardening) constitutive law implemented in SMART+ is a rate independent, isotropic, von Mises type material with power-law isotropic hardening. \n", - "Eight parameters are required for the thermomechanical version: \n", - "1. The density $\\rho$\n", - "2. The specific heat $c_p$\n", - "3. The Young modulus $E$,\n", - "4. The Poisson ratio $\\nu$,\n", - "5. The coefficient of thermal expansion $\\alpha$,\n", - "6. The von Mises equivalent yield stress limit $\\sigma_{Y}$,\n", - "7. The hardening parameter $k$,\n", - "8. The hardening exponent $m$.\n", - "9. The kinematic hardening parameter $k_X$\n", - "\n", - "The constitutive law is given by the set of equations \n", - "\n", - "$$\\begin{matrix} {\\sigma}_{ij}=L_{ijkl}\\left({\\varepsilon}^{\\textrm{tot}}_{kl}-\\alpha_{kl}\\left(T-T^{\\textrm{ref}}\\right)-{\\varepsilon}^{\\textrm{p}}_{kl}\\right),\\\\\n", - "\\dot{\\varepsilon}^{\\textrm{p}}_{ij}=\\dot{p}\\Lambda_{ij}, \\quad \\Lambda_{ij}=\\frac{3}{2}\\frac{\\sigma'_{ij}}{\\lvert \\mathbf{\\sigma}\\rvert}, \\quad \\sigma'_{ij}=\\sigma_{ij}-\\frac{1}{3}\\sigma_{kk}\\delta_{ij}, \\quad \\lvert q \\rvert = \\sqrt{\\frac{3}{2}q'_{kl}q'_{kl}},\\\\ \\Phi=\\lvert \\mathbf{\\sigma}-\\mathbf{X} \\rvert -\\sigma_{Y}-kp^m\\leq 0, \\quad \\dot{p}\\geq0,~~~ \\dot{p}~\\Phi=0, \\end{matrix}$$\n", - "\n", - "where ${\\varepsilon}^{\\textrm{p}}_{ij}$ is the plastic strain tensor, $p$ is the plastic multiplier, $\\sigma'_{ij}$ is the deviatoric part of the stress and $\\overline{\\sigma}$ is the von Mises equivalent stress (Lemaitre and Chaboche, 2002). Moreover, $T^{\\textrm{ref}}$ is a reference temperature (usually the temperature at the beginning of the analysis).\n", - "\n", - "In SMART+ the elastoplastic material constitutive law is implemented using a *return mapping algorithm*, with use of the *convex cutting plane* algorithm (Simo and Hughes, 1998). The updated stress is provided for 1D, plane stress, and generalized plane strain/3D analysis according to the forms of elastic isotropic materials.\n", - "The updated work, and internal heat production $r$ are determined with the algorithm presented in the *simcoon* documentation.\n", - "\n", - "As a start we should input the name of the UMAT as well as the list of parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "umat_name = 'EPKCP' #This is the 5 character code for the elastic-plastic subroutine\n", - "nstatev = 14 #The number of scalar variables required, only the initial temperature is stored here\n", - "\n", - "rho = 4.4\n", - "c_p = 0.656\n", - "E = 113800\n", - "nu = 0.342\n", - "alpha = 0.86E-5\n", - "sigma_Y = 500\n", - "H = 1200\n", - "beta = 0.25\n", - "kX = 8000\n", - "\n", - "##local orientation\n", - "psi_rve = 0.\n", - "theta_rve = 0.\n", - "phi_rve = 0.\n", - "solver_type = 0\n", - "\n", - "#Define the properties\n", - "props = np.array([rho, c_p, E, nu, alpha, sigma_Y, H, beta, kX])\n", - "path_data = 'data'\n", - "path_results = 'results'\n", - "\n", - "#Run the simulation\n", - "pathfile = 'path.txt'\n", - "outputfile = 'results_EPICP.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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nZdmTu3DhQrrrrruIiOjxxx+n7OxsTeRqgbMyc3NzZZegoCCbfFBWsAiRrDeg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\n3ngjLrvsMoeyjh8/jubmZuswXkNDAw4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TueJFx5J2vkxaGZP3ZuLoOFJZowF86uI9kMoK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\nv0iPDRiKBRbDpH3OHcRYXJFkxgR0zi3VFSzFaQGFnoQOHx9++CE333w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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#prepare the load\n", - "fig = plt.figure()\n", - "outputfile_global = 'results_EPICP_global-0.txt'\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global = path + outputfile_global\n", - "\n", - "#Get the data\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(P_global, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time, T, Q, r = np.loadtxt(P_global, usecols=(4,5,6,7), unpack=True)\n", - "Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt(P_global, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(2, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11,s11, c='black', label='direction 1')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time t (s)', size = 15)\n", - "plt.ylabel(r'temperature $\\theta$\\,(K)',size = 15)\n", - "plt.plot(time,T, c='black', label='temperature')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 3)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "plt.plot(time,Wm, c='black', label=r'$W_m$')\n", - "plt.plot(time,Wm_r, c='green', label=r'$W_m^r$')\n", - "plt.plot(time,Wm_ir, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time,Wm_d, c='red', label=r'$W_m^d$')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 4)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_t$',size = 15)\n", - "plt.plot(time,Wt, c='black', label=r'$W_t$')\n", - "plt.plot(time,Wt_r, c='green', label=r'$W_t^r$')\n", - "plt.plot(time,Wt_ir, c='blue', label=r'$W_t^{ir}$')\n", - "plt.legend(loc=3)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test now the increments " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#Run the simulations - 1 increment\n", - "pathfile = 'path_1.txt'\n", - "outputfile = 'results_EPICP_1.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1 = 'results_EPICP_1_global-0.txt'\n", - "\n", - "#Run the simulations - 10 increments\n", - "pathfile = 'path_10.txt'\n", - "outputfile = 'results_EPICP_10.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_10 = 'results_EPICP_10_global-0.txt'\n", - "\n", - "#Run the simulations - 100 increments\n", - "pathfile = 'path_100.txt'\n", - "outputfile = 'results_EPICP_100.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_100 = 'results_EPICP_100_global-0.txt'\n", - "\n", - "#Run the simulations - 1000 increments\n", - "pathfile = 'path_1000.txt'\n", - "outputfile = 'results_EPICP_1000.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1000 = 'results_EPICP_1000_global-0.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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cHBhrU3fx3iAiIuo9Joi7iUErdVd37436+no+1ZPiJhHvr1BJ2sDtn332GRob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\nA9XV1Ro7dmyym4E0x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F155ZWB54uKito8708S19TUJLK5cLZ1ktYG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\nIIKlgx74nhoAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global_1 = path + outputfile_global_1\n", - "P_global_10 = path + outputfile_global_10\n", - "P_global_100 = path + outputfile_global_100\n", - "P_global_1000 = path + outputfile_global_1000\n", - "\n", - "#Get the data\n", - "e11_1, e22_1, e33_1, e12_1, e13_1, e23_1, s11_1, s22_1, s33_1, s12_1, s13_1, s23_1 = np.loadtxt(P_global_1, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1, T_1, Q_1, r_1 = np.loadtxt(P_global_1, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1, Wm_r_1, Wm_ir_1, Wm_d_1, Wt_1, Wt_r_1, Wt_ir_1 = np.loadtxt(P_global_1, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "e11_10, e22_10, e33_10, e12_10, e13_10, e23_10, s11_10, s22_10, s33_10, s12_10, s13_10, s23_10 = np.loadtxt(P_global_10, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_10, T_10, Q_10, r_10 = np.loadtxt(P_global_10, usecols=(4,5,6,7), unpack=True)\n", - "Wm_10, Wm_r_10, Wm_ir_10, Wm_d_10, Wt_10, Wt_r_10, Wt_ir_10 = np.loadtxt(P_global_10, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "e11_100, e22_100, e33_100, e12_100, e13_100, e23_100, s11_100, s22_100, s33_100, s12_100, s13_100, s23_100 = np.loadtxt(P_global_100, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_100, T_100, Q_100, r_100 = np.loadtxt(P_global_100, usecols=(4,5,6,7), unpack=True)\n", - "Wm_100, Wm_r_100, Wm_ir_100, Wm_d_100, Wt_100, Wt_r_100, Wt_ir_100 = np.loadtxt(P_global_100, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "e11_1000, e22_1000, e33_1000, e12_1000, e13_1000, e23_1000, s11_1000, s22_1000, s33_1000, s12_1000, s13_1000, s23_1000 = np.loadtxt(P_global_1000, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1000, T_1000, Q_1000, r_1000 = np.loadtxt(P_global_1000, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1000, Wm_r_1000, Wm_ir_1000, Wm_d_1000, Wt_1000, Wt_r_1000, Wt_ir_1000 = np.loadtxt(P_global_1000, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "fig = plt.figure()\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(2, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11_1,s11_1, linestyle='None', marker='D', color='black', markersize=10, label='1 increment')\n", - "plt.plot(e11_10,s11_10, linestyle='None', marker='o', color='black', markersize=10, label='10 increment')\n", - "plt.plot(e11_100,s11_100, linestyle='None', marker='x', color='black', markersize=10, label='100 increments')\n", - "plt.plot(e11_1000,s11_1000, c='black', label='1000 increments')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time t (s)', size = 15)\n", - "plt.ylabel(r'temperature $\\theta$\\,(K)',size = 15)\n", - "plt.plot(time_1,T_1, linestyle='None', marker='D', color='black', markersize=10, label='1 increment')\n", - "plt.plot(time_10,T_10, linestyle='None', marker='o', color='black', markersize=10, label='10 increment')\n", - "plt.plot(time_100,T_100, linestyle='None', marker='x', color='black', markersize=10, label='100 increments')\n", - "plt.plot(time_1000,T_1000, c='black', label='1000 increments')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 3)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "#1 increment\n", - "plt.plot(time_1,Wm_1, linestyle='None', marker='D', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_1,Wm_r_1, linestyle='None', marker='D', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_1,Wm_ir_1, linestyle='None', marker='D', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_1,Wm_d_1, linestyle='None', marker='D', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#10 increment\n", - "plt.plot(time_10,Wm_10, linestyle='None', marker='o', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_10,Wm_r_10, linestyle='None', marker='o', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_10,Wm_ir_10, linestyle='None', marker='o', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_10,Wm_d_10, linestyle='None', marker='o', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#100 increments\n", - "plt.plot(time_100,Wm_100, linestyle='None', marker='x', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_100,Wm_r_100, linestyle='None', marker='x', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_100,Wm_ir_100, linestyle='None', marker='x', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_100,Wm_d_100, linestyle='None', marker='x', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#1000 increments\n", - "plt.plot(time_1000,Wm_1000, c='black', label=r'$W_m$')\n", - "plt.plot(time_1000,Wm_r_1000, c='green', label=r'$W_m^r$')\n", - "plt.plot(time_1000,Wm_ir_1000, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time_1000,Wm_d_1000, c='red', label=r'$W_m^d$')\n", - "##\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 4)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_t$',size = 15)\n", - "#1 increment\n", - "plt.plot(time_1,Wt_1, linestyle='None', marker='D', color='black', markersize=10)#, label=r'$W_t$')\n", - "plt.plot(time_1,Wt_r_1, linestyle='None', marker='D', color='green', markersize=10)#, label=r'$W_t^r$')\n", - "plt.plot(time_1,Wt_ir_1, linestyle='None', marker='D', color='blue', markersize=10)#, label=r'$W_t^{ir}$')\n", - "#10 increment\n", - "plt.plot(time_10,Wt_10, linestyle='None', marker='o', color='black', markersize=10)#, label=r'$W_t$')\n", - "plt.plot(time_10,Wt_r_10, linestyle='None', marker='o', color='green', markersize=10)#, label=r'$W_t^r$')\n", - "plt.plot(time_10,Wt_ir_10, linestyle='None', marker='o', color='blue', markersize=10)#, label=r'$W_t^{ir}$')\n", - "#100 increments\n", - "plt.plot(time_100,Wt_100, linestyle='None', marker='x', color='black', markersize=10)#, label=r'$W_t$')\n", - "plt.plot(time_100,Wt_r_100, linestyle='None', marker='x', color='green', markersize=10)#, label=r'$W_t^r$')\n", - "plt.plot(time_100,Wt_ir_100, linestyle='None', marker='x', color='blue', markersize=10)#, label=r'$W_t^{ir}$')\n", - "#1000 increments\n", - "plt.plot(time_1000,Wt_1000, c='black', label=r'$W_t$')\n", - "plt.plot(time_1000,Wt_r_1000, c='green', label=r'$W_t^r$')\n", - "plt.plot(time_1000,Wt_ir_1000, c='blue', label=r'$W_t^{ir}$')\n", - "##\n", - "plt.legend(loc=2)\n", - "\n", - "plt.show()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Notebooks/Umats/Thermomechanical/Plasticity/Loops_iso/EPICP.ipynb b/Notebooks/Umats/Thermomechanical/Plasticity/Loops_iso/EPICP.ipynb deleted file mode 100644 index c5ef31b2a..000000000 --- a/Notebooks/Umats/Thermomechanical/Plasticity/Loops_iso/EPICP.ipynb +++ /dev/null @@ -1,374 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plasticity with isotropic hardening" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import pylab\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import rc\n", - "import simcoon as sim\n", - "import os\n", - "from IPython.display import HTML\n", - "dir = os.path.dirname(os.path.realpath('__file__'))\n", - "\n", - "plt.rc('text', usetex=True)\n", - "plt.rc('font', family='serif')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The elastic-plastic (isotropic hardening) constitutive law implemented in SMART+ is a rate independent, isotropic, von Mises type material with power-law isotropic hardening. \n", - "Eight parameters are required for the thermomechanical version: \n", - "1. The density $\\rho$\n", - "2. The specific heat $c_p$\n", - "3. The Young modulus $E$,\n", - "4. The Poisson ratio $\\nu$,\n", - "5. The coefficient of thermal expansion $\\alpha$,\n", - "6. The von Mises equivalent yield stress limit $\\sigma_{Y}$,\n", - "7. The hardening parameter $k$,\n", - "8. The hardening exponent $m$.\n", - "\n", - "The constitutive law is given by the set of equations \n", - "\n", - "$$\\begin{matrix} {\\sigma}_{ij}=L_{ijkl}\\left({\\varepsilon}^{\\textrm{tot}}_{kl}-\\alpha_{kl}\\left(T-T^{\\textrm{ref}}\\right)-{\\varepsilon}^{\\textrm{p}}_{kl}\\right),\\\\\n", - "\\dot{\\varepsilon}^{\\textrm{p}}_{ij}=\\dot{p}\\Lambda_{ij}, \\quad \\Lambda_{ij}=\\frac{3}{2}\\frac{\\sigma'_{ij}}{\\overline{\\sigma}}, \\quad \\sigma'_{ij}=\\sigma_{ij}-\\frac{1}{3}\\sigma_{kk}\\delta_{ij}, \\quad \\overline{\\sigma}=\\sqrt{\\frac{3}{2}\\sigma'_{kl}\\sigma'_{kl}},\\\\ \\Phi=\\overline{\\sigma}-\\sigma_{Y}-kp^m\\leq 0, \\quad \\dot{p}\\geq0,~~~ \\dot{p}~\\Phi=0, \\end{matrix}$$\n", - "\n", - "where ${\\varepsilon}^{\\textrm{p}}_{ij}$ is the plastic strain tensor, $p$ is the plastic multiplier, $\\sigma'_{ij}$ is the deviatoric part of the stress and $\\overline{\\sigma}$ is the von Mises equivalent stress (Lemaitre and Chaboche, 2002). Moreover, $T^{\\textrm{ref}}$ is a reference temperature (usually the temperature at the beginning of the analysis).\n", - "\n", - "In SMART+ the elastoplastic material constitutive law is implemented using a *return mapping algorithm*, with use of the *convex cutting plane* algorithm (Simo and Hughes, 1998). The updated stress is provided for 1D, plane stress, and generalized plane strain/3D analysis according to the forms of elastic isotropic materials.\n", - "The updated work, and internal heat production $r$ are determined with the algorithm presented in the *simcoon* documentation.\n", - "\n", - "As a start we should input the name of the UMAT as well as the list of parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "umat_name = 'EPICP' #This is the 5 character code for the elastic-plastic subroutine\n", - "nstatev = 8 #The number of scalar variables required, only the initial temperature is stored here\n", - "\n", - "rho = 4.4\n", - "c_p = 0.656\n", - "E = 113800\n", - "nu = 0.342\n", - "alpha = 0.86E-5\n", - "sigma_Y = 500\n", - "H = 1600\n", - "beta = 0.25\n", - "\n", - "psi_rve = 0.\n", - "theta_rve = 0.\n", - "phi_rve = 0.\n", - "solver_type = 0\n", - "\n", - "#Define the properties\n", - "props = np.array([rho, c_p, E, nu, alpha, sigma_Y, H, beta])\n", - "path_data = 'data'\n", - "path_results = 'results'\n", - "\n", - "#Run the simulation\n", - "pathfile = 'path.txt'\n", - "outputfile = 'results_EPICP.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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9W0rZqNq5QwAOSil/X+1cjXm6pcA8WEr5Qa3XJFU/ZznPebZCvOcow3wpw3wp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+Tlh7z8kJefqOR1auSUZo7PDLuM0DDBDAAAUEfowQwA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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#prepare the load\n", - "fig = plt.figure()\n", - "outputfile_global = 'results_EPICP_global-0.txt'\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global = path + outputfile_global\n", - "\n", - "#Get the data\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(P_global, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time, T, Q, r = np.loadtxt(P_global, usecols=(4,5,6,7), unpack=True)\n", - "Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt(P_global, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(2, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11,s11, c='black', label='direction 1')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time t (s)', size = 15)\n", - "plt.ylabel(r'temperature $\\theta$\\,(K)',size = 15)\n", - "plt.plot(time,T, c='black', label='temperature')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 3)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "plt.plot(time,Wm, c='black', label=r'$W_m$')\n", - "plt.plot(time,Wm_r, c='green', label=r'$W_m^r$')\n", - "plt.plot(time,Wm_ir, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time,Wm_d, c='red', label=r'$W_m^d$')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 4)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_t$',size = 15)\n", - "plt.plot(time,Wt, c='black', label=r'$W_t$')\n", - "plt.plot(time,Wt_r, c='green', label=r'$W_t^r$')\n", - "plt.plot(time,Wt_ir, c='blue', label=r'$W_t^{ir}$')\n", - "plt.legend(loc=3)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "## Test now the increments " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#Run the simulations - 1 increment\n", - "pathfile = 'path_1.txt'\n", - "outputfile = 'results_EPICP_1.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1 = 'results_EPICP_1_global-0.txt'\n", - "\n", - "#Run the simulations - 10 increments\n", - "pathfile = 'path_10.txt'\n", - "outputfile = 'results_EPICP_10.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_10 = 'results_EPICP_10_global-0.txt'\n", - "\n", - "#Run the simulations - 100 increments\n", - "pathfile = 'path_100.txt'\n", - "outputfile = 'results_EPICP_100.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_100 = 'results_EPICP_100_global-0.txt'\n", - "\n", - "#Run the simulations - 1000 increments\n", - "pathfile = 'path_1000.txt'\n", - "outputfile = 'results_EPICP_1000.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1000 = 'results_EPICP_1000_global-0.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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cc801eP7553H77bfj6quvxsUXX4yvf/3rsFqtePDBB/GPf/wD3/3ud7Fp0yb85S9/weHD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ZtZh3Hb5glfu3YNgwYNwvnz55GcnIyMjAzFxehqk59FRERERESkZo4Wx7PMEHNFxnFKSgq2b9+O\n8ePHIzU1FZ06dUJxcTGaN29eq3bn5+ejtLTU4TGOFsL7+eefK9+LUp6xMxbCIyLyBhqNBgMHDsTg\nwYORlJSEnj17OpwhbPler+n7n24eIybczF5UxPz58/Hdd98hNzcXw4YNw6pVqwAoX6bES5SIiIjI\nmzBiwj+oYaxNvs3VURUxMTHIzs6usR1NmjTBjRs30KFDB5SVleGxxx5Dt27d8OmnnyIlJQV/+tOf\nbup1rd8bwKgKIvIN9r6zrb311ltITEzEO++8g2PHjjE+oo6cMdbmDGI3U5oFrNFo8L///Q+7d+/G\n0KFDsWrVqsq/livNGLb+KzoREREREZE/cPWM42nTpqFx48YOjwkMDMS6detw5MgRFBYWYtasWfjt\nt9/wzjvvYNeuXWjdujW6dOmCvn37olOnTnjkkUfw3HPPwWAwOHxeRzOOjUYjZxwTkdeJjIy0ewV8\neXk54uPjkZCQgO3bt2Py5Mm8Yt7DWCB2A+tYCaB6kfi1117DRx99hN69e1cb8DBWwrvk5OR4ugnk\nw3h+kSvx/CIi8j3+8t3urMLxiBEj0KVLF4ev1blzZ0RFRWHVqlXQ6/U4deoU5s+fj3379uH8+fP4\nv//7P5w4cQLjxo3D119/jaVLl2Lv3r1o1aoVHnjgAQwYMADNmjXDI488goSEhBp/x2Ph2D385bPi\nbdgv6nMzfWKvnmUwGPDQQw9h/fr1OHz4MAYPHuzweHIPFojdQOmvJpYTf9SoUViyZAnuvPNObNiw\nAWlp/8/evcdHVd/5H39/UQFpTSYgtq5aktCLlhQCCWB/QZdboIJtLSD+qrjKVcC2WytQLOvW6iKX\nUNfqWkXcn7VWK3LRWmIVCNCHUrdeGrB4WWoyYWu1uJJMaNetVvj8/phLJ5OZIZeZzJmZ1/PxOA84\nZ86Z+Z58zpz5zme+53PWJVyXGisAAAAA0DGdTRwfPXpUQ4cOVUVFRbuRxH379lVFRYU+85nPaNmy\nZZF6xtHJjEAgoDvvvFN+v1+HDx/WbbfdpmeffVZvvfWWfvnLX+qZZ57RhRdeqNdee03/8i//oocf\nflglJSX68pe/rAkTJqiiokJz587VDTfcQOIYQE6ITfq++uqrGjFihF555RXt379f5eXlSddHz6EG\ncQ+JV3t4+/bt+tKXvqT3339f+/fv19ChQxOuCwAAkK2oQZwfqEGMbBeul1lQUKDHHntM999/v957\n7z3169dPc+bM0dixY7Vs2TJVV1frsssui2wXr65wbF3kFStWaOnSpaqpqYl8z/vwww+1c+dOXXTR\nRbruuuv09ttv68UXX9Tbb7+t0047TZWVlTp8+LCuv/56lZeX64477ohsS41jAF7RkVrDhw4d0owZ\nM9TY2Kizzz5bTzzxhAYNGpRwfc5HnZOKvjYJ4jSJ9waJ7iQ0NDRozJgx+uhHP6q6ujqtX7++3Y3r\nSBIDAIBcQII4P5AgRi5L9v2sM8najiSOA4GAfvGLX+jyyy/XwoUL9cYbb+jFF1+M3Bxv3Lhx+uMf\n/6h//ud/1pAhQ3TLLbd0O3F87NgxXX755TIz/fWvf1W/fv00e/ZsTZs2TUePHiVRAyChE+WvWlpa\n9IUvfEFNTU1655135Pf7VVxc3PMNzWHcpM7DkpWVuO666zR58mQ551RXV6ehQ4fGvXEdZSWyDzWS\nkE4cX0gnji8AyD2c21MnuixFtM7eCM/n8+miiy5qUx4itlSFJD377LPy+/3q1auXNm3apObmZu3d\nu1f33HOPHn30UX384x/XkiVLVFxcrEceeUSf+9zntHDhQl122WWaOXOmbrzxxg5dol1VVaVrr71W\n5557rn75y19qx44d2rNnj5588knNmjVLo0aN0re+9a2cL1XBe8WbiIv3xItJsrIQb775piorK/XB\nBx/ooosukt/vV01NDeUjPIgEcZokeoP07t1bv/71r3XkyBFt3749UlYi3vrR9bAAAAAAAJmRqhvh\nJRL+Ppgocdza2qqioiLV1dXJ7/fr9NNP1+7du/XnP/9Zr7zyim6++WatX79eZqZ/+qd/UllZmZ56\n6imNHDlSy5cv14wZMzR79mytXr26zXfO48ePa/v27Xr33Xf1/vvvt2nT+++/r5deekkvv/yyCgoK\nIstra2t16NChhDWOw/ufK8ljACcWL6e1f/9+DRs2TBUVFRo1apRuv/32uD+IwSPMLG+m4O6m17Zt\n26ylpSUy39LSYosXL7aWlhb78MMPbdy4cda7d29btWpVZHm06PUBAAByQagPlvG+IFP297WBbBH7\nvTAs+vteS0uLbdu2rc1jc+fOtblz57b7Thm7PPZ5Fi9ebH6/v813zwMHDti6detMkl188cX2mc98\nxj7ykY/YyJEjbciQIXbdddfZ8OHDrXfv3iYp4dSnTx/bsmVLpD1NTU1WVlZmTU1Ncfc52Xfa2H0G\nkFvC7/8HH3zQTj31VLvpppts0aJF5L7SLBV97Yx3JHty6olOa7yDvKWlxRYtWmSXXXaZ9enTx6ZP\nn570g5MPTQAAkEtIEOfHRIIYOLFMJ45bW1ttz5499s///M8myYqKipImh8PTpEmT2jx/U1NT3O+9\n8dpJ4hjIDYnOX9EOHz5skyZNMkl27733Jk0CkyROnVT0tSkxkWKJSkX07dtXGzdu1EUXXaT77rsv\ncglSvKH1lJbIXtRIQjpxfCGdOL4AIPdwbveeqVOnat++fe2W91SpiuPHj2vYsGF699135ff7deqp\np3ao3b/61a9055136tprr9Utt9yiQYMGdfgy8aqqqjbtib5BXiAQiOxjJusc817xJuLiLVVVVbr6\n6qsTvudffvlllZWV6fXXX9dLL72kxx9/XMuWLYtbnkfi3lteQ4I4BcIfbMeOHdOmTZt0xRVXRGqt\nPPDAA7rjjjt05513auTIkRowYECbbZMV8wYAAAAA5L5M1TgeMmRIh9o3cOBAfeMb39CePXt0xhln\naPDgwVqwYIEGDhyoK6+8Ur/4xS+0fPnySHu6kzgOS5Y4BtDzfD6f5s2b1+79bWbasGGDRo8erZKS\nEu3bt08jRozQQw89pLVr1yY9FzBA0jtccCRyfnDOWTr2NxAI6Fvf+pZefvllvfLKK/rLX/4S/ZqS\npLPOOkvPPvusCgsLtWLFinYfgIFAQHv37uWNAQAAco5zTmbmMt0OpFe6+toA2qutrVVVVVXCpOrK\nlSslqc13zEAgoCVLlkiS1q1bJ5/Pp82bN2vWrFntblAXrU+fPho3bpzuvvtu1dTU6Lvf/a6am5v1\n4osv6qWXXtILL7ygvXv36tRTT9XnPvc5VVRUqKSkRLt379aNN96oBx98ULfeeqsktfkuHK894X0r\nKyvT2rVr2404Du9zou/PfK8Gui/R+SUs/N6trq5WVVWV5s6dqxdeeEEXXHCBHnjggXa5rng5MKRW\nKvraJIhT4Pjx4xo1apReeumlhOuUlZVp//796tWrF28QAACQV0gQ5wcSxEDmdTZx3JHvsgMHDtTr\nr7+u/v37t/suG55funSpbr31Vl1yySU6ePCgXn/9db3++uv65S9/qQEDBmjUqFE699xz9bGPfUy/\n/vWv9S//8i+66667Iu2Jfs5Dhw7p4osv1rZt2zRo0KB2+7Bs2bJ2yePYfeR7NtB1J3ovBQIBXX/9\n9WpsbNRvf/tbfeITn9CwYcP0r//6rwnX572ZXqnoa1NioovCZSUkaevWrXrllVeSrv/aa6/poYce\nkkRZiVxGjSSkE8cX0onjCwByD+d2b0pnXDpbquLo0aMaOnSoKioq1KdPnzbb9OnTRwMHDlR1dbV6\n9QqmDqK/yx46dCiS9CkuLtbatWtVW1urq6++WqtXr9aQIUPU2NioKVOm6IorrtCZZ56ppqYmNTQ0\naMiQIdq5c6euuuoq3XLLLSovL9fixYvV2NiotWvXatu2be0uTff5fFq2bJkuvvjiNnVNa2tr27Ql\nUYIqWS1j3iveRFwyI1nOatu2bfr617+uQ4cO6a233tKRI0f0ta99LWFyOPr5qDXsbSSIu6iqqiry\nZrn//vvblJWI59ixY7rxxhvb3LiONwgAAAAAIN2SJY5vu+02Pf/883rooYc0depUjRs3TpMmTdK4\nceP0+uuv66677mrzvTVRojZeneOSkhLdcccd+tWvfqX58+dr1apVGjNmjP7zP/9Tw4YN04wZM3T6\n6afr6aef1jPPPKPBgwdr586d+ta3vqUBAwboyiuvVGNjo6Rgkjde8risrKxdWyRv3QQPyDbxksT/\n/d//rWXLlunJJ59UVVWVxo0bJ7/fn/QKhOjno/SLt1FiohvCHzT79+/vUKJ3zJgxGjp0KMPqAQBA\nXqHERH6gxASQO5LVII0u9XDgwIE2SZ9EdYXjLY8te7FixQrNmzdPq1evVnV1tV577TX9+te/1gsv\nvCDnnAoKCjRlyhRVVVXpYx/7mDZv3qzvfe97WrduXdyyE8lqHFdVVenYsWO6/PLLZWb661//qn79\n+mn27NmaNm2ajh49Si1j5LwT1RqWpEOHDunaa6/V1VdfrWuuuUbDhw/X97//fd17773tSs2Q68oc\nahB3Uio6rbFvoEAgoGHDhum//uu/Trjt1KlT9ZOf/IQ3DgAAyCskiPMDCWIgP6TqBnmJlscmm/x+\nv0pLS7V582a99dZbeumll+T3+/Xqq6/q3Xff1YUXXqgxY8bojDPO0J49e3TnnXfq7LPPTvqa1157\nrbZv364//elPbW7Q16dPH5WVlWno0KG67bbb4u4jiWPkio7UGl60aJHeeecd7dq1S3feeaeuuOIK\n/dM//RM1wD2GGsQZEF1aQgoOk7/55pt10kknJd2ub9++mjNnDqUlchw1kpBOHF9IJ44vAMg9nNu9\nKdvj0tk6x+HE0bp167Ru3boTfheOrXO8bt06+f1+7dq1S1deeaV+9KMf6Wc/+5kuvfRSPffcc/rI\nRz6iv/71r9q/f79+//vfq6SkRJ/85Cc1atQoffSjH9XIkSO1aNEiHTlyRFLwJvPbt2/Xu+++2yY5\nLEnvv/++XnrpJb388ssqKCiQRKmKTMr294rXJas13NjYqAsuuEA7duzQ+++/rwMHDmjfvn2aNWtW\n3CQw99rKfiSIOyneQX/llVfq4x//eNLthgwZoksuuSTyHPziCAAAAADIFalMHMfWOS4uLm53c7xb\nb71V559/vh5++GH9z//8j2677Ta9+OKLevXVV9XQ0KB58+Zp4MCB2rVrl/7jP/5DZ555pkaOHKnP\nfvazamlpSbovBw4c0OOPPy4pOEgsuq5ydKkKEsfIBuFjNZ7o2uEbN27UX//6V61Zs0ZlZWUaMWKE\nvvzlL2vbtm0aMmSIqqurk74OAyKznJnlzRTc3c7btm2btbS0tFnW0tJiixcvtpaWFnvwwQftpJNO\nsn79+lmvXr1MUmTq27evVVRU2OzZs9s9BwAAQD4I9cEy3hdk8mZfG0B+ivc9Oyz8fbupqcm2bdsW\nWd7U1GRlZWXW1NSUcP3Fixeb3++PfF8P27dvn0my8847r8139kRTdXV15Lnnzp1rc+fObfN8iZaH\n9y3clmT7GL1vQLpE568SPT579mwbM2aMlZSU2Nlnn21PP/103G1O9FzIjFT0tRlB3AGxZSWkv/0y\nMnv2bM2ZM0dnnnmmDhw4oPvvv1+DBg3SmDFjNHXqVD300EN6/vnnddttt/ErCgAAAAAASjziOLqW\n6aBBg9qMOF67dq22bdumtWvXtvt+nmjEcXiU77333iu/36/W1tYOtW/nzp264IILNH78eF1wwQWa\nMWOGli5d2qHL58vKyiJtid5HRh0jE5KVf/jv//5vTZs2Tc8884yOHTsmv9+vbdu26Wc/+xmlJPJN\ndzPM2TSpG6Ma4v1K8tvf/tZOPfVUk2T79+9Pui7yw+7duzPdBOQwji+kE8cX0kmMIM6LqTt9baQH\n53ZvIi7JJRpZHL08dvRtohHH8Ub5VldXd2gE8f/5P//HJNk3v/lN+/KXv2wjR4ouer8AACAASURB\nVI60fv36mc/nsy996Uv2+c9/3u6++27bu3evLVy4sE3bokczd2TUcXjfkuUS8nHEMe+Vzkk2Kt/s\nb8ffI488Yu+//77dc889VlhYaGPHjrXHHnvMFi1aZH6/36ZMmdJulH5YOCb5eDx6WSr62owgTiC2\nRkvsrySHDh3SmDFj1Lt3b+3fv1/r169vc+M6flEBAAAAAKBzEo0sjl4er5Zx7IjjRBYsWKA+ffok\nXadPnz4qKCiQ3+/XBx98oB/96Ed6/vnn1draqp/+9Kd64okndN5552n37t266qqr9OCDD+q8887T\nrFmzNHnyZP393/+9+vbt2+G8AHWOkQrxrn6P9f777+v2229XSUmJbrnlFj3wwAN67LHHtGPHDt16\n660qLi7WQw891G6UfizurZWDupthzqZJnRjVkOiXu5aWFrvmmmts8ODB1rt3b9u3b1/C9flFBQAA\nIDWjGpi8P3Wmrw0AqZJo1GT0d/To7+bHjh2zioqKpKOHBw4caEeOHIn7PPFqHLe0tNjDDz9skmzh\nwoU2ceJEKyoqsrPOOsvGjRtn5557rt188802ffp0e+utt9rlDzpb55gRx/mtK/W79+/fb0OGDLGC\nggL74he/aJLM7/cnzX1xZXz2SEVfO+MdyZ6cOttpjfeGOH78uE2aNMkk2Z49e064PgAAQL4jQZwf\nEwliAF6SLHE8e/Zsq6iosD59+rRJDPfp08cGDhxol19++QkTtSdKHB8/ftwaGhrsySeftFtuucUk\n2Wc/+1k79dRTrbS01P7+7//ehg8fbnfeead99atftaamJhLH6JAT5Z7CN3N844037Gc/+5lNmjTJ\n+vTpY1//+tft4MGDkWM10U0WO/o68I5U9LUpMREjurREvFIRCxYsUF1dnW6++WY9+uijSctQIP/s\n2bMn001ADuP4QjpxfAFA7uHc7k3EpWckKlWxd+9e3XbbbXr++ef10EMPaerUqSovL9ekSZM0btw4\nvf7667rrrrtOeJP58Pf/6NIQ0TfHa21tVWlpqT7/+c/r7bfflt/v19ixY9XY2Khf/OIX+va3v62x\nY8fq61//up577jkNGTJEI0eO1Ntvv60pU6bo/vvv1zXXXKPVq1dr3bp1eVmqIt/fK7GlT8Oic0+H\nDh1qE7dAIKAbb7xR55xzjj7zmc/oiiuu0Isvvqg77rhDN910k26//fbIsVpdXZ309cOvE/1eyPeY\n5LTuZpizaVIHRjUkKhWxePFiu/HGG+3kk0+2L33pS0l/leMXufxFEX2kE8cX0onjC+kkRhDnxdSR\nvjZ6Fud2byIu3rN79+5Ol6oIP5ZohG+8G9UlG3X87rvv2iuvvGIPPPCAXX311SbJSkpKrF+/fnb+\n+efbvHnzbMyYMbZhwwa76qqr4uYkTjTieM6cOXb//ffblClTbOzYsVZZWWk/+tGP7MiRI54ccZzv\n75WOjhRuamqy5uZmW7NmjfXv399OOukkO+mkk9qNjj/jjDPs4MGDnXqNWPkeE69KRV874x3Jnpw6\n2mmN9wb5t3/7N5NkF110UdwTP0PuAQAA4iNBnB8TCWIAuagriWOztsm72O06U67i0KFDtmfPHvv+\n979vs2bNMklWVFRkZ555pk2ePNmuuOIKu+CCC2zjxo12xRVX2JEjR+Imji+//HIbOHBgu7IavXr1\nstNPP71N4pBSFT2rKzWFW1pa7Oqrr7bbbrvNzj77bDvttNPsk5/8pH36059OWl979OjRduzYsbiv\nQV4re6Wir+2Cz5MfnHPW0f2NvhPqs88+q0suuURjx45VcXGx1q1b1+ZSleh1413CAgAAkM+cczIz\nl+l2IL0609cGgGxXW1urqqqquDmAcI5g2bJlOnDggKZOndrmsSVLlkhSm9xCvOXRuQZJWrFihZYu\nXaq1a9dq4cKF+v3vf68333xTe/bs0SOPPKK/+7u/0//8z/9o+PDhGjRokP7zP/9T1157rbZv365X\nXnlFv/nNbxLuT0VFhZ5//nn16tUrYRtj948cSMd19XiRpEOHDuniiy/Wtm3b5JzTli1b9IMf/ECB\nQEBjx47VxIkT9fWvf1133XWXrr/+ev3lL39J2I6+ffvqoYce0rRp09q1Ye/eve1eG9khJX3t7maY\ns2lSglENyX4RnDlzpvXu3duqqqrskUceoawEkuJyC6QTxxfSieML6SRGEOfFlKivjczh3O5NxMV7\nUh2TVJer6MyI49/97nf2i1/8wu666y6bM2eOSbLCwsKko0ol2UknnWQ//vGPk7Zl27Zt7cpmxO5v\notxIV3Im2fheSfWI88OHD9sXv/hFu/baa23AgAHm8/nsU5/6lG3YsMECgUCb+H/iE584YZwl2dSp\nU7u8f9kYk3yQir42N6lTsJB7vILvLS0teuqpp/TBBx+opKREkydPTngjOp/Pxy8tAAAAAADkuWQ3\nyAuPuo3OIYRHkK5bt07r1q3r1g3y/vVf/1Xnn3++Lr/8cvXt21d+v1+FhYUnbPOxY8e0aNEiLVu2\nTFdccYWuvPJKfe9739N3vvOdSO6jrKxMF198sZYtW9Zm/8I5lUOHDrW7CV5tbW3C5eHnTXSDvD//\n+c+eu3FeWKIbyEXnl6L3KzZm0X+LQCCgtWvXatu2bVqzZo2eeeYZrVq1SuPHj9dZZ52lP/zhDxow\nYID+7d/+TYFAQNu3b9e8efNkZpGR3MXFxTrzzDM71PbW1tbU/BGQW7qbYc6mSUlGNcSODH777bft\nYx/7mJ133nk2a9ashEXnqdECAACQnBhBnBdTsr42ACCxdI84rqqq6tDI0tLSUpNkY8eOtSFDhlif\nPn1swIABdvbZZ9v06dPt85//vN1+++02Y8YM+93vftemrYlGwCarxRzvRn6J6h8nGqWcqeWpis3+\n/ftt4sSJtnjxYrvwwgvttNNOs4KCAps9e7ZNmjTJ3nzzzTavFx4pHm8kd2VlZYfiXFlZacgtqehr\nZ7wj2ZNTdKc13gk4/IY7cuSInX/++XbOOedYdXV1wuLslJUAAAA4MRLE+TGRIAaA1EpV4njKlCkd\nShwOGjQokoBsaWmx48eP25tvvmn//u//bpJs7ty5dumll1pZWZn17t3bTjvtNBs+fLhNnTrVysrK\nbMWKFTZ58mTbvn27/fGPf0yYBA6LlzxOllyN91yZWn6itsYuP3LkiH31q1+19evX2+LFi624uNg+\n9alP2amnnmpf/vKXbdWqVbZ9+3Z79913ze/3myTz+/3t4p3o72ZmtmnTJuvbt2/SGPft29e2bNmS\n/MBD1iFB3I1Oa7JawmVlZTZw4ED7zGc+0+5ExahhJEM9HqQTxxfSieML6USCOD8mEsTew7ndm4iL\n92RbTDqbON60aZP16dMnaeKwV69ebWoQJ6pxHJ3wnDNnjm3dutUmT55sq1evtmuvvdamTJliAwcO\ntMLCQjv55JOttLTULrzwQvvKV75iQ4cOte9+97v24IMP2hNPPGFf/epXbf/+/e0SsuHk6s9//vM2\n+9fZUcrpXh6bCD5+/Ljt37/fLrnkErv99ttt2bJlVlpaap/61KfslFNOsUGDBtmMGTNs1apVds89\n95gkO3jwYNwYJhspnKgW9LFjx2z06NFJ4zx69Gg7duxY7KHTYdn2XskXJIi72WmNl/Bds2aNnXba\naSbJ9u/f3+6PTpIYy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IeYeBNx6R6fz6ebbrpJK1eu1KxZs0gOQ5LW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\nSgPEIiIiIiIiIiIiIp3U/wdXrdr1mhXG3gAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global_1 = path + outputfile_global_1\n", - "P_global_10 = path + outputfile_global_10\n", - "P_global_100 = path + outputfile_global_100\n", - "P_global_1000 = path + outputfile_global_1000\n", - "\n", - "#Get the data\n", - "e11_1, e22_1, e33_1, e12_1, e13_1, e23_1, s11_1, s22_1, s33_1, s12_1, s13_1, s23_1 = np.loadtxt(P_global_1, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1, T_1, Q_1, r_1 = np.loadtxt(P_global_1, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1, Wm_r_1, Wm_ir_1, Wm_d_1, Wt_1, Wt_r_1, Wt_ir_1 = np.loadtxt(P_global_1, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "e11_10, e22_10, e33_10, e12_10, e13_10, e23_10, s11_10, s22_10, s33_10, s12_10, s13_10, s23_10 = np.loadtxt(P_global_10, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_10, T_10, Q_10, r_10 = np.loadtxt(P_global_10, usecols=(4,5,6,7), unpack=True)\n", - "Wm_10, Wm_r_10, Wm_ir_10, Wm_d_10, Wt_10, Wt_r_10, Wt_ir_10 = np.loadtxt(P_global_10, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "e11_100, e22_100, e33_100, e12_100, e13_100, e23_100, s11_100, s22_100, s33_100, s12_100, s13_100, s23_100 = np.loadtxt(P_global_100, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_100, T_100, Q_100, r_100 = np.loadtxt(P_global_100, usecols=(4,5,6,7), unpack=True)\n", - "Wm_100, Wm_r_100, Wm_ir_100, Wm_d_100, Wt_100, Wt_r_100, Wt_ir_100 = np.loadtxt(P_global_100, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "e11_1000, e22_1000, e33_1000, e12_1000, e13_1000, e23_1000, s11_1000, s22_1000, s33_1000, s12_1000, s13_1000, s23_1000 = np.loadtxt(P_global_1000, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1000, T_1000, Q_1000, r_1000 = np.loadtxt(P_global_1000, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1000, Wm_r_1000, Wm_ir_1000, Wm_d_1000, Wt_1000, Wt_r_1000, Wt_ir_1000 = np.loadtxt(P_global_1000, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "fig = plt.figure()\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(2, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11_1,s11_1, linestyle='None', marker='D', color='black', markersize=10, label='1 increment')\n", - "plt.plot(e11_10,s11_10, linestyle='None', marker='o', color='black', markersize=10, label='10 increment')\n", - "plt.plot(e11_100,s11_100, linestyle='None', marker='x', color='black', markersize=10, label='100 increments')\n", - "plt.plot(e11_1000,s11_1000, c='black', label='1000 increments')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time t (s)', size = 15)\n", - "plt.ylabel(r'temperature $\\theta$\\,(K)',size = 15)\n", - "plt.plot(time_1,T_1, linestyle='None', marker='D', color='black', markersize=10, label='1 increment')\n", - "plt.plot(time_10,T_10, linestyle='None', marker='o', color='black', markersize=10, label='10 increment')\n", - "plt.plot(time_100,T_100, linestyle='None', marker='x', color='black', markersize=10, label='100 increments')\n", - "plt.plot(time_1000,T_1000, c='black', label='1000 increments')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 3)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "#1 increment\n", - "plt.plot(time_1,Wm_1, linestyle='None', marker='D', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_1,Wm_r_1, linestyle='None', marker='D', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_1,Wm_ir_1, linestyle='None', marker='D', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_1,Wm_d_1, linestyle='None', marker='D', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#10 increment\n", - "plt.plot(time_10,Wm_10, linestyle='None', marker='o', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_10,Wm_r_10, linestyle='None', marker='o', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_10,Wm_ir_10, linestyle='None', marker='o', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_10,Wm_d_10, linestyle='None', marker='o', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#100 increments\n", - "plt.plot(time_100,Wm_100, linestyle='None', marker='x', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_100,Wm_r_100, linestyle='None', marker='x', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_100,Wm_ir_100, linestyle='None', marker='x', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_100,Wm_d_100, linestyle='None', marker='x', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#1000 increments\n", - "plt.plot(time_1000,Wm_1000, c='black', label=r'$W_m$')\n", - "plt.plot(time_1000,Wm_r_1000, c='green', label=r'$W_m^r$')\n", - "plt.plot(time_1000,Wm_ir_1000, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time_1000,Wm_d_1000, c='red', label=r'$W_m^d$')\n", - "##\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 4)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_t$',size = 15)\n", - "#1 increment\n", - "plt.plot(time_1,Wt_1, linestyle='None', marker='D', color='black', markersize=10)#, label=r'$W_t$')\n", - "plt.plot(time_1,Wt_r_1, linestyle='None', marker='D', color='green', markersize=10)#, label=r'$W_t^r$')\n", - "plt.plot(time_1,Wt_ir_1, linestyle='None', marker='D', color='blue', markersize=10)#, label=r'$W_t^{ir}$')\n", - "#10 increment\n", - "plt.plot(time_10,Wt_10, linestyle='None', marker='o', color='black', markersize=10)#, label=r'$W_t$')\n", - "plt.plot(time_10,Wt_r_10, linestyle='None', marker='o', color='green', markersize=10)#, label=r'$W_t^r$')\n", - "plt.plot(time_10,Wt_ir_10, linestyle='None', marker='o', color='blue', markersize=10)#, label=r'$W_t^{ir}$')\n", - "#100 increments\n", - "plt.plot(time_100,Wt_100, linestyle='None', marker='x', color='black', markersize=10)#, label=r'$W_t$')\n", - "plt.plot(time_100,Wt_r_100, linestyle='None', marker='x', color='green', markersize=10)#, label=r'$W_t^r$')\n", - "plt.plot(time_100,Wt_ir_100, linestyle='None', marker='x', color='blue', markersize=10)#, label=r'$W_t^{ir}$')\n", - "#1000 increments\n", - "plt.plot(time_1000,Wt_1000, c='black', label=r'$W_t$')\n", - "plt.plot(time_1000,Wt_r_1000, c='green', label=r'$W_t^r$')\n", - "plt.plot(time_1000,Wt_ir_1000, c='blue', label=r'$W_t^{ir}$')\n", - "##\n", - "plt.legend(loc=2)\n", - "\n", - "plt.show()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Notebooks/Umats/Thermomechanical/SMA_T/SMA_T.ipynb b/Notebooks/Umats/Thermomechanical/SMA_T/SMA_T.ipynb deleted file mode 100644 index b3e535aa6..000000000 --- a/Notebooks/Umats/Thermomechanical/SMA_T/SMA_T.ipynb +++ /dev/null @@ -1,403 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SMA model" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import pylab\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import rc\n", - "import simcoon as sim\n", - "import os\n", - "from IPython.display import HTML\n", - "dir = os.path.dirname(os.path.realpath('__file__'))\n", - "\n", - "plt.rc('text', usetex=True)\n", - "plt.rc('font', family='serif')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The SMA (transformation-only) constitutive law implemented in SMART+ is a rate independent description of phase transformation where the two transformations, i.e. austenite $\\rightarrow$ martensite, and martensite $\\rightarrow$ are considered as independant mechanisms.\n", - "\n", - "27 parameters are required for the thermomechanical version: \n", - "1. props[0] : density\n", - "2. props[1] : specific heat capacity\n", - "3. props[2] : flagT: 0 transformation temperatures linearly extrapolated; 1 : smooth temperatures\n", - "4. props[3] : EA: Young's modulus of Austenite\n", - "5. props[4] : EM: Young's modulus of Martensite\n", - "6. props[5] : nuA : Poisson's ratio of Austenite\n", - "7. props[6] : nuM : Poisson's ratio of Martensite\n", - "8. props[7] : alphaA_iso : CTE of Austenite\n", - "9. props[8] : alphaM_iso : CTE of Martensite\n", - "10. props[9] : Hmin : Minimal transformation strain magnitude\n", - "11. props[10] : Hmax : Maximal transformation strain magnitude\n", - "12. props[11] : k1 : Exponential evolution of transformation strain magnitude\n", - "13. props[12] : sigmacrit : Critical stress for change of transformation strain magnitude\n", - "14. props[13]: C_A : Slope of martesnite -> austenite parameter\n", - "15. props[14]: C_M : Slope of austenite -> martensite parameter\n", - "16. props[15]: Ms0 : Martensite start at zero stress\n", - "17. props[16]: Mf0 : Martensite finish at zero stress\n", - "18. props[17]: As0 : Austenite start at zero stress\n", - "19. props[18]: Af0 : Austenite finish at zero stress\n", - "20. props[19]: n1 : Martensite start smooth exponent\n", - "21. props[20]: n2 : Martensite finish smooth exponent\n", - "22. props[21]: n3 : Austenite start smooth exponent\n", - "23. props[22]: n4 : Austenite finish smooth exponent\n", - "\n", - "24. props[23]: c_lambda : penalty function exponent start point\n", - "25. props[24]: p0_lambda : penalty function exponent limit penalty value\n", - "26. props[25]: n_lambda : penalty function power law exponent\n", - "27. props[26]: alpha_lambda : penalty function power law parameter\n", - "\n", - "In SMART+ the SMA material constitutive law is implemented using a *return mapping algorithm*, with use of the *convex cutting plane* algorithm (Simo and Hughes, 1998). The updated stress is provided for 1D, plane stress, and generalized plane strain/3D analysis according to the forms of elastic isotropic materials.\n", - "The updated work, and internal heat production $r$ are determined with the algorithm presented in the *simcoon* documentation.\n", - "\n", - "As a start we should input the name of the UMAT as well as the list of parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "umat_name = 'SMAUT' #This is the 5 character code for the elastic-plastic subroutine\n", - "nstatev = 17 #The number of scalar variables required, only the initial temperature is stored here\n", - "\n", - "rho = 5.5\n", - "c_pA = 0.5\n", - "c_pM = 0.5\n", - "flagT = 0\n", - "E_A = 67538\n", - "E_M = 67538\n", - "nu_A = 0.349\n", - "nu_M = 0.349\n", - "alphaA = 1.E-6\n", - "alphaM = 1.E-6\n", - "Hmin = 0.\n", - "Hmax = 0.0418\n", - "k1 = 0.021\n", - "sigmacrit = 0.0\n", - "C_A = 10\n", - "C_M = 10\n", - "Ms0 = 300\n", - "Mf0 = 290\n", - "As0 = 295\n", - "Af0 = 305\n", - "n1 = 0.2\n", - "n2 = 0.2\n", - "n3 = 0.2\n", - "n4 = 0.2\n", - "sigmacaliber = 300\n", - "b_prager = 0.2\n", - "n_prager = 2.\n", - "c_lambda = 1.E-6\n", - "p0_lambda = 1.E-3\n", - "n_lambda = 1.0\n", - "alpha_lambda = 1.E8\n", - "\n", - "##local orientation\n", - "psi_rve = 0.\n", - "theta_rve = 0.\n", - "phi_rve = 0.\n", - "solver_type = 0\n", - "\n", - "#Define the properties\n", - "props = np.array([rho, c_pA, c_pA, flagT, E_A, E_M, nu_A, nu_M, alphaA, alphaM, Hmin, Hmax, k1, \\\n", - " sigmacrit, C_A, C_M, Ms0, Mf0, As0, Af0, n1, n2, n3, n4, sigmacaliber, b_prager, n_prager, \\\n", - " c_lambda, p0_lambda, n_lambda, alpha_lambda])\n", - "path_data = 'data'\n", - "path_results = 'results'\n", - "\n", - "#Run the simulation\n", - "pathfile = 'path.txt'\n", - "outputfile = 'results_SMA.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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fj3nz5jl+jMuEsjmMTSgTERERqdLb24tJkyYBAL797W/jJz/5ieYtIqJj9cc/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VKz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k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\nHl1mXH3W1SqdU6p3PntHl3a6NOkxy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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#prepare the load\n", - "fig = plt.figure()\n", - "outputfile_global = 'results_SMA_global-0.txt'\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global = path + outputfile_global\n", - "\n", - "#Get the data\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(P_global, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time, T, Q, r = np.loadtxt(P_global, usecols=(4,5,6,7), unpack=True)\n", - "Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt(P_global, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(2, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11,s11, c='black') #, label='direction 1')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time t (s)', size = 15)\n", - "plt.ylabel(r'temperature $\\theta$\\,(K)',size = 15)\n", - "plt.plot(time,T, c='black') #, label='temperature')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 3)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "plt.plot(time,Wm, c='black', label=r'$W_m$')\n", - "plt.plot(time,Wm_r, c='green', label=r'$W_m^r$')\n", - "plt.plot(time,Wm_ir, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time,Wm_d, c='red', label=r'$W_m^d$')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 4)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_t$',size = 15)\n", - "plt.plot(time,Wt, c='black', label=r'$W_t$')\n", - "plt.plot(time,Wt_r, c='green', label=r'$W_t^r$')\n", - "plt.plot(time,Wt_ir, c='blue', label=r'$W_t^{ir}$')\n", - "plt.legend(loc=2)\n", - "\n", - "plt.savefig('example.pdf', format='pdf')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test now the increments " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#Run the simulations - 1 increment\n", - "#pathfile = 'path_1.txt'\n", - "#outputfile = 'results_SMA_1.txt'\n", - "#sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, path_data, path_results, pathfile, outputfile)\n", - "#outputfile_global_1 = 'results_SMA_1_global-0.txt'\n", - "\n", - "#Run the simulations - 10 increments\n", - "pathfile = 'path_10.txt'\n", - "outputfile = 'results_SMA_10.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_10 = 'results_SMA_10_global-0.txt'\n", - "\n", - "#Run the simulations - 100 increments\n", - "pathfile = 'path_100.txt'\n", - "outputfile = 'results_SMA_100.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_100 = 'results_SMA_100_global-0.txt'\n", - "\n", - "#Run the simulations - 1000 increments\n", - "pathfile = 'path_1000.txt'\n", - "outputfile = 'results_SMA_1000.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1000 = 'results_SMA_1000_global-0.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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r7NixCA0NRWlpKT744ANoNBokJiZywJgIXY+TPR1nJyQkID09HS0tLaioqMDGjRsBMN62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\ndusrX/mKKisrO2uoOA18hjgTcXEeYuI8xKRzBZ9EL9zlGTNma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5O0MixyRcAbn1CfVqa2v1pz/9SWvWrFFWVlbEfsjf/va39fOf/1y1tbXq3bt3h8bh74e8Zs0a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aplH3An1gN1sa4t6t8udfX6VehUWLlgDKCUHVfHqKt9IqkeUjSWSOo4paWlaLVaLl26ZDFq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\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6MeQnwO65wQ7dyDbg3wK5H7NrUyNObFcPgIWzV0PEjxDoDHmJkDcbcBDRDvybYddiyI+D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\nRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "#P_global_1 = path + outputfile_global_1\n", - "P_global_10 = path + outputfile_global_10\n", - "P_global_100 = path + outputfile_global_100\n", - "P_global_1000 = path + outputfile_global_1000\n", - "\n", - "#Get the data\n", - "#e11_1, e22_1, e33_1, e12_1, e13_1, e23_1, s11_1, s22_1, s33_1, s12_1, s13_1, s23_1 = np.loadtxt(P_global_1, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "#time_1, T_1, Q_1, r_1 = np.loadtxt(P_global_1, usecols=(4,5,6,7), unpack=True)\n", - "#Wm_1, Wm_r_1, Wm_ir_1, Wm_d_1, Wt_1, Wt_r_1, Wt_ir_1 = np.loadtxt(P_global_1, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "e11_10, e22_10, e33_10, e12_10, e13_10, e23_10, s11_10, s22_10, s33_10, s12_10, s13_10, s23_10 = np.loadtxt(P_global_10, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_10, T_10, Q_10, r_10 = np.loadtxt(P_global_10, usecols=(4,5,6,7), unpack=True)\n", - "Wm_10, Wm_r_10, Wm_ir_10, Wm_d_10, Wt_10, Wt_r_10, Wt_ir_10 = np.loadtxt(P_global_10, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "e11_100, e22_100, e33_100, e12_100, e13_100, e23_100, s11_100, s22_100, s33_100, s12_100, s13_100, s23_100 = np.loadtxt(P_global_100, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_100, T_100, Q_100, r_100 = np.loadtxt(P_global_100, usecols=(4,5,6,7), unpack=True)\n", - "Wm_100, Wm_r_100, Wm_ir_100, Wm_d_100, Wt_100, Wt_r_100, Wt_ir_100 = np.loadtxt(P_global_100, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "e11_1000, e22_1000, e33_1000, e12_1000, e13_1000, e23_1000, s11_1000, s22_1000, s33_1000, s12_1000, s13_1000, s23_1000 = np.loadtxt(P_global_1000, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1000, T_1000, Q_1000, r_1000 = np.loadtxt(P_global_1000, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1000, Wm_r_1000, Wm_ir_1000, Wm_d_1000, Wt_1000, Wt_r_1000, Wt_ir_1000 = np.loadtxt(P_global_1000, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "fig = plt.figure()\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(2, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "#plt.plot(e11_1,s11_1, linestyle='None', marker='D', color='black', markersize=10, label='1 increment')\n", - "plt.plot(e11_10,s11_10, linestyle='None', marker='o', color='black', markersize=10, label='10 increment')\n", - "plt.plot(e11_100,s11_100, linestyle='None', marker='x', color='black', markersize=10, label='100 increments')\n", - "plt.plot(e11_1000,s11_1000, c='black', label='1000 increments')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time t (s)', size = 15)\n", - "plt.ylabel(r'temperature $\\theta$\\,(K)',size = 15)\n", - "#plt.plot(time_1,T_1, linestyle='None', marker='D', color='black', markersize=10, label='1 increment')\n", - "plt.plot(time_10,T_10, linestyle='None', marker='o', color='black', markersize=10, label='20 increments')\n", - "plt.plot(time_100,T_100, linestyle='None', marker='x', color='black', markersize=10, label='100 increments')\n", - "plt.plot(time_1000,T_1000, c='black', label='1000 increments')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 3)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "#1 increment\n", - "#plt.plot(time_1,Wm_1, linestyle='None', marker='D', color='black', markersize=10)#, label=r'$W_m$')\n", - "#plt.plot(time_1,Wm_r_1, linestyle='None', marker='D', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "#plt.plot(time_1,Wm_ir_1, linestyle='None', marker='D', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "#plt.plot(time_1,Wm_d_1, linestyle='None', marker='D', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#10 increment\n", - "plt.plot(time_10,Wm_10, linestyle='None', marker='o', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_10,Wm_r_10, linestyle='None', marker='o', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_10,Wm_ir_10, linestyle='None', marker='o', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_10,Wm_d_10, linestyle='None', marker='o', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#100 increments\n", - "plt.plot(time_100,Wm_100, linestyle='None', marker='x', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_100,Wm_r_100, linestyle='None', marker='x', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_100,Wm_ir_100, linestyle='None', marker='x', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_100,Wm_d_100, linestyle='None', marker='x', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#1000 increments\n", - "plt.plot(time_1000,Wm_1000, c='black', label=r'$W_m$')\n", - "plt.plot(time_1000,Wm_r_1000, c='green', label=r'$W_m^r$')\n", - "plt.plot(time_1000,Wm_ir_1000, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time_1000,Wm_d_1000, c='red', label=r'$W_m^d$')\n", - "##\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 4)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_t$',size = 15)\n", - "#1 increment\n", - "#plt.plot(time_1,Wt_1, linestyle='None', marker='D', color='black', markersize=10)#, label=r'$W_t$')\n", - "#plt.plot(time_1,Wt_r_1, linestyle='None', marker='D', color='green', markersize=10)#, label=r'$W_t^r$')\n", - "#plt.plot(time_1,Wt_ir_1, linestyle='None', marker='D', color='blue', markersize=10)#, label=r'$W_t^{ir}$')\n", - "#10 increment\n", - "plt.plot(time_10,Wt_10, linestyle='None', marker='o', color='black', markersize=10)#, label=r'$W_t$')\n", - "plt.plot(time_10,Wt_r_10, linestyle='None', marker='o', color='green', markersize=10)#, label=r'$W_t^r$')\n", - "plt.plot(time_10,Wt_ir_10, linestyle='None', marker='o', color='blue', markersize=10)#, label=r'$W_t^{ir}$')\n", - "#100 increments\n", - "plt.plot(time_100,Wt_100, linestyle='None', marker='x', color='black', markersize=10)#, label=r'$W_t$')\n", - "plt.plot(time_100,Wt_r_100, linestyle='None', marker='x', color='green', markersize=10)#, label=r'$W_t^r$')\n", - "plt.plot(time_100,Wt_ir_100, linestyle='None', marker='x', color='blue', markersize=10)#, label=r'$W_t^{ir}$')\n", - "#1000 increments\n", - "plt.plot(time_1000,Wt_1000, c='black', label=r'$W_t$')\n", - "plt.plot(time_1000,Wt_r_1000, c='green', label=r'$W_t^r$')\n", - "plt.plot(time_1000,Wt_ir_1000, c='blue', label=r'$W_t^{ir}$')\n", - "##\n", - "plt.legend(loc=2)\n", - "\n", - "plt.savefig('example2.pdf', format='pdf')\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.14" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/Notebooks/Umats/Thermomechanical/Viscoelasticity/Zener/Zener.ipynb b/Notebooks/Umats/Thermomechanical/Viscoelasticity/Zener/Zener.ipynb deleted file mode 100644 index 04424c6a3..000000000 --- a/Notebooks/Umats/Thermomechanical/Viscoelasticity/Zener/Zener.ipynb +++ /dev/null @@ -1,362 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Viscoelasticity : Poynting-Thomson rheological model" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import pylab\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import rc\n", - "import simcoon as sim\n", - "import os\n", - "from IPython.display import HTML\n", - "dir = os.path.dirname(os.path.realpath('__file__'))\n", - "\n", - "plt.rc('text', usetex=True)\n", - "plt.rc('font', family='serif')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Poynting-Thomson constitutive law implemented in SMART+ is a rate dependent, isotropic, Zener-type linear viscoelastic model that consider thermal strains\n", - "\n", - "Nine parameters are required for the thermomechanical version: \n", - "\n", - "1. The density $\\rho$\n", - "2. The specific heat $c_p$\n", - "3. The Thermoelastic Young's modulus $E_0$,\n", - "4. The Thermoelastic Poisson's ratio $\\nu_0$,\n", - "5. The Thermoelastic coefficient of thermal expansion $\\alpha_0$,\n", - "6. The Viscoelastic Young's modulus of Zener branch $E_1$,\n", - "7. The Viscoelastic Poisson's ratio of Zener branch $\\nu_1$,\n", - "8. The Viscoelastic Bulk viscosity of Zener branch $\\eta_B$\n", - "9. The Viscoelastic shear viscosity of Zener branch $\\eta_s$\n", - "\n", - "In 'smartplus' the viscoelastic material constitutive law is implemented using a *fast scalar updating method*. The updated stress is provided for 1D, plane stress, and generalized plane strain/3D analysis according to the forms of elastic isotropic materials.\n", - "The updated work, and internal heat production $r$ are determined with the algorithm presented in the *simcoon* documentation.\n", - "\n", - "As a start we should input the name of the UMAT as well as the list of parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "Mat::init(): requested size is not compatible with column vector layout", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0mpathfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'path.txt'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0moutputfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'results_EPICP.txt'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0msim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mumat_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprops\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnstatev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpsi_rve\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtheta_rve\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphi_rve\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msolver_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath_results\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpathfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m: Mat::init(): requested size is not compatible with column vector layout" - ] - } - ], - "source": [ - "umat_name = 'ZENER' #This is the 5 character code for the elastic-plastic subroutine\n", - "nstatev = 8 #The number of scalar variables required, only the initial temperature is stored here\n", - "\n", - "rho = 4.4\n", - "c_p = 0.656\n", - "E_0 = 3000\n", - "nu_0 = 0.4\n", - "alpha = 0.86E-5\n", - "E_1 = 1200\n", - "nu_1 = 0.3\n", - "eta_B = 12500\n", - "eta_S = 400\n", - "\n", - "##local orientation\n", - "psi_rve = 0.\n", - "theta_rve = 0.\n", - "phi_rve = 0.\n", - "solver_type = 0\n", - "\n", - "#Define the properties\n", - "props = np.array([rho, c_p, E_0, nu_0, alpha, E_1, nu_1, eta_B, eta_S])\n", - "path_data = 'data'\n", - "path_results = 'results'\n", - "\n", - "#Run the simulation\n", - "pathfile = 'path.txt'\n", - "outputfile = 'results_EPICP.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [], - "source": [ - "#prepare the load\n", - "fig = plt.figure()\n", - "outputfile_global = 'results_EPICP_global-0.txt'\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global = path + outputfile_global\n", - "\n", - "#Get the data\n", - "e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt(P_global, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time, T, Q, r = np.loadtxt(P_global, usecols=(4,5,6,7), unpack=True)\n", - "Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt(P_global, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(2, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11,s11, c='black', label='direction 1')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time t (s)', size = 15)\n", - "plt.ylabel(r'temperature $\\theta$\\,(K)',size = 15)\n", - "plt.plot(time,T, c='black', label='temperature')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 3)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "plt.plot(time,Wm, c='black', label=r'$W_m$')\n", - "plt.plot(time,Wm_r, c='green', label=r'$W_m^r$')\n", - "plt.plot(time,Wm_ir, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time,Wm_d, c='red', label=r'$W_m^d$')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 4)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_t$',size = 15)\n", - "plt.plot(time,Wt, c='black', label=r'$W_t$')\n", - "plt.plot(time,Wt_r, c='green', label=r'$W_t^r$')\n", - "plt.plot(time,Wt_ir, c='blue', label=r'$W_t^{ir}$')\n", - "plt.legend(loc=3)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "## Test now the increments " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#Run the simulations - 1 increment\n", - "pathfile = 'path_1.txt'\n", - "outputfile = 'results_EPICP_1.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1 = 'results_EPICP_1_global-0.txt'\n", - "\n", - "#Run the simulations - 10 increments\n", - "pathfile = 'path_10.txt'\n", - "outputfile = 'results_EPICP_10.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_10 = 'results_EPICP_10_global-0.txt'\n", - "\n", - "#Run the simulations - 100 increments\n", - "pathfile = 'path_100.txt'\n", - "outputfile = 'results_EPICP_100.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_100 = 'results_EPICP_100_global-0.txt'\n", - "\n", - "#Run the simulations - 1000 increments\n", - "pathfile = 'path_1000.txt'\n", - "outputfile = 'results_EPICP_1000.txt'\n", - "sim.solver(umat_name, props, nstatev, psi_rve, theta_rve, phi_rve, solver_type, path_data, path_results, pathfile, outputfile)\n", - "outputfile_global_1000 = 'results_EPICP_1000_global-0.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [], - "source": [ - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "path = dir + '/results/'\n", - "P_global_1 = path + outputfile_global_1\n", - "P_global_10 = path + outputfile_global_10\n", - "P_global_100 = path + outputfile_global_100\n", - "P_global_1000 = path + outputfile_global_1000\n", - "\n", - "#Get the data\n", - "e11_1, e22_1, e33_1, e12_1, e13_1, e23_1, s11_1, s22_1, s33_1, s12_1, s13_1, s23_1 = np.loadtxt(P_global_1, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1, T_1, Q_1, r_1 = np.loadtxt(P_global_1, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1, Wm_r_1, Wm_ir_1, Wm_d_1, Wt_1, Wt_r_1, Wt_ir_1 = np.loadtxt(P_global_1, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "e11_10, e22_10, e33_10, e12_10, e13_10, e23_10, s11_10, s22_10, s33_10, s12_10, s13_10, s23_10 = np.loadtxt(P_global_10, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_10, T_10, Q_10, r_10 = np.loadtxt(P_global_10, usecols=(4,5,6,7), unpack=True)\n", - "Wm_10, Wm_r_10, Wm_ir_10, Wm_d_10, Wt_10, Wt_r_10, Wt_ir_10 = np.loadtxt(P_global_10, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "e11_100, e22_100, e33_100, e12_100, e13_100, e23_100, s11_100, s22_100, s33_100, s12_100, s13_100, s23_100 = np.loadtxt(P_global_100, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_100, T_100, Q_100, r_100 = np.loadtxt(P_global_100, usecols=(4,5,6,7), unpack=True)\n", - "Wm_100, Wm_r_100, Wm_ir_100, Wm_d_100, Wt_100, Wt_r_100, Wt_ir_100 = np.loadtxt(P_global_100, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "e11_1000, e22_1000, e33_1000, e12_1000, e13_1000, e23_1000, s11_1000, s22_1000, s33_1000, s12_1000, s13_1000, s23_1000 = np.loadtxt(P_global_1000, usecols=(8,9,10,11,12,13,14,15,16,17,18,19), unpack=True)\n", - "time_1000, T_1000, Q_1000, r_1000 = np.loadtxt(P_global_1000, usecols=(4,5,6,7), unpack=True)\n", - "Wm_1000, Wm_r_1000, Wm_ir_1000, Wm_d_1000, Wt_1000, Wt_r_1000, Wt_ir_1000 = np.loadtxt(P_global_1000, usecols=(20,21,22,23,24,25,26), unpack=True)\n", - "\n", - "fig = plt.figure()\n", - "pylab.rcParams['figure.figsize'] = (24.0, 12.0) #configure the figure output size\n", - "\n", - "#Plot the results\n", - "ax = fig.add_subplot(2, 2, 1)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel(r'Strain $\\varepsilon_{11}$', size = 15)\n", - "plt.ylabel(r'Stress $\\sigma_{11}$\\,(MPa)', size = 15)\n", - "plt.plot(e11_1,s11_1, linestyle='None', marker='D', color='black', markersize=10, label='1 increment')\n", - "plt.plot(e11_10,s11_10, linestyle='None', marker='o', color='black', markersize=10, label='10 increment')\n", - "plt.plot(e11_100,s11_100, linestyle='None', marker='x', color='black', markersize=10, label='100 increments')\n", - "plt.plot(e11_1000,s11_1000, c='black', label='1000 increments')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 2)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time t (s)', size = 15)\n", - "plt.ylabel(r'temperature $\\theta$\\,(K)',size = 15)\n", - "plt.plot(time_1,T_1, linestyle='None', marker='D', color='black', markersize=10, label='1 increment')\n", - "plt.plot(time_10,T_10, linestyle='None', marker='o', color='black', markersize=10, label='10 increment')\n", - "plt.plot(time_100,T_100, linestyle='None', marker='x', color='black', markersize=10, label='100 increments')\n", - "plt.plot(time_1000,T_1000, c='black', label='1000 increments')\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 3)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_m$',size = 15)\n", - "#1 increment\n", - "plt.plot(time_1,Wm_1, linestyle='None', marker='D', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_1,Wm_r_1, linestyle='None', marker='D', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_1,Wm_ir_1, linestyle='None', marker='D', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_1,Wm_d_1, linestyle='None', marker='D', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#10 increment\n", - "plt.plot(time_10,Wm_10, linestyle='None', marker='o', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_10,Wm_r_10, linestyle='None', marker='o', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_10,Wm_ir_10, linestyle='None', marker='o', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_10,Wm_d_10, linestyle='None', marker='o', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#100 increments\n", - "plt.plot(time_100,Wm_100, linestyle='None', marker='x', color='black', markersize=10)#, label=r'$W_m$')\n", - "plt.plot(time_100,Wm_r_100, linestyle='None', marker='x', color='green', markersize=10)#, label=r'$W_m^r$')\n", - "plt.plot(time_100,Wm_ir_100, linestyle='None', marker='x', color='blue', markersize=10)#, label=r'$W_m^{ir}$')\n", - "plt.plot(time_100,Wm_d_100, linestyle='None', marker='x', color='red', markersize=10)#, label=r'$W_m^d$')\n", - "#1000 increments\n", - "plt.plot(time_1000,Wm_1000, c='black', label=r'$W_m$')\n", - "plt.plot(time_1000,Wm_r_1000, c='green', label=r'$W_m^r$')\n", - "plt.plot(time_1000,Wm_ir_1000, c='blue', label=r'$W_m^{ir}$')\n", - "plt.plot(time_1000,Wm_d_1000, c='red', label=r'$W_m^d$')\n", - "##\n", - "plt.legend(loc=2)\n", - "\n", - "ax = fig.add_subplot(2, 2, 4)\n", - "plt.grid(True)\n", - "plt.tick_params(axis='both', which='major', labelsize=15)\n", - "plt.xlabel('time (s)', size = 15)\n", - "plt.ylabel(r'$W_t$',size = 15)\n", - "#1 increment\n", - "plt.plot(time_1,Wt_1, linestyle='None', marker='D', color='black', markersize=10)#, label=r'$W_t$')\n", - "plt.plot(time_1,Wt_r_1, linestyle='None', marker='D', color='green', markersize=10)#, label=r'$W_t^r$')\n", - "plt.plot(time_1,Wt_ir_1, linestyle='None', marker='D', color='blue', markersize=10)#, label=r'$W_t^{ir}$')\n", - "#10 increment\n", - "plt.plot(time_10,Wt_10, linestyle='None', marker='o', color='black', markersize=10)#, label=r'$W_t$')\n", - "plt.plot(time_10,Wt_r_10, linestyle='None', marker='o', color='green', markersize=10)#, label=r'$W_t^r$')\n", - "plt.plot(time_10,Wt_ir_10, linestyle='None', marker='o', color='blue', markersize=10)#, label=r'$W_t^{ir}$')\n", - "#100 increments\n", - "plt.plot(time_100,Wt_100, linestyle='None', marker='x', color='black', markersize=10)#, label=r'$W_t$')\n", - "plt.plot(time_100,Wt_r_100, linestyle='None', marker='x', color='green', markersize=10)#, label=r'$W_t^r$')\n", - "plt.plot(time_100,Wt_ir_100, linestyle='None', marker='x', color='blue', markersize=10)#, label=r'$W_t^{ir}$')\n", - "#1000 increments\n", - "plt.plot(time_1000,Wt_1000, c='black', label=r'$W_t$')\n", - "plt.plot(time_1000,Wt_r_1000, c='green', label=r'$W_t^r$')\n", - "plt.plot(time_1000,Wt_ir_1000, c='blue', label=r'$W_t^{ir}$')\n", - "##\n", - "plt.legend(loc=2)\n", - "\n", - "plt.show()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/analysis/convergence_study.py b/examples/analysis/convergence_study.py new file mode 100644 index 000000000..2ecc56dba --- /dev/null +++ b/examples/analysis/convergence_study.py @@ -0,0 +1,137 @@ +""" +Increment size convergence study +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +This example demonstrates the effect of increment size on the accuracy of the +elastoplastic solver. We run the same loading path with increasing numbers of +increments (1, 10, 100, 1000) and compare the stress-strain curves and energy +terms. The elastic-plastic model with isotropic hardening (EPICP) is used as a +representative case. +""" + +import numpy as np +import matplotlib.pyplot as plt +import simcoon as sim +import os + +plt.rcParams["figure.figsize"] = (18, 10) +dir = os.path.dirname(os.path.realpath("__file__")) + +plt.rc("text", usetex=True) +plt.rc("font", family="serif") + +# ################################################################################### +# Material definition +# -------------------- +# We use an elastic-plastic material with isotropic hardening (EPICP): + +umat_name = "EPICP" +nstatev = 8 + +E = 113800 +nu = 0.342 +alpha = 0.86e-5 +sigma_Y = 600 +H = 1600 +beta = 0.25 + +psi_rve = 0.0 +theta_rve = 0.0 +phi_rve = 0.0 +solver_type = 0 +corate_type = 3 + +props = np.array([E, nu, alpha, sigma_Y, H, beta]) +path_data = "../data" +path_results = "results" + +# ################################################################################### +# Running the solver with different increment sizes +# --------------------------------------------------- +# We run 4 simulations with 1, 10, 100, and 1000 increments for the same +# loading path. With fewer increments, the implicit return-mapping algorithm +# takes larger steps, which can introduce errors in the plastic correction. + +increments = [1, 10, 100, 1000] +data = [] + +for inc in increments: + pathfile = f"EPICP_path_{inc}.txt" + outputfile = f"results_conv_{inc}.txt" + sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile, + ) + result_file = os.path.join(dir, "results", f"results_conv_{inc}_global-0.txt") + e11, s11 = np.loadtxt(result_file, usecols=(8, 14), unpack=True) + time = np.loadtxt(result_file, usecols=(4,), unpack=True) + Wm, Wm_r, Wm_ir, Wm_d = np.loadtxt( + result_file, usecols=(20, 21, 22, 23), unpack=True + ) + data.append( + {"e11": e11, "s11": s11, "time": time, + "Wm": Wm, "Wm_r": Wm_r, "Wm_ir": Wm_ir, "Wm_d": Wm_d} + ) + +# ################################################################################### +# Stress-strain convergence +# --------------------------- +# With only 1 increment, the solver jumps directly to the final state. As the +# number of increments increases, the stress-strain path converges to the +# reference solution (1000 increments). + +fig, (ax1, ax2) = plt.subplots(1, 2) + +markers = ["D", "o", "x", None] +labels = [f"{inc} increment{'s' if inc > 1 else ''}" for inc in increments] + +ax1.grid(True) +ax1.tick_params(axis="both", which="major", labelsize=15) +ax1.set_xlabel(r"Strain $\varepsilon_{11}$", size=15) +ax1.set_ylabel(r"Stress $\sigma_{11}$ (MPa)", size=15) + +for i, d in enumerate(data): + if markers[i] is not None: + ax1.plot(d["e11"], d["s11"], linestyle="None", marker=markers[i], + color="black", markersize=10, label=labels[i]) + else: + ax1.plot(d["e11"], d["s11"], c="black", label=labels[i]) +ax1.legend(loc="upper left") + +# ################################################################################### +# Energy convergence +# -------------------- +# The work terms should also converge. The mechanical work :math:`W_m` is +# decomposed into recoverable :math:`W_m^r`, irrecoverable :math:`W_m^{ir}`, +# and dissipated :math:`W_m^d` contributions. + +ax2.grid(True) +ax2.tick_params(axis="both", which="major", labelsize=15) +ax2.set_xlabel("time (s)", size=15) +ax2.set_ylabel(r"$W_m$", size=15) + +work_colors = ["black", "green", "blue", "red"] +work_keys = ["Wm", "Wm_r", "Wm_ir", "Wm_d"] +work_labels = [r"$W_m$", r"$W_m^r$", r"$W_m^{ir}$", r"$W_m^d$"] + +for i, d in enumerate(data): + for j, (wk, wc, wl) in enumerate(zip(work_keys, work_colors, work_labels)): + label = wl if i == len(data) - 1 else None + if markers[i] is not None: + ax2.plot(d["time"], d[wk], linestyle="None", marker=markers[i], + color=wc, markersize=10, label=label) + else: + ax2.plot(d["time"], d[wk], c=wc, label=label) +ax2.legend(loc="upper left") + +plt.tight_layout() +plt.show() diff --git a/examples/umats/data/ELISO_path.txt b/examples/data/ELISO_path.txt similarity index 100% rename from examples/umats/data/ELISO_path.txt rename to examples/data/ELISO_path.txt diff --git a/examples/umats/data/ELISO_path_1.txt b/examples/data/ELISO_path_1.txt similarity index 100% rename from examples/umats/data/ELISO_path_1.txt rename to examples/data/ELISO_path_1.txt diff --git a/examples/umats/data/ELISO_path_2.txt b/examples/data/ELISO_path_2.txt similarity index 100% rename from examples/umats/data/ELISO_path_2.txt rename to examples/data/ELISO_path_2.txt diff --git a/examples/umats/data/ELISO_path_3.txt b/examples/data/ELISO_path_3.txt similarity index 100% rename from examples/umats/data/ELISO_path_3.txt rename to examples/data/ELISO_path_3.txt diff --git a/examples/umats/data/ELIST_path.txt b/examples/data/ELIST_path.txt similarity index 100% rename from examples/umats/data/ELIST_path.txt rename to examples/data/ELIST_path.txt diff --git a/examples/umats/data/ELORT_path.txt b/examples/data/ELORT_path.txt similarity index 100% rename from examples/umats/data/ELORT_path.txt rename to examples/data/ELORT_path.txt diff --git a/examples/umats/data/EPCHA_path.txt b/examples/data/EPCHA_path.txt similarity index 100% rename from examples/umats/data/EPCHA_path.txt rename to examples/data/EPCHA_path.txt diff --git a/examples/umats/data/EPICP_path.txt b/examples/data/EPICP_path.txt similarity index 100% rename from examples/umats/data/EPICP_path.txt rename to examples/data/EPICP_path.txt diff --git a/examples/umats/data/EPICP_path_1.txt b/examples/data/EPICP_path_1.txt similarity index 100% rename from examples/umats/data/EPICP_path_1.txt rename to examples/data/EPICP_path_1.txt diff --git a/examples/umats/data/EPICP_path_10.txt b/examples/data/EPICP_path_10.txt similarity index 100% rename from examples/umats/data/EPICP_path_10.txt rename to examples/data/EPICP_path_10.txt diff --git a/examples/umats/data/EPICP_path_100.txt b/examples/data/EPICP_path_100.txt similarity index 100% rename from examples/umats/data/EPICP_path_100.txt rename to examples/data/EPICP_path_100.txt diff --git a/examples/umats/data/EPICP_path_1000.txt b/examples/data/EPICP_path_1000.txt similarity index 100% rename from examples/umats/data/EPICP_path_1000.txt rename to examples/data/EPICP_path_1000.txt diff --git a/examples/umats/data/EPKCP_path.txt b/examples/data/EPKCP_path.txt similarity index 100% rename from examples/umats/data/EPKCP_path.txt rename to examples/data/EPKCP_path.txt diff --git a/Notebooks/Umats/Mechanical/Plasticity/Loop_kin/data/path_1.txt b/examples/data/EPKCP_path_1.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Plasticity/Loop_kin/data/path_1.txt rename to examples/data/EPKCP_path_1.txt diff --git a/Notebooks/Umats/Mechanical/Plasticity/Loop_kin/data/path_10.txt b/examples/data/EPKCP_path_10.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Plasticity/Loop_kin/data/path_10.txt rename to examples/data/EPKCP_path_10.txt diff --git a/Notebooks/Umats/Mechanical/Plasticity/Loop_kin/data/path_100.txt b/examples/data/EPKCP_path_100.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Plasticity/Loop_kin/data/path_100.txt rename to examples/data/EPKCP_path_100.txt diff --git a/Notebooks/Umats/Mechanical/Plasticity/Loop_kin/data/path_1000.txt b/examples/data/EPKCP_path_1000.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Plasticity/Loop_kin/data/path_1000.txt rename to examples/data/EPKCP_path_1000.txt diff --git a/Notebooks/Umats/Mechanical/Prony_N/data/path.txt b/examples/data/PRONK_path.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Prony_N/data/path.txt rename to examples/data/PRONK_path.txt diff --git a/Notebooks/Umats/Mechanical/Prony_N/data/path_1.txt b/examples/data/PRONK_path_1.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Prony_N/data/path_1.txt rename to examples/data/PRONK_path_1.txt diff --git a/Notebooks/Umats/Mechanical/Prony_N/data/path_10.txt b/examples/data/PRONK_path_10.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Prony_N/data/path_10.txt rename to examples/data/PRONK_path_10.txt diff --git a/Notebooks/Umats/Mechanical/Prony_N/data/path_100.txt b/examples/data/PRONK_path_100.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Prony_N/data/path_100.txt rename to examples/data/PRONK_path_100.txt diff --git a/Notebooks/Umats/Mechanical/Prony_N/data/path_1000.txt b/examples/data/PRONK_path_1000.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Prony_N/data/path_1000.txt rename to examples/data/PRONK_path_1000.txt diff --git a/Notebooks/Umats/Mechanical/Prony_N/data/path_10000.txt b/examples/data/PRONK_path_10000.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Prony_N/data/path_10000.txt rename to examples/data/PRONK_path_10000.txt diff --git a/Notebooks/Umats/Mechanical/Prony_N/data/Prony_raw.dat b/examples/data/Prony_raw.dat similarity index 100% rename from Notebooks/Umats/Mechanical/Prony_N/data/Prony_raw.dat rename to examples/data/Prony_raw.dat diff --git a/Notebooks/Umats/Mechanical/SMA_TR/data/path.txt b/examples/data/SMAUT_path.txt similarity index 100% rename from Notebooks/Umats/Mechanical/SMA_TR/data/path.txt rename to examples/data/SMAUT_path.txt diff --git a/Notebooks/Umats/Mechanical/SMA_TR/data/path_1.txt b/examples/data/SMAUT_path_1.txt similarity index 100% rename from Notebooks/Umats/Mechanical/SMA_TR/data/path_1.txt rename to examples/data/SMAUT_path_1.txt diff --git a/Notebooks/Umats/Mechanical/SMA_TR/data/path_100.txt b/examples/data/SMAUT_path_100.txt similarity index 100% rename from Notebooks/Umats/Mechanical/SMA_TR/data/path_100.txt rename to examples/data/SMAUT_path_100.txt diff --git a/Notebooks/Umats/Mechanical/SMA_TR/data/path_1000.txt b/examples/data/SMAUT_path_1000.txt similarity index 100% rename from Notebooks/Umats/Mechanical/SMA_TR/data/path_1000.txt rename to examples/data/SMAUT_path_1000.txt diff --git a/Notebooks/Umats/Mechanical/SMA_TR/data/path_20.txt b/examples/data/SMAUT_path_20.txt similarity index 100% rename from Notebooks/Umats/Mechanical/SMA_TR/data/path_20.txt rename to examples/data/SMAUT_path_20.txt diff --git a/Notebooks/Umats/Thermomechanical/Elasticity/data/path.txt b/examples/data/THERM_ELISO_path.txt similarity index 100% rename from Notebooks/Umats/Thermomechanical/Elasticity/data/path.txt rename to examples/data/THERM_ELISO_path.txt diff --git a/Notebooks/Umats/Thermomechanical/Elasticity/data/path_1.txt b/examples/data/THERM_ELISO_path_1.txt similarity index 100% rename from Notebooks/Umats/Thermomechanical/Elasticity/data/path_1.txt rename to examples/data/THERM_ELISO_path_1.txt diff --git a/Notebooks/Umats/Thermomechanical/Elasticity/data/path_2.txt b/examples/data/THERM_ELISO_path_2.txt similarity index 100% rename from Notebooks/Umats/Thermomechanical/Elasticity/data/path_2.txt rename to examples/data/THERM_ELISO_path_2.txt diff --git a/Notebooks/Umats/Thermomechanical/Elasticity/data/path_3.txt b/examples/data/THERM_ELISO_path_3.txt similarity index 100% rename from Notebooks/Umats/Thermomechanical/Elasticity/data/path_3.txt rename to examples/data/THERM_ELISO_path_3.txt diff --git a/Notebooks/Umats/Thermomechanical/Plasticity/Loops_iso/data/path.txt b/examples/data/THERM_EPICP_path.txt similarity index 97% rename from Notebooks/Umats/Thermomechanical/Plasticity/Loops_iso/data/path.txt rename to examples/data/THERM_EPICP_path.txt index 7e62c8c30..7d2eb4e52 100644 --- a/Notebooks/Umats/Thermomechanical/Plasticity/Loops_iso/data/path.txt +++ b/examples/data/THERM_EPICP_path.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Thermomechanical/Plasticity/Loops_iso/data/path_1.txt b/examples/data/THERM_EPICP_path_1.txt similarity index 92% rename from Notebooks/Umats/Thermomechanical/Plasticity/Loops_iso/data/path_1.txt rename to examples/data/THERM_EPICP_path_1.txt index 87c63b104..ca42fa873 100644 --- a/Notebooks/Umats/Thermomechanical/Plasticity/Loops_iso/data/path_1.txt +++ b/examples/data/THERM_EPICP_path_1.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Thermomechanical/Plasticity/Loops_iso/data/path_10.txt b/examples/data/THERM_EPICP_path_10.txt similarity index 92% rename from Notebooks/Umats/Thermomechanical/Plasticity/Loops_iso/data/path_10.txt rename to examples/data/THERM_EPICP_path_10.txt index 16291461a..ed7468e17 100644 --- a/Notebooks/Umats/Thermomechanical/Plasticity/Loops_iso/data/path_10.txt +++ b/examples/data/THERM_EPICP_path_10.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Thermomechanical/Plasticity/Loop_kin/data/path_100.txt b/examples/data/THERM_EPICP_path_100.txt similarity index 92% rename from Notebooks/Umats/Thermomechanical/Plasticity/Loop_kin/data/path_100.txt rename to examples/data/THERM_EPICP_path_100.txt index 5ce111ca4..735744de4 100644 --- a/Notebooks/Umats/Thermomechanical/Plasticity/Loop_kin/data/path_100.txt +++ b/examples/data/THERM_EPICP_path_100.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Thermomechanical/Plasticity/Loops_iso/data/path_1000.txt b/examples/data/THERM_EPICP_path_1000.txt similarity index 92% rename from Notebooks/Umats/Thermomechanical/Plasticity/Loops_iso/data/path_1000.txt rename to examples/data/THERM_EPICP_path_1000.txt index bf4e077a0..a9a0308e3 100644 --- a/Notebooks/Umats/Thermomechanical/Plasticity/Loops_iso/data/path_1000.txt +++ b/examples/data/THERM_EPICP_path_1000.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Thermomechanical/Plasticity/Loop_kin/data/path.txt b/examples/data/THERM_EPKCP_path.txt similarity index 97% rename from Notebooks/Umats/Thermomechanical/Plasticity/Loop_kin/data/path.txt rename to examples/data/THERM_EPKCP_path.txt index 7e62c8c30..7d2eb4e52 100644 --- a/Notebooks/Umats/Thermomechanical/Plasticity/Loop_kin/data/path.txt +++ b/examples/data/THERM_EPKCP_path.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Thermomechanical/SMA_T/data/path_1.txt b/examples/data/THERM_EPKCP_path_1.txt similarity index 92% rename from Notebooks/Umats/Thermomechanical/SMA_T/data/path_1.txt rename to examples/data/THERM_EPKCP_path_1.txt index 87c63b104..ca42fa873 100644 --- a/Notebooks/Umats/Thermomechanical/SMA_T/data/path_1.txt +++ b/examples/data/THERM_EPKCP_path_1.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Thermomechanical/Viscoelasticity/Zener/data/path_10.txt b/examples/data/THERM_EPKCP_path_10.txt similarity index 92% rename from Notebooks/Umats/Thermomechanical/Viscoelasticity/Zener/data/path_10.txt rename to examples/data/THERM_EPKCP_path_10.txt index 16291461a..ed7468e17 100644 --- a/Notebooks/Umats/Thermomechanical/Viscoelasticity/Zener/data/path_10.txt +++ b/examples/data/THERM_EPKCP_path_10.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Thermomechanical/Plasticity/Loops_iso/data/path_100.txt b/examples/data/THERM_EPKCP_path_100.txt similarity index 92% rename from Notebooks/Umats/Thermomechanical/Plasticity/Loops_iso/data/path_100.txt rename to examples/data/THERM_EPKCP_path_100.txt index 5ce111ca4..735744de4 100644 --- a/Notebooks/Umats/Thermomechanical/Plasticity/Loops_iso/data/path_100.txt +++ b/examples/data/THERM_EPKCP_path_100.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Thermomechanical/Viscoelasticity/Zener/data/path_1000.txt b/examples/data/THERM_EPKCP_path_1000.txt similarity index 92% rename from Notebooks/Umats/Thermomechanical/Viscoelasticity/Zener/data/path_1000.txt rename to examples/data/THERM_EPKCP_path_1000.txt index bf4e077a0..a9a0308e3 100644 --- a/Notebooks/Umats/Thermomechanical/Viscoelasticity/Zener/data/path_1000.txt +++ b/examples/data/THERM_EPKCP_path_1000.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Thermomechanical/SMA_T/data/path.txt b/examples/data/THERM_SMAUT_path.txt similarity index 93% rename from Notebooks/Umats/Thermomechanical/SMA_T/data/path.txt rename to examples/data/THERM_SMAUT_path.txt index 9889724c0..99dc8f5a1 100644 --- a/Notebooks/Umats/Thermomechanical/SMA_T/data/path.txt +++ b/examples/data/THERM_SMAUT_path.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps @@ -30,6 +32,8 @@ Q 0 2 #Loading_type 2 +#Control_type +1 #Repeat 4 #Steps @@ -67,6 +71,8 @@ Q 0 3 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Thermomechanical/Viscoelasticity/Zener/data/path_1.txt b/examples/data/THERM_SMAUT_path_1.txt similarity index 92% rename from Notebooks/Umats/Thermomechanical/Viscoelasticity/Zener/data/path_1.txt rename to examples/data/THERM_SMAUT_path_1.txt index 87c63b104..ca42fa873 100644 --- a/Notebooks/Umats/Thermomechanical/Viscoelasticity/Zener/data/path_1.txt +++ b/examples/data/THERM_SMAUT_path_1.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Thermomechanical/SMA_T/data/path_10.txt b/examples/data/THERM_SMAUT_path_10.txt similarity index 95% rename from Notebooks/Umats/Thermomechanical/SMA_T/data/path_10.txt rename to examples/data/THERM_SMAUT_path_10.txt index de9b51524..84726388c 100644 --- a/Notebooks/Umats/Thermomechanical/SMA_T/data/path_10.txt +++ b/examples/data/THERM_SMAUT_path_10.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Thermomechanical/SMA_T/data/path_100.txt b/examples/data/THERM_SMAUT_path_100.txt similarity index 95% rename from Notebooks/Umats/Thermomechanical/SMA_T/data/path_100.txt rename to examples/data/THERM_SMAUT_path_100.txt index 2f2d3e367..437d02035 100644 --- a/Notebooks/Umats/Thermomechanical/SMA_T/data/path_100.txt +++ b/examples/data/THERM_SMAUT_path_100.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Thermomechanical/SMA_T/data/path_1000.txt b/examples/data/THERM_SMAUT_path_1000.txt similarity index 95% rename from Notebooks/Umats/Thermomechanical/SMA_T/data/path_1000.txt rename to examples/data/THERM_SMAUT_path_1000.txt index 22e0a6a38..06856070d 100644 --- a/Notebooks/Umats/Thermomechanical/SMA_T/data/path_1000.txt +++ b/examples/data/THERM_SMAUT_path_1000.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Thermomechanical/Viscoelasticity/Zener/data/path.txt b/examples/data/THERM_ZENER_path.txt similarity index 97% rename from Notebooks/Umats/Thermomechanical/Viscoelasticity/Zener/data/path.txt rename to examples/data/THERM_ZENER_path.txt index 128b9da86..789b0032c 100644 --- a/Notebooks/Umats/Thermomechanical/Viscoelasticity/Zener/data/path.txt +++ b/examples/data/THERM_ZENER_path.txt @@ -7,10 +7,12 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps -1 +6 #Mode 1 diff --git a/Notebooks/Umats/Thermomechanical/Plasticity/Loop_kin/data/path_1.txt b/examples/data/THERM_ZENER_path_1.txt similarity index 92% rename from Notebooks/Umats/Thermomechanical/Plasticity/Loop_kin/data/path_1.txt rename to examples/data/THERM_ZENER_path_1.txt index 87c63b104..ca42fa873 100644 --- a/Notebooks/Umats/Thermomechanical/Plasticity/Loop_kin/data/path_1.txt +++ b/examples/data/THERM_ZENER_path_1.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Thermomechanical/Plasticity/Loop_kin/data/path_10.txt b/examples/data/THERM_ZENER_path_10.txt similarity index 92% rename from Notebooks/Umats/Thermomechanical/Plasticity/Loop_kin/data/path_10.txt rename to examples/data/THERM_ZENER_path_10.txt index 16291461a..ed7468e17 100644 --- a/Notebooks/Umats/Thermomechanical/Plasticity/Loop_kin/data/path_10.txt +++ b/examples/data/THERM_ZENER_path_10.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Thermomechanical/Viscoelasticity/Zener/data/path_100.txt b/examples/data/THERM_ZENER_path_100.txt similarity index 92% rename from Notebooks/Umats/Thermomechanical/Viscoelasticity/Zener/data/path_100.txt rename to examples/data/THERM_ZENER_path_100.txt index 5ce111ca4..735744de4 100644 --- a/Notebooks/Umats/Thermomechanical/Viscoelasticity/Zener/data/path_100.txt +++ b/examples/data/THERM_ZENER_path_100.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Thermomechanical/Plasticity/Loop_kin/data/path_1000.txt b/examples/data/THERM_ZENER_path_1000.txt similarity index 92% rename from Notebooks/Umats/Thermomechanical/Plasticity/Loop_kin/data/path_1000.txt rename to examples/data/THERM_ZENER_path_1000.txt index bf4e077a0..a9a0308e3 100644 --- a/Notebooks/Umats/Thermomechanical/Plasticity/Loop_kin/data/path_1000.txt +++ b/examples/data/THERM_ZENER_path_1000.txt @@ -7,6 +7,8 @@ 1 #Loading_type 2 +#Control_type +1 #Repeat 1 #Steps diff --git a/Notebooks/Umats/Mechanical/Zener/data/path.txt b/examples/data/ZENER_path.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Zener/data/path.txt rename to examples/data/ZENER_path.txt diff --git a/Notebooks/Umats/Mechanical/Zener/data/path_1.txt b/examples/data/ZENER_path_1.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Zener/data/path_1.txt rename to examples/data/ZENER_path_1.txt diff --git a/Notebooks/Umats/Mechanical/Zener/data/path_10.txt b/examples/data/ZENER_path_10.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Zener/data/path_10.txt rename to examples/data/ZENER_path_10.txt diff --git a/Notebooks/Umats/Mechanical/Zener/data/path_100.txt b/examples/data/ZENER_path_100.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Zener/data/path_100.txt rename to examples/data/ZENER_path_100.txt diff --git a/Notebooks/Umats/Mechanical/Zener/data/path_1000.txt b/examples/data/ZENER_path_1000.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Zener/data/path_1000.txt rename to examples/data/ZENER_path_1000.txt diff --git a/Notebooks/Umats/Mechanical/Zener_N/data/path.txt b/examples/data/ZENNK_path.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Zener_N/data/path.txt rename to examples/data/ZENNK_path.txt diff --git a/Notebooks/Umats/Mechanical/Zener_N/data/path_1.txt b/examples/data/ZENNK_path_1.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Zener_N/data/path_1.txt rename to examples/data/ZENNK_path_1.txt diff --git a/Notebooks/Umats/Mechanical/Zener_N/data/path_10.txt b/examples/data/ZENNK_path_10.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Zener_N/data/path_10.txt rename to examples/data/ZENNK_path_10.txt diff --git a/Notebooks/Umats/Mechanical/Zener_N/data/path_100.txt b/examples/data/ZENNK_path_100.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Zener_N/data/path_100.txt rename to examples/data/ZENNK_path_100.txt diff --git a/Notebooks/Umats/Mechanical/Zener_N/data/path_1000.txt b/examples/data/ZENNK_path_1000.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Zener_N/data/path_1000.txt rename to examples/data/ZENNK_path_1000.txt diff --git a/Notebooks/Umats/Mechanical/Zener_N/data/path_10000.txt b/examples/data/ZENNK_path_10000.txt similarity index 100% rename from Notebooks/Umats/Mechanical/Zener_N/data/path_10000.txt rename to examples/data/ZENNK_path_10000.txt diff --git a/Notebooks/Umats/Mechanical/Zener_N/data/Zener_raw.dat b/examples/data/Zener_raw.dat similarity index 100% rename from Notebooks/Umats/Mechanical/Zener_N/data/Zener_raw.dat rename to examples/data/Zener_raw.dat diff --git a/examples/umats/ELISO.py b/examples/mechanical/ELISO.py similarity index 99% rename from examples/umats/ELISO.py rename to examples/mechanical/ELISO.py index 9f7f49336..6dd4dae26 100644 --- a/examples/umats/ELISO.py +++ b/examples/mechanical/ELISO.py @@ -106,7 +106,7 @@ props = np.array([E, nu, alpha]) -path_data = "data" +path_data = "../data" path_results = "results" pathfile = "ELISO_path.txt" outputfile = "results_ELISO.txt" diff --git a/examples/umats/ELIST.py b/examples/mechanical/ELIST.py similarity index 99% rename from examples/umats/ELIST.py rename to examples/mechanical/ELIST.py index 52957e5bf..4142e0d7d 100644 --- a/examples/umats/ELIST.py +++ b/examples/mechanical/ELIST.py @@ -69,7 +69,7 @@ props = np.array([axis, E_L, E_T, nu_TL, nu_TT, G_LT, alpha_L, alpha_T]) -path_data = "data" +path_data = "../data" path_results = "results" pathfile = "ELIST_path.txt" outputfile = "results_ELIST.txt" diff --git a/examples/umats/ELORT.py b/examples/mechanical/ELORT.py similarity index 99% rename from examples/umats/ELORT.py rename to examples/mechanical/ELORT.py index c888a91dd..d8d0bd8ca 100644 --- a/examples/umats/ELORT.py +++ b/examples/mechanical/ELORT.py @@ -76,7 +76,7 @@ [E_1, E_2, E_3, nu_12, nu_13, nu_23, G_12, G_13, G_23, alpha_1, alpha_2, alpha_3] ) -path_data = "data" +path_data = "../data" path_results = "results" pathfile = "ELORT_path.txt" outputfile = "results_ELORT.txt" diff --git a/examples/umats/EPCHA.py b/examples/mechanical/EPCHA.py similarity index 99% rename from examples/umats/EPCHA.py rename to examples/mechanical/EPCHA.py index c5a535d35..3f351947f 100644 --- a/examples/umats/EPCHA.py +++ b/examples/mechanical/EPCHA.py @@ -64,7 +64,7 @@ props = np.array([E, nu, alpha, sigma_Y, Q, b, C_1, D_1, C_2, D_2]) -path_data = "data" +path_data = "../data" path_results = "results" pathfile = "EPCHA_path.txt" outputfile = "results_EPCHA.txt" diff --git a/examples/umats/EPICP.py b/examples/mechanical/EPICP.py similarity index 99% rename from examples/umats/EPICP.py rename to examples/mechanical/EPICP.py index 67fe2c451..3d478e3ce 100644 --- a/examples/umats/EPICP.py +++ b/examples/mechanical/EPICP.py @@ -68,7 +68,7 @@ # Define the properties props = np.array([E, nu, alpha, sigma_Y, H, beta]) -path_data = "data" +path_data = "../data" path_results = "results" # Run the simulation diff --git a/examples/umats/EPKCP.py b/examples/mechanical/EPKCP.py similarity index 99% rename from examples/umats/EPKCP.py rename to examples/mechanical/EPKCP.py index abbe5f172..948f2bf93 100644 --- a/examples/umats/EPKCP.py +++ b/examples/mechanical/EPKCP.py @@ -57,7 +57,7 @@ props = np.array([E, nu, alpha, sigma_Y, k, m, k_X]) -path_data = "data" +path_data = "../data" path_results = "results" pathfile = "EPKCP_path.txt" outputfile = "results_EPKCP.txt" diff --git a/examples/mechanical/PRONY_N.py b/examples/mechanical/PRONY_N.py new file mode 100644 index 000000000..b62ec6bbe --- /dev/null +++ b/examples/mechanical/PRONY_N.py @@ -0,0 +1,131 @@ +""" +Prony Series Viscoelastic Model (Generalized Maxwell) +===================================================== +""" + +import numpy as np +import matplotlib.pyplot as plt +import simcoon as sim +import os + +plt.rcParams["figure.figsize"] = (18, 10) + +################################################################################### +# The Prony series (generalized Maxwell) constitutive law is a rate-dependent, +# isotropic, linear viscoelastic model that considers thermal strains. +# It extends the Zener (standard linear solid) model to :math:`N` Maxwell branches +# connected in parallel with a long-term elastic spring. +# +# The material parameters are: +# +# 1. The long-term (equilibrium) Young's modulus :math:`E_0` +# 2. The long-term Poisson's ratio :math:`\nu_0` +# 3. The coefficient of thermal expansion :math:`\alpha` +# 4. The number of Prony branches :math:`N` +# +# Then, for each branch :math:`i = 1, \ldots, N`: +# +# 5. The branch Young's modulus :math:`E_i` +# 6. The branch Poisson's ratio :math:`\nu_i` +# 7. The branch bulk viscosity :math:`\eta_{B,i}` +# 8. The branch shear viscosity :math:`\eta_{S,i}` +# +# The constitutive law is given by: +# +# .. math:: +# +# \boldsymbol{\sigma}(t) = \mathbf{L}_0 : \boldsymbol{\varepsilon}(t) +# + \sum_{i=1}^{N} \mathbf{L}_i : \boldsymbol{\varepsilon}^{v}_i(t) +# +# where each viscous strain :math:`\boldsymbol{\varepsilon}^{v}_i` evolves according +# to the Maxwell element ODE with characteristic viscosities :math:`\eta_{B,i}` +# and :math:`\eta_{S,i}`. + +umat_name = "PRONK" # 5 character code for the generalized Maxwell (Prony) model + +E_0 = 9400.0 # Long-term Young's modulus (MPa) +nu_0 = 0.4 # Long-term Poisson's ratio +alpha = 0.0 # Coefficient of thermal expansion +n_prony = 5 # Number of Prony (Maxwell) branches + +# Read branch parameters from data file +path_data = "../data" +mat_file = os.path.join(path_data, "Prony_raw.dat") +E_i, nu_i, etaB_i, etaS_i = np.loadtxt(mat_file, usecols=(0, 1, 2, 3), unpack=True) + +# nstatev depends on the number of branches +nstatev = 8 + 7 * n_prony # Number of internal state variables + +psi_rve = 0.0 +theta_rve = 0.0 +phi_rve = 0.0 +solver_type = 0 +corate_type = 1 + +# Build the properties array: [E_0, nu_0, alpha, n_prony, E_1, nu_1, etaB_1, etaS_1, ...] +props = np.array([E_0, nu_0, alpha, n_prony]) +for i in range(n_prony): + props = np.append(props, [E_i[i], nu_i[i], etaB_i[i], etaS_i[i]]) + +path_results = "results" +pathfile = "PRONK_path.txt" +outputfile = "results_PRONK.txt" + +sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile, +) + +################################################################################### +# Plotting the results +# ---------------------- +# +# We plot the stress-strain response which shows the viscoelastic behavior +# (rate-dependent stiffness and hysteresis from viscous dissipation). + +outputfile_macro = os.path.join(path_results, "results_PRONK_global-0.txt") + +fig = plt.figure() + +e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt( + outputfile_macro, + usecols=(8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), + unpack=True, +) +time, T, Q_out, r = np.loadtxt(outputfile_macro, usecols=(4, 5, 6, 7), unpack=True) +Wm, Wm_r, Wm_ir, Wm_d = np.loadtxt( + outputfile_macro, usecols=(20, 21, 22, 23), unpack=True +) + +# First subplot: Stress vs Strain +ax1 = fig.add_subplot(1, 2, 1) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel(r"Strain $\varepsilon_{11}$", size=15) +plt.ylabel(r"Stress $\sigma_{11}$ (MPa)", size=15) +plt.plot(e11, s11, c="blue", label="Prony series model") +plt.legend(loc="best") + +# Second subplot: Work terms vs Time +ax2 = fig.add_subplot(1, 2, 2) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_m$", size=15) +plt.plot(time, Wm, c="black", label=r"$W_m$") +plt.plot(time, Wm_r, c="green", label=r"$W_m^r$") +plt.plot(time, Wm_ir, c="blue", label=r"$W_m^{ir}$") +plt.plot(time, Wm_d, c="red", label=r"$W_m^d$") +plt.legend(loc="best") + +plt.show() diff --git a/examples/umats/README.rst b/examples/mechanical/README.rst similarity index 52% rename from examples/umats/README.rst rename to examples/mechanical/README.rst index 235509a24..137d463af 100644 --- a/examples/umats/README.rst +++ b/examples/mechanical/README.rst @@ -1,9 +1,7 @@ -Constitutive Laws Examples +Mechanical Constitutive Laws ----------------------------------------------- -Below are examples illustrating Simcoon's constitutive laws library. - -This gallery contains examples demonstrating the following material models: +Below are examples illustrating Simcoon's mechanical constitutive laws library. **Elastic Models:** @@ -15,4 +13,14 @@ This gallery contains examples demonstrating the following material models: - **EPICP** - Plasticity with isotropic hardening (power-law) - **EPKCP** - Plasticity with combined isotropic and kinematic hardening -- **EPCHA** - Plasticity with Chaboche hardening (cyclic plasticity) \ No newline at end of file +- **EPCHA** - Plasticity with Chaboche hardening (cyclic plasticity) + +**Viscoelastic Models:** + +- **ZENER** - Poynting-Thomson (Zener) model +- **ZENER_N** - Generalized Zener model (N Kelvin branches) +- **PRONY_N** - Prony series (Generalized Maxwell) + +**Shape Memory Alloys:** + +- **SMA_TR** - Superelastic model (transformation only) \ No newline at end of file diff --git a/examples/mechanical/SMA_TR.py b/examples/mechanical/SMA_TR.py new file mode 100644 index 000000000..aa2f6dcda --- /dev/null +++ b/examples/mechanical/SMA_TR.py @@ -0,0 +1,164 @@ +""" +Shape Memory Alloy - Superelastic Model +========================================= +""" + +import numpy as np +import matplotlib.pyplot as plt +import simcoon as sim +import os + +plt.rcParams["figure.figsize"] = (18, 10) + +################################################################################### +# The SMA (Shape Memory Alloy) transformation constitutive law is a rate-independent +# description of the austenite-martensite phase transformation. Both forward +# (austenite to martensite) and reverse (martensite to austenite) transformations +# are treated as independent mechanisms. +# +# Twenty-eight parameters are required: +# +# 1. :math:`\mathrm{flagT}` : Temperature extrapolation flag (0: linear, 1: smooth) +# 2. :math:`E_A` : Young's modulus of austenite +# 3. :math:`E_M` : Young's modulus of martensite +# 4. :math:`\nu_A` : Poisson's ratio of austenite +# 5. :math:`\nu_M` : Poisson's ratio of martensite +# 6. :math:`\alpha_A` : CTE of austenite +# 7. :math:`\alpha_M` : CTE of martensite +# 8. :math:`H_{\min}` : Minimal transformation strain magnitude +# 9. :math:`H_{\max}` : Maximal transformation strain magnitude +# 10. :math:`k_1` : Exponential evolution of transformation strain +# 11. :math:`\sigma_{\mathrm{crit}}` : Critical stress for transformation strain change +# 12. :math:`C_A` : Clausius-Clapeyron slope (martensite to austenite) +# 13. :math:`C_M` : Clausius-Clapeyron slope (austenite to martensite) +# 14. :math:`M_{s0}` : Martensite start temperature at zero stress +# 15. :math:`M_{f0}` : Martensite finish temperature at zero stress +# 16. :math:`A_{s0}` : Austenite start temperature at zero stress +# 17. :math:`A_{f0}` : Austenite finish temperature at zero stress +# 18. :math:`n_1` : Martensite start smooth exponent +# 19. :math:`n_2` : Martensite finish smooth exponent +# 20. :math:`n_3` : Austenite start smooth exponent +# 21. :math:`n_4` : Austenite finish smooth exponent +# 22. :math:`\sigma_{\mathrm{caliber}}` : Calibration stress +# 23. :math:`b_{\mathrm{Prager}}` : Prager parameter +# 24. :math:`n_{\mathrm{Prager}}` : Prager exponent +# 25. :math:`c_{\lambda}` : Penalty function exponent start point +# 26. :math:`p_{0,\lambda}` : Penalty function limit value +# 27. :math:`n_{\lambda}` : Penalty function power law exponent +# 28. :math:`\alpha_{\lambda}` : Penalty function power law parameter +# +# The constitutive law uses a return mapping algorithm with a convex cutting plane +# method (Simo and Hughes, 1998). The superelastic response exhibits a stress-induced +# phase transformation loop. + +umat_name = "SMAUT" # 5 character code for the SMA transformation model +nstatev = 50 # Number of internal state variables + +# Material parameters +flagT = 0 # Temperature extrapolation flag +E_A = 67538.0 # Young's modulus of austenite (MPa) +E_M = 67538.0 # Young's modulus of martensite (MPa) +nu_A = 0.349 # Poisson's ratio of austenite +nu_M = 0.349 # Poisson's ratio of martensite +alphaA = 1.0e-6 # CTE of austenite +alphaM = 1.0e-6 # CTE of martensite +Hmin = 0.0 # Minimal transformation strain +Hmax = 0.0418 # Maximal transformation strain +k1 = 0.021 # Exponential evolution parameter +sigmacrit = 0.0 # Critical stress +C_A = 10.0 # Clausius-Clapeyron slope (M -> A) +C_M = 10.0 # Clausius-Clapeyron slope (A -> M) +Ms0 = 250.0 # Martensite start temperature (K) +Mf0 = 230.0 # Martensite finish temperature (K) +As0 = 260.0 # Austenite start temperature (K) +Af0 = 280.0 # Austenite finish temperature (K) +n1 = 0.2 # Martensite start smooth exponent +n2 = 0.2 # Martensite finish smooth exponent +n3 = 0.2 # Austenite start smooth exponent +n4 = 0.2 # Austenite finish smooth exponent +sigmacaliber = 300.0 # Calibration stress (MPa) +b_prager = 1.4 # Prager parameter +n_prager = 2.0 # Prager exponent +c_lambda = 1.0e-6 # Penalty function start point +p0_lambda = 1.0e-3 # Penalty function limit value +n_lambda = 1.0 # Penalty function power law exponent +alpha_lambda = 1.0e8 # Penalty function power law parameter + +psi_rve = 0.0 +theta_rve = 0.0 +phi_rve = 0.0 +solver_type = 0 +corate_type = 3 + +props = np.array([ + flagT, E_A, E_M, nu_A, nu_M, alphaA, alphaM, + Hmin, Hmax, k1, sigmacrit, + C_A, C_M, Ms0, Mf0, As0, Af0, + n1, n2, n3, n4, + sigmacaliber, b_prager, n_prager, + c_lambda, p0_lambda, n_lambda, alpha_lambda, +]) + +path_data = "../data" +path_results = "results" +pathfile = "SMAUT_path.txt" +outputfile = "results_SMAUT.txt" + +sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile, +) + +################################################################################### +# Plotting the results +# ---------------------- +# +# We plot the superelastic stress-strain loop which shows the stress-induced +# austenite-martensite phase transformation and its reverse upon unloading. + +outputfile_macro = os.path.join(path_results, "results_SMAUT_global-0.txt") + +fig = plt.figure() + +e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt( + outputfile_macro, + usecols=(8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), + unpack=True, +) +time, T, Q_out, r = np.loadtxt(outputfile_macro, usecols=(4, 5, 6, 7), unpack=True) +Wm, Wm_r, Wm_ir, Wm_d = np.loadtxt( + outputfile_macro, usecols=(20, 21, 22, 23), unpack=True +) + +# First subplot: Stress vs Strain (superelastic loop) +ax1 = fig.add_subplot(1, 2, 1) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel(r"Strain $\varepsilon_{11}$", size=15) +plt.ylabel(r"Stress $\sigma_{11}$ (MPa)", size=15) +plt.plot(e11, s11, c="blue", label="SMA superelastic model") +plt.legend(loc="best") + +# Second subplot: Work terms vs Time +ax2 = fig.add_subplot(1, 2, 2) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_m$", size=15) +plt.plot(time, Wm, c="black", label=r"$W_m$") +plt.plot(time, Wm_r, c="green", label=r"$W_m^r$") +plt.plot(time, Wm_ir, c="blue", label=r"$W_m^{ir}$") +plt.plot(time, Wm_d, c="red", label=r"$W_m^d$") +plt.legend(loc="best") + +plt.show() diff --git a/examples/mechanical/ZENER.py b/examples/mechanical/ZENER.py new file mode 100644 index 000000000..d579128de --- /dev/null +++ b/examples/mechanical/ZENER.py @@ -0,0 +1,114 @@ +""" +Zener viscoelastic model +========================= +""" + +import numpy as np +import matplotlib.pyplot as plt +import simcoon as sim +import os + +plt.rcParams["figure.figsize"] = (18, 10) + +################################################################################### +# The Poynting-Thomson (Zener) constitutive law is a rate-dependent, isotropic, +# linear viscoelastic model that accounts for thermal strains. It consists of +# an elastic spring in parallel with a Maxwell element (spring + dashpot in series). +# +# Seven parameters are required: +# +# 1. The thermoelastic Young's modulus :math:`E_0` +# 2. The thermoelastic Poisson's ratio :math:`\nu_0` +# 3. The coefficient of thermal expansion :math:`\alpha` +# 4. The viscoelastic Young's modulus of the Zener branch :math:`E_1` +# 5. The viscoelastic Poisson's ratio of the Zener branch :math:`\nu_1` +# 6. The bulk viscosity of the Zener branch :math:`\eta_B` +# 7. The shear viscosity of the Zener branch :math:`\eta_S` +# +# The viscoelastic material constitutive law is implemented using a +# *fast scalar updating method*. The updated stress is provided for 1D, +# plane stress, and generalized plane strain/3D analysis. + +umat_name = "ZENER" # 5 character code for the Zener model +nstatev = 8 # Number of internal variables + +# Material parameters +E_0 = 3000.0 # Thermoelastic Young's modulus (MPa) +nu_0 = 0.4 # Thermoelastic Poisson's ratio +alpha = 0.0 # Thermal expansion coefficient +E_1 = 100.0 # Viscoelastic Young's modulus (MPa) +nu_1 = 0.3 # Viscoelastic Poisson's ratio +eta_S = 4000.0 # Shear viscosity +eta_B = eta_S / 4.0 # Bulk viscosity + +psi_rve = 0.0 +theta_rve = 0.0 +phi_rve = 0.0 +solver_type = 0 +corate_type = 1 + +props = np.array([E_0, nu_0, alpha, E_1, nu_1, eta_B, eta_S]) + +path_data = "../data" +path_results = "results" +pathfile = "ZENER_path.txt" +outputfile = "results_ZENER.txt" + +sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile, +) + +################################################################################### +# Plotting the results +# ---------------------- +# +# We plot the stress-strain response which exhibits the characteristic +# rate-dependent behavior of the Zener viscoelastic model. + +outputfile_macro = os.path.join(path_results, "results_ZENER_global-0.txt") + +fig = plt.figure() + +e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt( + outputfile_macro, + usecols=(8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), + unpack=True, +) +time, T, Q_out, r = np.loadtxt(outputfile_macro, usecols=(4, 5, 6, 7), unpack=True) +Wm, Wm_r, Wm_ir, Wm_d = np.loadtxt( + outputfile_macro, usecols=(20, 21, 22, 23), unpack=True +) + +# First subplot: Stress vs Strain +ax1 = fig.add_subplot(1, 2, 1) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel(r"Strain $\varepsilon_{11}$", size=15) +plt.ylabel(r"Stress $\sigma_{11}$ (MPa)", size=15) +plt.plot(e11, s11, c="blue", label="Zener model") +plt.legend(loc="best") + +# Second subplot: Work terms vs Time +ax2 = fig.add_subplot(1, 2, 2) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_m$", size=15) +plt.plot(time, Wm, c="black", label=r"$W_m$") +plt.plot(time, Wm_r, c="green", label=r"$W_m^r$") +plt.plot(time, Wm_ir, c="blue", label=r"$W_m^{ir}$") +plt.plot(time, Wm_d, c="red", label=r"$W_m^d$") +plt.legend(loc="best") + +plt.show() diff --git a/examples/mechanical/ZENER_N.py b/examples/mechanical/ZENER_N.py new file mode 100644 index 000000000..3f858a0cd --- /dev/null +++ b/examples/mechanical/ZENER_N.py @@ -0,0 +1,124 @@ +""" +Generalized Zener model (N Kelvin branches) +============================================= +""" + +import numpy as np +import matplotlib.pyplot as plt +import simcoon as sim +import os + +plt.rcParams["figure.figsize"] = (18, 10) + +################################################################################### +# The generalized Zener model extends the standard Poynting-Thomson (Zener) model +# by using :math:`N` Kelvin-Voigt branches in parallel with an elastic spring. +# This allows for a more accurate representation of the relaxation spectrum +# of real viscoelastic materials. +# +# The base parameters are: +# +# 1. The thermoelastic Young's modulus :math:`E_0` +# 2. The thermoelastic Poisson's ratio :math:`\nu_0` +# 3. The coefficient of thermal expansion :math:`\alpha` +# 4. The number of Kelvin branches :math:`N` +# +# For each branch :math:`i` (:math:`i = 1, \ldots, N`), four additional parameters +# are required: +# +# - The viscoelastic Young's modulus :math:`E_i` +# - The viscoelastic Poisson's ratio :math:`\nu_i` +# - The bulk viscosity :math:`\eta_{B,i}` +# - The shear viscosity :math:`\eta_{S,i}` + +umat_name = "ZENNK" # 5 character code for the generalized Zener model + +# Base material parameters +E_0 = 3000.0 # Thermoelastic Young's modulus (MPa) +nu_0 = 0.4 # Thermoelastic Poisson's ratio +alpha = 0.0 # Thermal expansion coefficient +n_kelvin = 3 # Number of Kelvin branches + +# Read branch parameters from data file +mat_file = os.path.join("..", "data", "Zener_raw.dat") +E_i, nu_i, etaB_i, etaS_i = np.loadtxt( + mat_file, usecols=(0, 1, 2, 3), unpack=True +) + +# nstatev depends on the number of branches +nstatev = 8 + 7 * n_kelvin + +# Build the properties array: base params + branch params +props = np.array([E_0, nu_0, alpha, n_kelvin]) +for i in range(n_kelvin): + props = np.append(props, [E_i[i], nu_i[i], etaB_i[i], etaS_i[i]]) + +psi_rve = 0.0 +theta_rve = 0.0 +phi_rve = 0.0 +solver_type = 0 +corate_type = 1 + +path_data = "../data" +path_results = "results" +pathfile = "ZENNK_path.txt" +outputfile = "results_ZENNK.txt" + +sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile, +) + +################################################################################### +# Plotting the results +# ---------------------- +# +# We plot the stress-strain response and the work decomposition for the +# generalized Zener model with multiple Kelvin branches. + +outputfile_macro = os.path.join(path_results, "results_ZENNK_global-0.txt") + +fig = plt.figure() + +e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt( + outputfile_macro, + usecols=(8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), + unpack=True, +) +time, T, Q_out, r = np.loadtxt(outputfile_macro, usecols=(4, 5, 6, 7), unpack=True) +Wm, Wm_r, Wm_ir, Wm_d = np.loadtxt( + outputfile_macro, usecols=(20, 21, 22, 23), unpack=True +) + +# First subplot: Stress vs Strain +ax1 = fig.add_subplot(1, 2, 1) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel(r"Strain $\varepsilon_{11}$", size=15) +plt.ylabel(r"Stress $\sigma_{11}$ (MPa)", size=15) +plt.plot(e11, s11, c="blue", label="Generalized Zener model") +plt.legend(loc="best") + +# Second subplot: Work terms vs Time +ax2 = fig.add_subplot(1, 2, 2) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_m$", size=15) +plt.plot(time, Wm, c="black", label=r"$W_m$") +plt.plot(time, Wm_r, c="green", label=r"$W_m^r$") +plt.plot(time, Wm_ir, c="blue", label=r"$W_m^{ir}$") +plt.plot(time, Wm_d, c="red", label=r"$W_m^d$") +plt.legend(loc="best") + +plt.show() diff --git a/examples/thermomechanical/ELISO.py b/examples/thermomechanical/ELISO.py new file mode 100644 index 000000000..4bbc92e25 --- /dev/null +++ b/examples/thermomechanical/ELISO.py @@ -0,0 +1,154 @@ +""" +Isotropic elasticity (thermomechanical) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" + +import numpy as np +import simcoon as sim +import matplotlib.pyplot as plt +import os + +plt.rcParams["figure.figsize"] = (18, 10) + +################################################################################### +# In thermoelastic isotropic materials three mechanical parameters and two thermal +# parameters are required: +# +# 1. The density :math:`\rho` +# 2. The specific heat :math:`c_p` +# 3. The Young modulus :math:`E` +# 4. The Poisson ratio :math:`\nu` +# 5. The coefficient of thermal expansion :math:`\alpha` +# +# The elastic stiffness tensor is written in the Voigt notation formalism as +# +# .. math:: +# +# \mathbf{L} = \begin{pmatrix} +# L_{1111} & L_{1122} & L_{1122} & 0 & 0 & 0 \\ +# L_{1122} & L_{1111} & L_{1122} & 0 & 0 & 0 \\ +# L_{1122} & L_{1122} & L_{1111} & 0 & 0 & 0 \\ +# 0 & 0 & 0 & L_{1212} & 0 & 0 \\ +# 0 & 0 & 0 & 0 & L_{1212} & 0 \\ +# 0 & 0 & 0 & 0 & 0 & L_{1212} +# \end{pmatrix} +# +# with +# +# .. math:: +# +# L_{1111} = \frac{E(1-\nu)}{(1+\nu)(1-2\nu)}, \quad +# L_{1122} = \frac{E\nu}{(1+\nu)(1-2\nu)}, \quad +# L_{1212} = \frac{E}{2(1+\nu)}. +# +# The increment of the elastic strain is given by +# +# .. math:: +# +# \Delta\varepsilon^{\mathrm{el}}_{ij} = \Delta\varepsilon^{\mathrm{tot}}_{ij} - \alpha \Delta T \delta_{ij} +# +# In the thermomechanical framework, the thermal work terms :math:`W_t`, :math:`W_t^r` +# and :math:`W_t^{ir}` are also computed alongside the mechanical work terms. + +umat_name = "ELISO" # 5 character code for the elastic-isotropic subroutine +nstatev = 1 # Number of internal variables + +# Material parameters +rho = 4.4 # Density +c_p = 0.656 # Specific heat capacity +E = 70000.0 # Young's modulus (MPa) +nu = 0.2 # Poisson ratio +alpha = 1.0e-5 # Thermal expansion coefficient + +psi_rve = 0.0 +theta_rve = 0.0 +phi_rve = 0.0 +solver_type = 0 +corate_type = 2 + +props = np.array([rho, c_p, E, nu, alpha]) + +path_data = "../data" +path_results = "results" +pathfile = "THERM_ELISO_path.txt" +outputfile = "results_THERM_ELISO.txt" + +sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile, +) + +################################################################################### +# Plotting the results +# ---------------------- +# +# We plot the stress-strain curve, the temperature evolution, the mechanical work +# terms and the thermal work terms. + +outputfile_macro = os.path.join(path_results, "results_THERM_ELISO_global-0.txt") + +fig = plt.figure() + +# Get the data +e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt( + outputfile_macro, + usecols=(8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), + unpack=True, +) +time, T, Q, r = np.loadtxt(outputfile_macro, usecols=(4, 5, 6, 7), unpack=True) +Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt( + outputfile_macro, usecols=(20, 21, 22, 23, 24, 25, 26), unpack=True +) + +# Stress vs Strain +ax = fig.add_subplot(2, 2, 1) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel(r"Strain $\varepsilon_{11}$", size=15) +plt.ylabel(r"Stress $\sigma_{11}$ (MPa)", size=15) +plt.plot(e11, s11, c="black", label="direction 1") +plt.legend(loc="best") + +# Temperature vs Time +ax = fig.add_subplot(2, 2, 2) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"Temperature $\theta$ (K)", size=15) +plt.plot(time, T, c="black", label="temperature") +plt.legend(loc="best") + +# Mechanical work vs Time +ax = fig.add_subplot(2, 2, 3) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_m$", size=15) +plt.plot(time, Wm, c="black", label=r"$W_m$") +plt.plot(time, Wm_r, c="green", label=r"$W_m^r$") +plt.plot(time, Wm_ir, c="blue", label=r"$W_m^{ir}$") +plt.plot(time, Wm_d, c="red", label=r"$W_m^d$") +plt.legend(loc="best") + +# Thermal work vs Time +ax = fig.add_subplot(2, 2, 4) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_t$", size=15) +plt.plot(time, Wt, c="black", label=r"$W_t$") +plt.plot(time, Wt_r, c="green", label=r"$W_t^r$") +plt.plot(time, Wt_ir, c="blue", label=r"$W_t^{ir}$") +plt.legend(loc="best") + +plt.show() diff --git a/examples/thermomechanical/ELIST.py b/examples/thermomechanical/ELIST.py new file mode 100644 index 000000000..25476fded --- /dev/null +++ b/examples/thermomechanical/ELIST.py @@ -0,0 +1,254 @@ +""" +Transversely isotropic elasticity (thermomechanical) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" + +import numpy as np +import simcoon as sim +import matplotlib.pyplot as plt +import os + +plt.rcParams["figure.figsize"] = (18, 10) + +################################################################################### +# In thermoelastic transversely isotropic materials, the following parameters +# are required: +# +# 1. The density :math:`\rho` +# 2. The specific heat :math:`c_p` +# 3. The axis of transverse isotropy (1, 2, or 3) +# 4. The longitudinal Young modulus :math:`E_L` +# 5. The transverse Young modulus :math:`E_T` +# 6. The Poisson ratio :math:`\nu_{TL}` +# 7. The Poisson ratio :math:`\nu_{TT}` +# 8. The shear modulus :math:`G_{LT}` +# 9. The longitudinal thermal expansion coefficient :math:`\alpha_L` +# 10. The transverse thermal expansion coefficient :math:`\alpha_T` +# +# The elastic stiffness tensor for a transversely isotropic material with axis 1 +# as the symmetry axis is written in the Voigt notation formalism as: +# +# .. math:: +# +# \mathbf{L} = \begin{pmatrix} +# L_{1111} & L_{1122} & L_{1122} & 0 & 0 & 0 \\ +# L_{1122} & L_{2222} & L_{2233} & 0 & 0 & 0 \\ +# L_{1122} & L_{2233} & L_{2222} & 0 & 0 & 0 \\ +# 0 & 0 & 0 & L_{1212} & 0 & 0 \\ +# 0 & 0 & 0 & 0 & L_{1212} & 0 \\ +# 0 & 0 & 0 & 0 & 0 & L_{2323} +# \end{pmatrix} +# +# The thermal expansion tensor is: +# +# .. math:: +# +# \boldsymbol{\alpha} = \begin{pmatrix} +# \alpha_L & 0 & 0 \\ +# 0 & \alpha_T & 0 \\ +# 0 & 0 & \alpha_T +# \end{pmatrix} + +umat_name = "ELIST" # 5 character code for transversely isotropic elastic subroutine +nstatev = 1 # Number of internal variables + +# Material parameters +rho = 4.4 # Density +c_p = 0.656 # Specific heat capacity +axis = 1 # Symmetry axis +E_L = 4500.0 # Longitudinal Young's modulus (MPa) +E_T = 2300.0 # Transverse Young's modulus (MPa) +nu_TL = 0.05 # Poisson ratio (transverse-longitudinal) +nu_TT = 0.3 # Poisson ratio (transverse-transverse) +G_LT = 2700.0 # Shear modulus (longitudinal-transverse) (MPa) +alpha_L = 1.0e-5 # Thermal expansion (longitudinal) +alpha_T = 2.5e-5 # Thermal expansion (transverse) + +psi_rve = 0.0 +theta_rve = 0.0 +phi_rve = 0.0 +solver_type = 0 +corate_type = 2 + +props = np.array([rho, c_p, axis, E_L, E_T, nu_TL, nu_TT, G_LT, alpha_L, alpha_T]) + +path_data = "../data" +path_results = "results" + +################################################################################### +# Loading in direction 1 +# ~~~~~~~~~~~~~~~~~~~~~~~~ +# +# First we apply a uniaxial stress loading along direction 1 (the symmetry axis). + +pathfile = "THERM_ELISO_path_1.txt" +outputfile_1 = "results_THERM_ELIST_1.txt" + +sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile_1, +) + +outputfile_macro_1 = os.path.join(path_results, "results_THERM_ELIST_1_global-0.txt") + +################################################################################### +# Loading in direction 2 +# ~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Then we apply a uniaxial stress loading along direction 2 (the transverse direction). + +pathfile = "THERM_ELISO_path_2.txt" +outputfile_2 = "results_THERM_ELIST_2.txt" + +sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile_2, +) + +outputfile_macro_2 = os.path.join(path_results, "results_THERM_ELIST_2_global-0.txt") + +################################################################################### +# Plotting the results -- Loading direction 1 +# ----------------------------------------------- +# +# We plot the stress-strain curve, the temperature evolution, and the work terms +# for loading along direction 1. + +fig = plt.figure() + +# Get the data +e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt( + outputfile_macro_1, + usecols=(8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), + unpack=True, +) +time, T, Q, r = np.loadtxt(outputfile_macro_1, usecols=(4, 5, 6, 7), unpack=True) +Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt( + outputfile_macro_1, usecols=(20, 21, 22, 23, 24, 25, 26), unpack=True +) + +# Stress vs Strain +ax = fig.add_subplot(2, 2, 1) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel(r"Strain $\varepsilon_{11}$", size=15) +plt.ylabel(r"Stress $\sigma_{11}$ (MPa)", size=15) +plt.plot(e11, s11, c="black", label="direction 1") +plt.legend(loc="best") + +# Temperature vs Time +ax = fig.add_subplot(2, 2, 2) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"Temperature $\theta$ (K)", size=15) +plt.plot(time, T, c="black", label="temperature") +plt.legend(loc="best") + +# Mechanical work vs Time +ax = fig.add_subplot(2, 2, 3) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_m$", size=15) +plt.plot(time, Wm, c="black", label=r"$W_m$") +plt.plot(time, Wm_r, c="green", label=r"$W_m^r$") +plt.plot(time, Wm_ir, c="blue", label=r"$W_m^{ir}$") +plt.plot(time, Wm_d, c="red", label=r"$W_m^d$") +plt.legend(loc="best") + +# Thermal work vs Time +ax = fig.add_subplot(2, 2, 4) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_t$", size=15) +plt.plot(time, Wt, c="black", label=r"$W_t$") +plt.plot(time, Wt_r, c="green", label=r"$W_t^r$") +plt.plot(time, Wt_ir, c="blue", label=r"$W_t^{ir}$") +plt.legend(loc="best") + +plt.show() + +################################################################################### +# Plotting the results -- Loading direction 2 +# ----------------------------------------------- +# +# We plot the stress-strain curve, the temperature evolution, and the work terms +# for loading along direction 2. + +fig = plt.figure() + +# Get the data +e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt( + outputfile_macro_2, + usecols=(8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), + unpack=True, +) +time, T, Q, r = np.loadtxt(outputfile_macro_2, usecols=(4, 5, 6, 7), unpack=True) +Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt( + outputfile_macro_2, usecols=(20, 21, 22, 23, 24, 25, 26), unpack=True +) + +# Stress vs Strain +ax = fig.add_subplot(2, 2, 1) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel(r"Strain $\varepsilon_{22}$", size=15) +plt.ylabel(r"Stress $\sigma_{22}$ (MPa)", size=15) +plt.plot(e22, s22, c="black", label="direction 2") +plt.legend(loc="best") + +# Temperature vs Time +ax = fig.add_subplot(2, 2, 2) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"Temperature $\theta$ (K)", size=15) +plt.plot(time, T, c="black", label="temperature") +plt.legend(loc="best") + +# Mechanical work vs Time +ax = fig.add_subplot(2, 2, 3) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_m$", size=15) +plt.plot(time, Wm, c="black", label=r"$W_m$") +plt.plot(time, Wm_r, c="green", label=r"$W_m^r$") +plt.plot(time, Wm_ir, c="blue", label=r"$W_m^{ir}$") +plt.plot(time, Wm_d, c="red", label=r"$W_m^d$") +plt.legend(loc="best") + +# Thermal work vs Time +ax = fig.add_subplot(2, 2, 4) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_t$", size=15) +plt.plot(time, Wt, c="black", label=r"$W_t$") +plt.plot(time, Wt_r, c="green", label=r"$W_t^r$") +plt.plot(time, Wt_ir, c="blue", label=r"$W_t^{ir}$") +plt.legend(loc="best") + +plt.show() diff --git a/examples/thermomechanical/ELORT.py b/examples/thermomechanical/ELORT.py new file mode 100644 index 000000000..5a163f3d8 --- /dev/null +++ b/examples/thermomechanical/ELORT.py @@ -0,0 +1,323 @@ +""" +Orthotropic elasticity (thermomechanical) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" + +import numpy as np +import simcoon as sim +import matplotlib.pyplot as plt +import os + +plt.rcParams["figure.figsize"] = (18, 10) + +################################################################################### +# In thermoelastic orthotropic materials, there are three mutually perpendicular +# planes of symmetry. The following parameters are required: +# +# 1. The density :math:`\rho` +# 2. The specific heat :math:`c_p` +# 3. The Young modulus in direction 1: :math:`E_1` +# 4. The Young modulus in direction 2: :math:`E_2` +# 5. The Young modulus in direction 3: :math:`E_3` +# 6. The Poisson ratio :math:`\nu_{12}` +# 7. The Poisson ratio :math:`\nu_{13}` +# 8. The Poisson ratio :math:`\nu_{23}` +# 9. The shear modulus :math:`G_{12}` +# 10. The shear modulus :math:`G_{13}` +# 11. The shear modulus :math:`G_{23}` +# 12. The coefficient of thermal expansion :math:`\alpha_1` +# 13. The coefficient of thermal expansion :math:`\alpha_2` +# 14. The coefficient of thermal expansion :math:`\alpha_3` +# +# The elastic stiffness tensor for an orthotropic material is written in the +# Voigt notation as: +# +# .. math:: +# +# \mathbf{L} = \begin{pmatrix} +# L_{11} & L_{12} & L_{13} & 0 & 0 & 0 \\ +# L_{12} & L_{22} & L_{23} & 0 & 0 & 0 \\ +# L_{13} & L_{23} & L_{33} & 0 & 0 & 0 \\ +# 0 & 0 & 0 & G_{12} & 0 & 0 \\ +# 0 & 0 & 0 & 0 & G_{13} & 0 \\ +# 0 & 0 & 0 & 0 & 0 & G_{23} +# \end{pmatrix} +# +# The thermal expansion tensor is: +# +# .. math:: +# +# \boldsymbol{\alpha} = \begin{pmatrix} +# \alpha_1 & 0 & 0 \\ +# 0 & \alpha_2 & 0 \\ +# 0 & 0 & \alpha_3 +# \end{pmatrix} + +umat_name = "ELORT" # 5 character code for orthotropic elastic subroutine +nstatev = 1 # Number of internal variables + +# Material parameters +rho = 4.4 # Density +c_p = 0.656 # Specific heat capacity +E_1 = 4500.0 # Young's modulus in direction 1 (MPa) +E_2 = 2300.0 # Young's modulus in direction 2 (MPa) +E_3 = 2700.0 # Young's modulus in direction 3 (MPa) +nu_12 = 0.06 # Poisson ratio 12 +nu_13 = 0.08 # Poisson ratio 13 +nu_23 = 0.3 # Poisson ratio 23 +G_12 = 2200.0 # Shear modulus 12 (MPa) +G_13 = 2100.0 # Shear modulus 13 (MPa) +G_23 = 2400.0 # Shear modulus 23 (MPa) +alpha_1 = 1.0e-5 # Thermal expansion in direction 1 +alpha_2 = 2.5e-5 # Thermal expansion in direction 2 +alpha_3 = 2.2e-5 # Thermal expansion in direction 3 + +psi_rve = 0.0 +theta_rve = 0.0 +phi_rve = 0.0 +solver_type = 0 +corate_type = 2 + +props = np.array( + [rho, c_p, E_1, E_2, E_3, nu_12, nu_13, nu_23, G_12, G_13, G_23, alpha_1, alpha_2, alpha_3] +) + +path_data = "../data" +path_results = "results" + +################################################################################### +# Loading in direction 1 +# ~~~~~~~~~~~~~~~~~~~~~~~~ + +pathfile = "THERM_ELISO_path_1.txt" +outputfile_1 = "results_THERM_ELORT_1.txt" + +sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile_1, +) + +outputfile_macro_1 = os.path.join(path_results, "results_THERM_ELORT_1_global-0.txt") + +################################################################################### +# Loading in direction 2 +# ~~~~~~~~~~~~~~~~~~~~~~~~ + +pathfile = "THERM_ELISO_path_2.txt" +outputfile_2 = "results_THERM_ELORT_2.txt" + +sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile_2, +) + +outputfile_macro_2 = os.path.join(path_results, "results_THERM_ELORT_2_global-0.txt") + +################################################################################### +# Loading in direction 3 +# ~~~~~~~~~~~~~~~~~~~~~~~~ + +pathfile = "THERM_ELISO_path_3.txt" +outputfile_3 = "results_THERM_ELORT_3.txt" + +sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile_3, +) + +outputfile_macro_3 = os.path.join(path_results, "results_THERM_ELORT_3_global-0.txt") + +################################################################################### +# Plotting the results -- Loading direction 1 +# ----------------------------------------------- + +fig = plt.figure() + +e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt( + outputfile_macro_1, + usecols=(8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), + unpack=True, +) +time, T, Q, r = np.loadtxt(outputfile_macro_1, usecols=(4, 5, 6, 7), unpack=True) +Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt( + outputfile_macro_1, usecols=(20, 21, 22, 23, 24, 25, 26), unpack=True +) + +ax = fig.add_subplot(2, 2, 1) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel(r"Strain $\varepsilon_{11}$", size=15) +plt.ylabel(r"Stress $\sigma_{11}$ (MPa)", size=15) +plt.plot(e11, s11, c="black", label="direction 1") +plt.legend(loc="best") + +ax = fig.add_subplot(2, 2, 2) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"Temperature $\theta$ (K)", size=15) +plt.plot(time, T, c="black", label="temperature") +plt.legend(loc="best") + +ax = fig.add_subplot(2, 2, 3) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_m$", size=15) +plt.plot(time, Wm, c="black", label=r"$W_m$") +plt.plot(time, Wm_r, c="green", label=r"$W_m^r$") +plt.plot(time, Wm_ir, c="blue", label=r"$W_m^{ir}$") +plt.plot(time, Wm_d, c="red", label=r"$W_m^d$") +plt.legend(loc="best") + +ax = fig.add_subplot(2, 2, 4) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_t$", size=15) +plt.plot(time, Wt, c="black", label=r"$W_t$") +plt.plot(time, Wt_r, c="green", label=r"$W_t^r$") +plt.plot(time, Wt_ir, c="blue", label=r"$W_t^{ir}$") +plt.legend(loc="best") + +plt.show() + +################################################################################### +# Plotting the results -- Loading direction 2 +# ----------------------------------------------- + +fig = plt.figure() + +e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt( + outputfile_macro_2, + usecols=(8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), + unpack=True, +) +time, T, Q, r = np.loadtxt(outputfile_macro_2, usecols=(4, 5, 6, 7), unpack=True) +Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt( + outputfile_macro_2, usecols=(20, 21, 22, 23, 24, 25, 26), unpack=True +) + +ax = fig.add_subplot(2, 2, 1) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel(r"Strain $\varepsilon_{22}$", size=15) +plt.ylabel(r"Stress $\sigma_{22}$ (MPa)", size=15) +plt.plot(e22, s22, c="black", label="direction 2") +plt.legend(loc="best") + +ax = fig.add_subplot(2, 2, 2) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"Temperature $\theta$ (K)", size=15) +plt.plot(time, T, c="black", label="temperature") +plt.legend(loc="best") + +ax = fig.add_subplot(2, 2, 3) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_m$", size=15) +plt.plot(time, Wm, c="black", label=r"$W_m$") +plt.plot(time, Wm_r, c="green", label=r"$W_m^r$") +plt.plot(time, Wm_ir, c="blue", label=r"$W_m^{ir}$") +plt.plot(time, Wm_d, c="red", label=r"$W_m^d$") +plt.legend(loc="best") + +ax = fig.add_subplot(2, 2, 4) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_t$", size=15) +plt.plot(time, Wt, c="black", label=r"$W_t$") +plt.plot(time, Wt_r, c="green", label=r"$W_t^r$") +plt.plot(time, Wt_ir, c="blue", label=r"$W_t^{ir}$") +plt.legend(loc="best") + +plt.show() + +################################################################################### +# Plotting the results -- Loading direction 3 +# ----------------------------------------------- + +fig = plt.figure() + +e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt( + outputfile_macro_3, + usecols=(8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), + unpack=True, +) +time, T, Q, r = np.loadtxt(outputfile_macro_3, usecols=(4, 5, 6, 7), unpack=True) +Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt( + outputfile_macro_3, usecols=(20, 21, 22, 23, 24, 25, 26), unpack=True +) + +ax = fig.add_subplot(2, 2, 1) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel(r"Strain $\varepsilon_{33}$", size=15) +plt.ylabel(r"Stress $\sigma_{33}$ (MPa)", size=15) +plt.plot(e33, s33, c="black", label="direction 3") +plt.legend(loc="best") + +ax = fig.add_subplot(2, 2, 2) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"Temperature $\theta$ (K)", size=15) +plt.plot(time, T, c="black", label="temperature") +plt.legend(loc="best") + +ax = fig.add_subplot(2, 2, 3) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_m$", size=15) +plt.plot(time, Wm, c="black", label=r"$W_m$") +plt.plot(time, Wm_r, c="green", label=r"$W_m^r$") +plt.plot(time, Wm_ir, c="blue", label=r"$W_m^{ir}$") +plt.plot(time, Wm_d, c="red", label=r"$W_m^d$") +plt.legend(loc="best") + +ax = fig.add_subplot(2, 2, 4) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_t$", size=15) +plt.plot(time, Wt, c="black", label=r"$W_t$") +plt.plot(time, Wt_r, c="green", label=r"$W_t^r$") +plt.plot(time, Wt_ir, c="blue", label=r"$W_t^{ir}$") +plt.legend(loc="best") + +plt.show() diff --git a/examples/thermomechanical/EPICP.py b/examples/thermomechanical/EPICP.py new file mode 100644 index 000000000..6795cf117 --- /dev/null +++ b/examples/thermomechanical/EPICP.py @@ -0,0 +1,271 @@ +""" +Plasticity with isotropic hardening (thermomechanical) +======================================================== +""" + +import numpy as np +import matplotlib.pyplot as plt +import simcoon as sim +import os + +plt.rcParams["figure.figsize"] = (18, 10) + +################################################################################### +# The elastic-plastic (isotropic hardening) constitutive law implemented in simcoon +# is a rate independent, isotropic, von Mises type material with power-law isotropic +# hardening. Eight parameters are required for the thermomechanical version: +# +# 1. The density :math:`\rho` +# 2. The specific heat :math:`c_p` +# 3. The Young modulus :math:`E` +# 4. The Poisson ratio :math:`\nu` +# 5. The coefficient of thermal expansion :math:`\alpha` +# 6. The von Mises equivalent yield stress limit :math:`\sigma_{Y}` +# 7. The hardening parameter :math:`k` +# 8. The hardening exponent :math:`m` +# +# The constitutive law is given by: +# +# .. math:: +# +# {\sigma}_{ij} & = L_{ijkl}\left({\varepsilon}^{\textrm{tot}}_{kl}-\alpha_{kl}\left(T-T^{\textrm{ref}}\right)-{\varepsilon}^{\textrm{p}}_{kl}\right) \\\\ +# \dot{\varepsilon}^{\textrm{p}}_{ij} & =\dot{p}\Lambda_{ij}, \quad \Lambda_{ij}=\frac{3}{2}\frac{\sigma'_{ij}}{\overline{\sigma}}, \quad \overline{\sigma}=\sqrt{\frac{3}{2}\sigma'_{kl}\sigma'_{kl}}, \\\\ +# \Phi & =\overline{\sigma}-\sigma_{Y}-kp^m\leq 0, \quad \dot{p}\geq0,~~~ \dot{p}~\Phi=0 +# +# The updated work terms and internal heat production :math:`r` are determined +# with the thermomechanical algorithm. + +umat_name = "EPICP" # 5 character code for the elastic-plastic subroutine +nstatev = 8 # Number of internal variables + +# Material parameters +rho = 4.4 # Density +c_p = 0.656 # Specific heat capacity +E = 113800.0 # Young's modulus (MPa) +nu = 0.342 # Poisson ratio +alpha = 0.86e-5 # Thermal expansion coefficient +sigma_Y = 500.0 # Yield stress (MPa) +H = 1600.0 # Hardening parameter +beta = 0.25 # Hardening exponent + +psi_rve = 0.0 +theta_rve = 0.0 +phi_rve = 0.0 +solver_type = 0 +corate_type = 2 + +# Define the properties +props = np.array([rho, c_p, E, nu, alpha, sigma_Y, H, beta]) + +path_data = "../data" +path_results = "results" + +# Run the simulation +pathfile = "THERM_EPICP_path.txt" +outputfile = "results_THERM_EPICP.txt" + +sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile, +) + +################################################################################### +# Plotting the results +# ---------------------- +# +# We plot the stress-strain curve, the temperature evolution, the mechanical work +# terms (:math:`W_m`, :math:`W_m^r`, :math:`W_m^{ir}`, :math:`W_m^d`) and the +# thermal work terms (:math:`W_t`, :math:`W_t^r`, :math:`W_t^{ir}`). + +fig = plt.figure() +outputfile_macro = os.path.join(path_results, "results_THERM_EPICP_global-0.txt") + +# Get the data +e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt( + outputfile_macro, + usecols=(8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), + unpack=True, +) +time, T, Q, r = np.loadtxt(outputfile_macro, usecols=(4, 5, 6, 7), unpack=True) +Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt( + outputfile_macro, usecols=(20, 21, 22, 23, 24, 25, 26), unpack=True +) + +# Stress vs Strain +ax = fig.add_subplot(2, 2, 1) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel(r"Strain $\varepsilon_{11}$", size=15) +plt.ylabel(r"Stress $\sigma_{11}$ (MPa)", size=15) +plt.plot(e11, s11, c="black", label="direction 1") +plt.legend(loc="best") + +# Temperature vs Time +ax = fig.add_subplot(2, 2, 2) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"Temperature $\theta$ (K)", size=15) +plt.plot(time, T, c="black", label="temperature") +plt.legend(loc="best") + +# Mechanical work vs Time +ax = fig.add_subplot(2, 2, 3) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_m$", size=15) +plt.plot(time, Wm, c="black", label=r"$W_m$") +plt.plot(time, Wm_r, c="green", label=r"$W_m^r$") +plt.plot(time, Wm_ir, c="blue", label=r"$W_m^{ir}$") +plt.plot(time, Wm_d, c="red", label=r"$W_m^d$") +plt.legend(loc="best") + +# Thermal work vs Time +ax = fig.add_subplot(2, 2, 4) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_t$", size=15) +plt.plot(time, Wt, c="black", label=r"$W_t$") +plt.plot(time, Wt_r, c="green", label=r"$W_t^r$") +plt.plot(time, Wt_ir, c="blue", label=r"$W_t^{ir}$") +plt.legend(loc="best") + +plt.show() + +################################################################################### +# Increment size effect +# ----------------------- +# +# Here we test the effect of the increment size on the results. We run the same +# simulation with 1, 10, 100, and 1000 increments. + +increments = [1, 10, 100, 1000] +outputfile_globals = {} + +for inc in increments: + pathfile = f"THERM_EPICP_path_{inc}.txt" + outputfile = f"results_THERM_EPICP_{inc}.txt" + sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile, + ) + outputfile_globals[inc] = f"results_THERM_EPICP_{inc}_global-0.txt" + +# Load data for each increment +data = [] +for inc in increments: + path = os.path.join(path_results, outputfile_globals[inc]) + e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt( + path, usecols=range(8, 20), unpack=True + ) + time, T, Q, r = np.loadtxt(path, usecols=range(4, 8), unpack=True) + Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt( + path, usecols=range(20, 27), unpack=True + ) + data.append( + { + "e11": e11, "s11": s11, "time": time, "T": T, + "Wm": Wm, "Wm_r": Wm_r, "Wm_ir": Wm_ir, "Wm_d": Wm_d, + "Wt": Wt, "Wt_r": Wt_r, "Wt_ir": Wt_ir, + } + ) + +################################################################################### +# Plotting the increment size comparison +# ----------------------------------------- +# +# We compare the stress-strain curves, the temperature evolution, the mechanical +# work terms and the thermal work terms for different increment sizes. + +fig = plt.figure() + +markers = ["D", "o", "x", None] +labels = ["1 increment", "10 increments", "100 increments", "1000 increments"] + +# Stress vs Strain +ax = fig.add_subplot(2, 2, 1) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel(r"Strain $\varepsilon_{11}$", size=15) +plt.ylabel(r"Stress $\sigma_{11}$ (MPa)", size=15) +for i, d in enumerate(data): + if markers[i] is not None: + plt.plot(d["e11"], d["s11"], linestyle="None", marker=markers[i], + color="black", markersize=10, label=labels[i]) + else: + plt.plot(d["e11"], d["s11"], c="black", label=labels[i]) +plt.legend(loc="best") + +# Temperature vs Time +ax = fig.add_subplot(2, 2, 2) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"Temperature $\theta$ (K)", size=15) +for i, d in enumerate(data): + if markers[i] is not None: + plt.plot(d["time"], d["T"], linestyle="None", marker=markers[i], + color="black", markersize=10, label=labels[i]) + else: + plt.plot(d["time"], d["T"], c="black", label=labels[i]) +plt.legend(loc="best") + +# Mechanical work vs Time +ax = fig.add_subplot(2, 2, 3) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_m$", size=15) +work_colors = ["black", "green", "blue", "red"] +work_keys = ["Wm", "Wm_r", "Wm_ir", "Wm_d"] +work_labels = [r"$W_m$", r"$W_m^r$", r"$W_m^{ir}$", r"$W_m^d$"] +for i, d in enumerate(data): + for j, (wk, wc, wl) in enumerate(zip(work_keys, work_colors, work_labels)): + if markers[i] is not None: + plt.plot(d["time"], d[wk], linestyle="None", marker=markers[i], + color=wc, markersize=10) + else: + plt.plot(d["time"], d[wk], c=wc, label=wl) +plt.legend(loc="best") + +# Thermal work vs Time +ax = fig.add_subplot(2, 2, 4) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_t$", size=15) +therm_keys = ["Wt", "Wt_r", "Wt_ir"] +therm_labels = [r"$W_t$", r"$W_t^r$", r"$W_t^{ir}$"] +therm_colors = ["black", "green", "blue"] +for i, d in enumerate(data): + for j, (wk, wc, wl) in enumerate(zip(therm_keys, therm_colors, therm_labels)): + if markers[i] is not None: + plt.plot(d["time"], d[wk], linestyle="None", marker=markers[i], + color=wc, markersize=10) + else: + plt.plot(d["time"], d[wk], c=wc, label=wl) +plt.legend(loc="best") + +plt.show() diff --git a/examples/thermomechanical/EPKCP.py b/examples/thermomechanical/EPKCP.py new file mode 100644 index 000000000..2c82d8cdd --- /dev/null +++ b/examples/thermomechanical/EPKCP.py @@ -0,0 +1,267 @@ +""" +Plasticity with kinematic hardening (thermomechanical) +======================================================== +""" + +import numpy as np +import matplotlib.pyplot as plt +import simcoon as sim +import os + +plt.rcParams["figure.figsize"] = (18, 10) + +################################################################################### +# The elastic-plastic (isotropic with kinematic hardening) constitutive law +# implemented in simcoon is a rate independent, isotropic, von Mises type material +# with power-law isotropic hardening and linear kinematic hardening. +# Nine parameters are required for the thermomechanical version: +# +# 1. The density :math:`\rho` +# 2. The specific heat :math:`c_p` +# 3. The Young modulus :math:`E` +# 4. The Poisson ratio :math:`\nu` +# 5. The coefficient of thermal expansion :math:`\alpha` +# 6. The von Mises equivalent yield stress limit :math:`\sigma_{Y}` +# 7. The hardening parameter :math:`k` +# 8. The hardening exponent :math:`m` +# 9. The kinematic hardening parameter :math:`k_X` +# +# The constitutive law is given by: +# +# .. math:: +# +# {\sigma}_{ij} & = L_{ijkl}\left({\varepsilon}^{\textrm{tot}}_{kl}-\alpha_{kl}\left(T-T^{\textrm{ref}}\right)-{\varepsilon}^{\textrm{p}}_{kl}\right) \\ +# \dot{\varepsilon}^{\textrm{p}}_{ij} & =\dot{p}\Lambda_{ij}, \quad \Lambda_{ij}=\frac{3}{2}\frac{\sigma'_{ij} - X_{ij}}{|\sigma - X|} \\ +# \dot{X}_{ij} & = \frac{2}{3} k_X \dot{\varepsilon}^{\textrm{p}}_{ij} \\ +# \Phi & =|\sigma - X|-\sigma_{Y}-kp^m\leq 0 + +umat_name = "EPKCP" # 5 character code for combined isotropic-kinematic hardening +nstatev = 14 # Number of internal variables + +# Material parameters +rho = 4.4 # Density +c_p = 0.656 # Specific heat capacity +E = 113800.0 # Young's modulus (MPa) +nu = 0.342 # Poisson ratio +alpha = 0.86e-5 # Thermal expansion coefficient +sigma_Y = 500.0 # Yield stress (MPa) +H = 1200.0 # Isotropic hardening parameter +beta = 0.25 # Isotropic hardening exponent +kX = 8000.0 # Kinematic hardening modulus + +psi_rve = 0.0 +theta_rve = 0.0 +phi_rve = 0.0 +solver_type = 0 +corate_type = 2 + +# Define the properties +props = np.array([rho, c_p, E, nu, alpha, sigma_Y, H, beta, kX]) + +path_data = "../data" +path_results = "results" + +# Run the simulation +pathfile = "THERM_EPKCP_path.txt" +outputfile = "results_THERM_EPKCP.txt" + +sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile, +) + +################################################################################### +# Plotting the results +# ---------------------- +# +# We plot the stress-strain curve, the temperature evolution, the mechanical work +# terms and the thermal work terms. + +fig = plt.figure() +outputfile_macro = os.path.join(path_results, "results_THERM_EPKCP_global-0.txt") + +# Get the data +e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt( + outputfile_macro, + usecols=(8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), + unpack=True, +) +time, T, Q, r = np.loadtxt(outputfile_macro, usecols=(4, 5, 6, 7), unpack=True) +Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt( + outputfile_macro, usecols=(20, 21, 22, 23, 24, 25, 26), unpack=True +) + +# Stress vs Strain +ax = fig.add_subplot(2, 2, 1) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel(r"Strain $\varepsilon_{11}$", size=15) +plt.ylabel(r"Stress $\sigma_{11}$ (MPa)", size=15) +plt.plot(e11, s11, c="black", label="direction 1") +plt.legend(loc="best") + +# Temperature vs Time +ax = fig.add_subplot(2, 2, 2) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"Temperature $\theta$ (K)", size=15) +plt.plot(time, T, c="black", label="temperature") +plt.legend(loc="best") + +# Mechanical work vs Time +ax = fig.add_subplot(2, 2, 3) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_m$", size=15) +plt.plot(time, Wm, c="black", label=r"$W_m$") +plt.plot(time, Wm_r, c="green", label=r"$W_m^r$") +plt.plot(time, Wm_ir, c="blue", label=r"$W_m^{ir}$") +plt.plot(time, Wm_d, c="red", label=r"$W_m^d$") +plt.legend(loc="best") + +# Thermal work vs Time +ax = fig.add_subplot(2, 2, 4) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_t$", size=15) +plt.plot(time, Wt, c="black", label=r"$W_t$") +plt.plot(time, Wt_r, c="green", label=r"$W_t^r$") +plt.plot(time, Wt_ir, c="blue", label=r"$W_t^{ir}$") +plt.legend(loc="best") + +plt.show() + +################################################################################### +# Increment size effect +# ----------------------- +# +# Here we test the effect of the increment size on the results. + +increments = [1, 10, 100, 1000] +outputfile_globals = {} + +for inc in increments: + pathfile = f"THERM_EPKCP_path_{inc}.txt" + outputfile = f"results_THERM_EPKCP_{inc}.txt" + sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile, + ) + outputfile_globals[inc] = f"results_THERM_EPKCP_{inc}_global-0.txt" + +# Load data for each increment +data = [] +for inc in increments: + path = os.path.join(path_results, outputfile_globals[inc]) + e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt( + path, usecols=range(8, 20), unpack=True + ) + time, T, Q, r = np.loadtxt(path, usecols=range(4, 8), unpack=True) + Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt( + path, usecols=range(20, 27), unpack=True + ) + data.append( + { + "e11": e11, "s11": s11, "time": time, "T": T, + "Wm": Wm, "Wm_r": Wm_r, "Wm_ir": Wm_ir, "Wm_d": Wm_d, + "Wt": Wt, "Wt_r": Wt_r, "Wt_ir": Wt_ir, + } + ) + +################################################################################### +# Plotting the increment size comparison +# ----------------------------------------- + +fig = plt.figure() + +markers = ["D", "o", "x", None] +labels = ["1 increment", "10 increments", "100 increments", "1000 increments"] + +# Stress vs Strain +ax = fig.add_subplot(2, 2, 1) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel(r"Strain $\varepsilon_{11}$", size=15) +plt.ylabel(r"Stress $\sigma_{11}$ (MPa)", size=15) +for i, d in enumerate(data): + if markers[i] is not None: + plt.plot(d["e11"], d["s11"], linestyle="None", marker=markers[i], + color="black", markersize=10, label=labels[i]) + else: + plt.plot(d["e11"], d["s11"], c="black", label=labels[i]) +plt.legend(loc="best") + +# Temperature vs Time +ax = fig.add_subplot(2, 2, 2) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"Temperature $\theta$ (K)", size=15) +for i, d in enumerate(data): + if markers[i] is not None: + plt.plot(d["time"], d["T"], linestyle="None", marker=markers[i], + color="black", markersize=10, label=labels[i]) + else: + plt.plot(d["time"], d["T"], c="black", label=labels[i]) +plt.legend(loc="best") + +# Mechanical work vs Time +ax = fig.add_subplot(2, 2, 3) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_m$", size=15) +work_colors = ["black", "green", "blue", "red"] +work_keys = ["Wm", "Wm_r", "Wm_ir", "Wm_d"] +work_labels = [r"$W_m$", r"$W_m^r$", r"$W_m^{ir}$", r"$W_m^d$"] +for i, d in enumerate(data): + for j, (wk, wc, wl) in enumerate(zip(work_keys, work_colors, work_labels)): + if markers[i] is not None: + plt.plot(d["time"], d[wk], linestyle="None", marker=markers[i], + color=wc, markersize=10) + else: + plt.plot(d["time"], d[wk], c=wc, label=wl) +plt.legend(loc="best") + +# Thermal work vs Time +ax = fig.add_subplot(2, 2, 4) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_t$", size=15) +therm_keys = ["Wt", "Wt_r", "Wt_ir"] +therm_labels = [r"$W_t$", r"$W_t^r$", r"$W_t^{ir}$"] +therm_colors = ["black", "green", "blue"] +for i, d in enumerate(data): + for j, (wk, wc, wl) in enumerate(zip(therm_keys, therm_colors, therm_labels)): + if markers[i] is not None: + plt.plot(d["time"], d[wk], linestyle="None", marker=markers[i], + color=wc, markersize=10) + else: + plt.plot(d["time"], d[wk], c=wc, label=wl) +plt.legend(loc="best") + +plt.show() diff --git a/examples/thermomechanical/README.rst b/examples/thermomechanical/README.rst new file mode 100644 index 000000000..438687f5e --- /dev/null +++ b/examples/thermomechanical/README.rst @@ -0,0 +1,25 @@ +Thermomechanical Constitutive Laws +----------------------------------------------- + +Below are examples illustrating Simcoon's thermomechanical constitutive laws. +These examples extend the mechanical models with thermal coupling, including +temperature evolution, thermal expansion, and dissipation. + +**Thermoelastic Models:** + +- **ELISO** - Isotropic elasticity with thermal coupling +- **ELIST** - Transversely isotropic elasticity with thermal coupling +- **ELORT** - Orthotropic elasticity with thermal coupling + +**Thermoplasticity Models:** + +- **EPICP** - Plasticity with isotropic hardening and thermal dissipation +- **EPKCP** - Plasticity with kinematic hardening and thermal dissipation + +**Thermoviscoelastic Models:** + +- **ZENER** - Zener model with thermal coupling + +**Shape Memory Alloys:** + +- **SMA_T** - SMA with full thermomechanical coupling diff --git a/examples/thermomechanical/SMA_T.py b/examples/thermomechanical/SMA_T.py new file mode 100644 index 000000000..16b60a865 --- /dev/null +++ b/examples/thermomechanical/SMA_T.py @@ -0,0 +1,309 @@ +""" +Shape Memory Alloy - Thermomechanical coupling +================================================= +""" + +import numpy as np +import matplotlib.pyplot as plt +import simcoon as sim +import os + +plt.rcParams["figure.figsize"] = (18, 10) + +################################################################################### +# The SMA (transformation-only) constitutive law implemented in simcoon is a +# rate independent description of phase transformation where two transformations +# (austenite to martensite, and martensite to austenite) are considered as +# independent mechanisms. +# +# 31 parameters are required for the thermomechanical version: +# +# 1. :math:`\rho` -- density +# 2. :math:`c_{pA}` -- specific heat capacity of austenite +# 3. :math:`c_{pM}` -- specific heat capacity of martensite +# 4. flagT -- 0: transformation temperatures linearly extrapolated; 1: smooth +# 5. :math:`E_A` -- Young's modulus of austenite +# 6. :math:`E_M` -- Young's modulus of martensite +# 7. :math:`\nu_A` -- Poisson's ratio of austenite +# 8. :math:`\nu_M` -- Poisson's ratio of martensite +# 9. :math:`\alpha_A` -- CTE of austenite +# 10. :math:`\alpha_M` -- CTE of martensite +# 11. :math:`H_{min}` -- minimal transformation strain magnitude +# 12. :math:`H_{max}` -- maximal transformation strain magnitude +# 13. :math:`k_1` -- exponential evolution of transformation strain magnitude +# 14. :math:`\sigma_{crit}` -- critical stress for change of transformation strain magnitude +# 15. :math:`C_A` -- slope of martensite to austenite parameter +# 16. :math:`C_M` -- slope of austenite to martensite parameter +# 17. :math:`M_{s0}` -- martensite start at zero stress +# 18. :math:`M_{f0}` -- martensite finish at zero stress +# 19. :math:`A_{s0}` -- austenite start at zero stress +# 20. :math:`A_{f0}` -- austenite finish at zero stress +# 21. :math:`n_1` -- martensite start smooth exponent +# 22. :math:`n_2` -- martensite finish smooth exponent +# 23. :math:`n_3` -- austenite start smooth exponent +# 24. :math:`n_4` -- austenite finish smooth exponent +# 25. :math:`\sigma_{caliber}` -- calibration stress +# 26. :math:`b_{prager}` -- Prager parameter +# 27. :math:`n_{prager}` -- Prager exponent +# 28. :math:`c_{\lambda}` -- penalty function exponent start point +# 29. :math:`p_{0\lambda}` -- penalty function exponent limit penalty value +# 30. :math:`n_{\lambda}` -- penalty function power law exponent +# 31. :math:`\alpha_{\lambda}` -- penalty function power law parameter + +umat_name = "SMAUT" # 5 character code for the SMA subroutine +nstatev = 17 # Number of internal variables + +# Material parameters +rho = 5.5 # Density +c_pA = 0.5 # Specific heat capacity (austenite) +c_pM = 0.5 # Specific heat capacity (martensite) +flagT = 0 # 0: linear extrapolation; 1: smooth +E_A = 67538.0 # Young's modulus of austenite (MPa) +E_M = 67538.0 # Young's modulus of martensite (MPa) +nu_A = 0.349 # Poisson's ratio of austenite +nu_M = 0.349 # Poisson's ratio of martensite +alphaA = 1.0e-6 # CTE of austenite +alphaM = 1.0e-6 # CTE of martensite +Hmin = 0.0 # Minimal transformation strain +Hmax = 0.0418 # Maximal transformation strain +k1 = 0.021 # Exponential evolution of transformation strain +sigmacrit = 0.0 # Critical stress for transformation strain +C_A = 10.0 # Slope of martensite -> austenite +C_M = 10.0 # Slope of austenite -> martensite +Ms0 = 300.0 # Martensite start at zero stress (K) +Mf0 = 290.0 # Martensite finish at zero stress (K) +As0 = 295.0 # Austenite start at zero stress (K) +Af0 = 305.0 # Austenite finish at zero stress (K) +n1 = 0.2 # Martensite start smooth exponent +n2 = 0.2 # Martensite finish smooth exponent +n3 = 0.2 # Austenite start smooth exponent +n4 = 0.2 # Austenite finish smooth exponent +sigmacaliber = 300.0 # Calibration stress +b_prager = 0.2 # Prager parameter +n_prager = 2.0 # Prager exponent +c_lambda = 1.0e-6 # Penalty function exponent start point +p0_lambda = 1.0e-3 # Penalty function exponent limit penalty value +n_lambda = 1.0 # Penalty function power law exponent +alpha_lambda = 1.0e8 # Penalty function power law parameter + +psi_rve = 0.0 +theta_rve = 0.0 +phi_rve = 0.0 +solver_type = 0 +corate_type = 2 + +# Define the properties +props = np.array([ + rho, c_pA, c_pM, flagT, E_A, E_M, nu_A, nu_M, alphaA, alphaM, + Hmin, Hmax, k1, sigmacrit, C_A, C_M, Ms0, Mf0, As0, Af0, + n1, n2, n3, n4, sigmacaliber, b_prager, n_prager, + c_lambda, p0_lambda, n_lambda, alpha_lambda, +]) + +path_data = "../data" +path_results = "results" + +# Run the simulation +pathfile = "THERM_SMAUT_path.txt" +outputfile = "results_THERM_SMAUT.txt" + +sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile, +) + +################################################################################### +# Plotting the results +# ---------------------- +# +# We plot the stress-strain curve, the temperature evolution, the mechanical work +# terms and the thermal work terms for the SMA thermomechanical response. + +fig = plt.figure() +outputfile_macro = os.path.join(path_results, "results_THERM_SMAUT_global-0.txt") + +# Get the data +e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt( + outputfile_macro, + usecols=(8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), + unpack=True, +) +time, T, Q, r = np.loadtxt(outputfile_macro, usecols=(4, 5, 6, 7), unpack=True) +Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt( + outputfile_macro, usecols=(20, 21, 22, 23, 24, 25, 26), unpack=True +) + +# Stress vs Strain +ax = fig.add_subplot(2, 2, 1) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel(r"Strain $\varepsilon_{11}$", size=15) +plt.ylabel(r"Stress $\sigma_{11}$ (MPa)", size=15) +plt.plot(e11, s11, c="black") +plt.legend(loc="best") + +# Temperature vs Time +ax = fig.add_subplot(2, 2, 2) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"Temperature $\theta$ (K)", size=15) +plt.plot(time, T, c="black") +plt.legend(loc="best") + +# Mechanical work vs Time +ax = fig.add_subplot(2, 2, 3) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_m$", size=15) +plt.plot(time, Wm, c="black", label=r"$W_m$") +plt.plot(time, Wm_r, c="green", label=r"$W_m^r$") +plt.plot(time, Wm_ir, c="blue", label=r"$W_m^{ir}$") +plt.plot(time, Wm_d, c="red", label=r"$W_m^d$") +plt.legend(loc="best") + +# Thermal work vs Time +ax = fig.add_subplot(2, 2, 4) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_t$", size=15) +plt.plot(time, Wt, c="black", label=r"$W_t$") +plt.plot(time, Wt_r, c="green", label=r"$W_t^r$") +plt.plot(time, Wt_ir, c="blue", label=r"$W_t^{ir}$") +plt.legend(loc="best") + +plt.show() + +################################################################################### +# Increment size effect +# ----------------------- +# +# Here we test the effect of the increment size on the results. + +increments = [10, 100, 1000] +outputfile_globals = {} + +for inc in increments: + pathfile = f"THERM_SMAUT_path_{inc}.txt" + outputfile = f"results_THERM_SMAUT_{inc}.txt" + sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile, + ) + outputfile_globals[inc] = f"results_THERM_SMAUT_{inc}_global-0.txt" + +# Load data for each increment +data = [] +for inc in increments: + path = os.path.join(path_results, outputfile_globals[inc]) + e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt( + path, usecols=range(8, 20), unpack=True + ) + time, T, Q, r = np.loadtxt(path, usecols=range(4, 8), unpack=True) + Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt( + path, usecols=range(20, 27), unpack=True + ) + data.append( + { + "e11": e11, "s11": s11, "time": time, "T": T, + "Wm": Wm, "Wm_r": Wm_r, "Wm_ir": Wm_ir, "Wm_d": Wm_d, + "Wt": Wt, "Wt_r": Wt_r, "Wt_ir": Wt_ir, + } + ) + +################################################################################### +# Plotting the increment size comparison +# ----------------------------------------- + +fig = plt.figure() + +markers = ["o", "x", None] +labels = ["10 increments", "100 increments", "1000 increments"] + +# Stress vs Strain +ax = fig.add_subplot(2, 2, 1) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel(r"Strain $\varepsilon_{11}$", size=15) +plt.ylabel(r"Stress $\sigma_{11}$ (MPa)", size=15) +for i, d in enumerate(data): + if markers[i] is not None: + plt.plot(d["e11"], d["s11"], linestyle="None", marker=markers[i], + color="black", markersize=10, label=labels[i]) + else: + plt.plot(d["e11"], d["s11"], c="black", label=labels[i]) +plt.legend(loc="best") + +# Temperature vs Time +ax = fig.add_subplot(2, 2, 2) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"Temperature $\theta$ (K)", size=15) +for i, d in enumerate(data): + if markers[i] is not None: + plt.plot(d["time"], d["T"], linestyle="None", marker=markers[i], + color="black", markersize=10, label=labels[i]) + else: + plt.plot(d["time"], d["T"], c="black", label=labels[i]) +plt.legend(loc="best") + +# Mechanical work vs Time +ax = fig.add_subplot(2, 2, 3) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_m$", size=15) +work_colors = ["black", "green", "blue", "red"] +work_keys = ["Wm", "Wm_r", "Wm_ir", "Wm_d"] +work_labels = [r"$W_m$", r"$W_m^r$", r"$W_m^{ir}$", r"$W_m^d$"] +for i, d in enumerate(data): + for j, (wk, wc, wl) in enumerate(zip(work_keys, work_colors, work_labels)): + if markers[i] is not None: + plt.plot(d["time"], d[wk], linestyle="None", marker=markers[i], + color=wc, markersize=10) + else: + plt.plot(d["time"], d[wk], c=wc, label=wl) +plt.legend(loc="best") + +# Thermal work vs Time +ax = fig.add_subplot(2, 2, 4) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_t$", size=15) +therm_keys = ["Wt", "Wt_r", "Wt_ir"] +therm_labels = [r"$W_t$", r"$W_t^r$", r"$W_t^{ir}$"] +therm_colors = ["black", "green", "blue"] +for i, d in enumerate(data): + for j, (wk, wc, wl) in enumerate(zip(therm_keys, therm_colors, therm_labels)): + if markers[i] is not None: + plt.plot(d["time"], d[wk], linestyle="None", marker=markers[i], + color=wc, markersize=10) + else: + plt.plot(d["time"], d[wk], c=wc, label=wl) +plt.legend(loc="best") + +plt.show() diff --git a/examples/thermomechanical/ZENER.py b/examples/thermomechanical/ZENER.py new file mode 100644 index 000000000..34fd911aa --- /dev/null +++ b/examples/thermomechanical/ZENER.py @@ -0,0 +1,265 @@ +""" +Zener viscoelastic model (thermomechanical) +============================================= +""" + +import numpy as np +import matplotlib.pyplot as plt +import simcoon as sim +import os + +plt.rcParams["figure.figsize"] = (18, 10) + +################################################################################### +# The Poynting-Thomson (Zener) constitutive law is a rate-dependent, isotropic, +# linear viscoelastic model that accounts for thermal strains. It consists of +# an elastic spring in parallel with a Maxwell element (spring + dashpot in series). +# +# Nine parameters are required for the thermomechanical version: +# +# 1. The density :math:`\rho` +# 2. The specific heat :math:`c_p` +# 3. The thermoelastic Young's modulus :math:`E_0` +# 4. The thermoelastic Poisson's ratio :math:`\nu_0` +# 5. The coefficient of thermal expansion :math:`\alpha` +# 6. The viscoelastic Young's modulus of the Zener branch :math:`E_1` +# 7. The viscoelastic Poisson's ratio of the Zener branch :math:`\nu_1` +# 8. The bulk viscosity of the Zener branch :math:`\eta_B` +# 9. The shear viscosity of the Zener branch :math:`\eta_S` +# +# The viscoelastic material constitutive law is implemented using a +# *fast scalar updating method*. The updated stress is provided for 1D, +# plane stress, and generalized plane strain/3D analysis. +# The updated work terms and internal heat production :math:`r` are +# determined with the thermomechanical algorithm. + +umat_name = "ZENER" # 5 character code for the Zener model +nstatev = 8 # Number of internal variables + +# Material parameters +rho = 4.4 # Density +c_p = 0.656 # Specific heat capacity +E_0 = 3000.0 # Thermoelastic Young's modulus (MPa) +nu_0 = 0.4 # Thermoelastic Poisson's ratio +alpha = 0.86e-5 # Thermal expansion coefficient +E_1 = 1200.0 # Viscoelastic Young's modulus (MPa) +nu_1 = 0.3 # Viscoelastic Poisson's ratio +eta_B = 12500.0 # Bulk viscosity +eta_S = 400.0 # Shear viscosity + +psi_rve = 0.0 +theta_rve = 0.0 +phi_rve = 0.0 +solver_type = 0 +corate_type = 0 + +# Define the properties +props = np.array([rho, c_p, E_0, nu_0, alpha, E_1, nu_1, eta_B, eta_S]) + +path_data = "../data" +path_results = "results" + +# Run the simulation +pathfile = "THERM_ZENER_path.txt" +outputfile = "results_THERM_ZENER.txt" + +sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile, +) + +################################################################################### +# Plotting the results +# ---------------------- +# +# We plot the stress-strain response, the temperature evolution, the mechanical +# work terms and the thermal work terms. + +fig = plt.figure() +outputfile_macro = os.path.join(path_results, "results_THERM_ZENER_global-0.txt") + +# Get the data +e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt( + outputfile_macro, + usecols=(8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), + unpack=True, +) +time, T, Q, r = np.loadtxt(outputfile_macro, usecols=(4, 5, 6, 7), unpack=True) +Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt( + outputfile_macro, usecols=(20, 21, 22, 23, 24, 25, 26), unpack=True +) + +# Stress vs Strain +ax = fig.add_subplot(2, 2, 1) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel(r"Strain $\varepsilon_{11}$", size=15) +plt.ylabel(r"Stress $\sigma_{11}$ (MPa)", size=15) +plt.plot(e11, s11, c="black", label="direction 1") +plt.legend(loc="best") + +# Temperature vs Time +ax = fig.add_subplot(2, 2, 2) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"Temperature $\theta$ (K)", size=15) +plt.plot(time, T, c="black", label="temperature") +plt.legend(loc="best") + +# Mechanical work vs Time +ax = fig.add_subplot(2, 2, 3) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_m$", size=15) +plt.plot(time, Wm, c="black", label=r"$W_m$") +plt.plot(time, Wm_r, c="green", label=r"$W_m^r$") +plt.plot(time, Wm_ir, c="blue", label=r"$W_m^{ir}$") +plt.plot(time, Wm_d, c="red", label=r"$W_m^d$") +plt.legend(loc="best") + +# Thermal work vs Time +ax = fig.add_subplot(2, 2, 4) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_t$", size=15) +plt.plot(time, Wt, c="black", label=r"$W_t$") +plt.plot(time, Wt_r, c="green", label=r"$W_t^r$") +plt.plot(time, Wt_ir, c="blue", label=r"$W_t^{ir}$") +plt.legend(loc="best") + +plt.show() + +################################################################################### +# Increment size effect +# ----------------------- +# +# Here we test the effect of the increment size on the results. + +increments = [1, 10, 100, 1000] +outputfile_globals = {} + +for inc in increments: + pathfile = f"THERM_ZENER_path_{inc}.txt" + outputfile = f"results_THERM_ZENER_{inc}.txt" + sim.solver( + umat_name, + props, + nstatev, + psi_rve, + theta_rve, + phi_rve, + solver_type, + corate_type, + path_data, + path_results, + pathfile, + outputfile, + ) + outputfile_globals[inc] = f"results_THERM_ZENER_{inc}_global-0.txt" + +# Load data for each increment +data = [] +for inc in increments: + path = os.path.join(path_results, outputfile_globals[inc]) + e11, e22, e33, e12, e13, e23, s11, s22, s33, s12, s13, s23 = np.loadtxt( + path, usecols=range(8, 20), unpack=True + ) + time, T, Q, r = np.loadtxt(path, usecols=range(4, 8), unpack=True) + Wm, Wm_r, Wm_ir, Wm_d, Wt, Wt_r, Wt_ir = np.loadtxt( + path, usecols=range(20, 27), unpack=True + ) + data.append( + { + "e11": e11, "s11": s11, "time": time, "T": T, + "Wm": Wm, "Wm_r": Wm_r, "Wm_ir": Wm_ir, "Wm_d": Wm_d, + "Wt": Wt, "Wt_r": Wt_r, "Wt_ir": Wt_ir, + } + ) + +################################################################################### +# Plotting the increment size comparison +# ----------------------------------------- + +fig = plt.figure() + +markers = ["D", "o", "x", None] +labels = ["1 increment", "10 increments", "100 increments", "1000 increments"] + +# Stress vs Strain +ax = fig.add_subplot(2, 2, 1) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel(r"Strain $\varepsilon_{11}$", size=15) +plt.ylabel(r"Stress $\sigma_{11}$ (MPa)", size=15) +for i, d in enumerate(data): + if markers[i] is not None: + plt.plot(d["e11"], d["s11"], linestyle="None", marker=markers[i], + color="black", markersize=10, label=labels[i]) + else: + plt.plot(d["e11"], d["s11"], c="black", label=labels[i]) +plt.legend(loc="best") + +# Temperature vs Time +ax = fig.add_subplot(2, 2, 2) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"Temperature $\theta$ (K)", size=15) +for i, d in enumerate(data): + if markers[i] is not None: + plt.plot(d["time"], d["T"], linestyle="None", marker=markers[i], + color="black", markersize=10, label=labels[i]) + else: + plt.plot(d["time"], d["T"], c="black", label=labels[i]) +plt.legend(loc="best") + +# Mechanical work vs Time +ax = fig.add_subplot(2, 2, 3) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_m$", size=15) +work_colors = ["black", "green", "blue", "red"] +work_keys = ["Wm", "Wm_r", "Wm_ir", "Wm_d"] +work_labels = [r"$W_m$", r"$W_m^r$", r"$W_m^{ir}$", r"$W_m^d$"] +for i, d in enumerate(data): + for j, (wk, wc, wl) in enumerate(zip(work_keys, work_colors, work_labels)): + if markers[i] is not None: + plt.plot(d["time"], d[wk], linestyle="None", marker=markers[i], + color=wc, markersize=10) + else: + plt.plot(d["time"], d[wk], c=wc, label=wl) +plt.legend(loc="best") + +# Thermal work vs Time +ax = fig.add_subplot(2, 2, 4) +plt.grid(True) +plt.tick_params(axis="both", which="major", labelsize=15) +plt.xlabel("time (s)", size=15) +plt.ylabel(r"$W_t$", size=15) +therm_keys = ["Wt", "Wt_r", "Wt_ir"] +therm_labels = [r"$W_t$", r"$W_t^r$", r"$W_t^{ir}$"] +therm_colors = ["black", "green", "blue"] +for i, d in enumerate(data): + for j, (wk, wc, wl) in enumerate(zip(therm_keys, therm_colors, therm_labels)): + if markers[i] is not None: + plt.plot(d["time"], d[wk], linestyle="None", marker=markers[i], + color=wc, markersize=10) + else: + plt.plot(d["time"], d[wk], c=wc, label=wl) +plt.legend(loc="best") + +plt.show() diff --git a/examples/umat_prototype_tensor.cpp b/examples/umat_prototype_tensor.cpp new file mode 100644 index 000000000..e15912a77 --- /dev/null +++ b/examples/umat_prototype_tensor.cpp @@ -0,0 +1,243 @@ +/* + * PROTOTYPE: Elastic-plastic UMAT using tensor2 / tensor4 + * ======================================================== + * + * Side-by-side comparison with the current raw arma::vec / arma::mat style. + * NOT meant to compile or replace anything — just to show how the code reads. + */ + +#include +#include +#include +#include +#include + +using namespace arma; +using namespace simcoon; + + +// ============================================================================ +// Isotropic elasticity — 3 lines of mechanics +// ============================================================================ +// +// BEFORE (raw arma): +// L = L_iso(E, nu, "Enu"); // what type? who knows +// vec Eel = Etot + DEtot - alpha*(T+DT-T_init); // stress voigt? strain voigt? +// sigma = el_pred(L, Eel, ndi); // hope you got the convention right +// Lt = L; +// +// AFTER (typed): + +void umat_elasticity_iso_typed( + const vec &Etot, const vec &DEtot, + vec &sigma_out, mat &Lt_out, mat &L_out, + const vec &props, double T_init) +{ + // --- The actual mechanics: 3 lines --- + tensor4 L(L_iso(props(0), props(1), "Enu"), Tensor4Type::stiffness); + tensor2 sigma = L * strain(Etot + DEtot - props(2)*(T_init))); + Lt_out = L_out = L.mat(); + + sigma_out = sigma.voigt(); +} + + +// ============================================================================ +// NEW STYLE — elastic-plastic with isotropic hardening (CCP) +// ============================================================================ + +void umat_plasticity_iso_CCP_typed( + const vec &Etot, const vec &DEtot, + vec &sigma_out, mat &Lt_out, mat &L_out, + vec &sigma_in, + const mat &DR, + const vec &props, vec &statev, + const double &T, const double &DT, + const bool &start, const int &solver_type) +{ + double E = props(0); + double nu = props(1); + double alpha_v = props(2); + double sigmaY = props(3); + double k = props(4); + double m = props(5); + + double T_init = statev(0); + double p = statev(1); + + // --- Typed stiffness --- + tensor4 L(L_iso(E, nu, "Enu"), Tensor4Type::stiffness); + + // --- Plastic strain: tagged as VoigtType::strain --- + // Can't accidentally rotate it with rotate_stress anymore. + tensor2 EP = tensor2::from_voigt( + vec::fixed<6>(statev.memptr() + 2), VoigtType::strain + ); + + // --- Rotation of internal variables --- + // EP.rotate() dispatches to apply_strain automatically. + // With raw code you'd write: EP = rotate_strain(EP, DR); + // ... and if you wrote rotate_stress(EP, DR) by mistake: silent bug. + Rotation dR = Rotation::from_matrix(DR); + EP = EP.rotate(dR); + + if (start) { + T_init = T; + EP = tensor2::zeros(VoigtType::strain); + p = 0.0; + } + + // --- CTE as a strain tensor --- + tensor2 alpha_tensor = tensor2::identity(VoigtType::strain) * alpha_v; + + // --- Elastic predictor --- + tensor2 Etot_t = strain(Etot + DEtot); + + tensor2 thermal = alpha_tensor * (T + DT - T_init); + tensor2 Eel = Etot_t - thermal - EP; + + // --- sigma = L : Eel --- + // Output is automatically VoigtType::stress. No ambiguity. + tensor2 sigma = L * Eel; + + // --- Yield function --- + double Hp = (p > 1e-12) ? k * std::pow(p, m) : 0.0; + double dHpdp = (p > 1e-12) ? m * k * std::pow(p, m - 1) : 0.0; + + double phi = Mises(sigma) - Hp - sigmaY; + + // --- Return mapping (CCP loop) --- + tensor2 sigma_start = sigma; + tensor2 EP_start = EP; + + if (phi > 0.0) { + // Flow direction: dev(sigma) / ||dev(sigma)|| + // This is a stress-like quantity — dev() returns stress Voigt + vec::fixed<6> n_v = dev(sigma); + double norm_n = arma::norm(n_v); + if (norm_n > 1e-14) n_v /= norm_n; + + // Flow direction as stress tensor + tensor2 N = tensor2::from_voigt(n_v, VoigtType::stress); + + // kappa = L : N → gives stress (stiffness contracts stress-normal → stress) + tensor2 kappa = L.contract( + tensor2::from_voigt(n_v, VoigtType::strain) // n as strain for L:n + ); + + // Newton iterations on the plastic multiplier dp + double dp = 0.0; + for (int iter = 0; iter < 25; ++iter) { + Hp = (p + dp > 1e-12) ? k * std::pow(p + dp, m) : 0.0; + dHpdp = (p + dp > 1e-12) ? m * k * std::pow(p + dp, m - 1) : 0.0; + + phi = Mises(sigma) - Hp - sigmaY; + if (std::abs(phi) < 1e-10) break; + + double denom = arma::dot(vec(dev(sigma)) / Mises(sigma), vec(kappa.voigt())) + dHpdp; + double ddp = phi / denom; + dp += ddp; + + // Update plastic strain and stress + // EP += ddp * N — both are strain-tagged, arithmetic preserves tag + EP = EP + tensor2::from_voigt(vec::fixed<6>(n_v * ddp), VoigtType::strain); + + Eel = Etot_t - thermal - EP; + sigma = L * Eel; + } + p += dp; + } + + // --- Tangent modulus --- + if (solver_type == 0 || solver_type == 2) { + if (phi > -1e-10 && Mises(sigma) > 1e-10) { + // Elastoplastic tangent + vec::fixed<6> n_v = dev(sigma); + double norm_n = arma::norm(n_v); + if (norm_n > 1e-14) n_v /= norm_n; + + // kappa = L : n (as strain) + vec::fixed<6> kappa_v = L.mat() * n_v; + + // Denominator: n : L : n + dH/dp + double denom = arma::dot(n_v, kappa_v) + dHpdp; + + // Lt = L - (kappa * kappa^T) / denom + // Type is preserved: stiffness - stiffness = stiffness + tensor4 correction( + mat::fixed<6,6>(kappa_v * kappa_v.t() / denom), + Tensor4Type::stiffness + ); + tensor4 Lt = L - correction; + Lt_out = Lt.mat(); + } else { + Lt_out = L.mat(); + } + } else if (solver_type == 1) { + sigma_in = -L.mat() * EP.voigt(); + } + + // --- Export --- + sigma_out = sigma.voigt(); + L_out = L.mat(); + + statev(0) = T_init; + statev(1) = p; + vec::fixed<6> ep_v = EP.voigt(); + for (int i = 0; i < 6; ++i) statev(2 + i) = ep_v(i); +} + + +// ============================================================================ +// Neo-Hookean (finite strain) — 5 lines of mechanics +// ============================================================================ +// +// BEFORE: 15 lines of FTensor boilerplate + manual /J everywhere +// AFTER: push_forward(F) includes the 1/J metric by default + +void umat_neohookean_typed( + const mat::fixed<3,3> &F, + vec &sigma_out, mat &c_out, + const vec &props) +{ + double mu = props(0) / (2.0 * (1.0 + props(1))); + double lam = props(0) * props(1) / ((1.0 + props(1)) * (1.0 - 2.0 * props(1))); + double J = arma::det(F); + + // --- 5 lines: stress + tangent --- + tensor2 I = tensor2::identity(VoigtType::stress); + tensor2 b = stress(F * F.t()); + tensor2 sigma = (mu * (b - I) + (lam * std::log(J)) * I) * (1.0 / J); + + tensor4 C_ref = lam * tensor4::volumetric() + 2.0 * mu * tensor4::identity(); + c_out = C_ref.push_forward(F).mat(); // metric=true: includes 1/J + + sigma_out = sigma.voigt(); +} + + +// ============================================================================ +// Rotated anisotropic elasticity — rotation dispatch is automatic +// ============================================================================ +// +// BEFORE: you must remember QS for stiffness, QE for compliance +// AFTER: .rotate(R) picks the right rule from the type tag + +void umat_elastic_aniso_typed( + const vec &Etot, const vec &DEtot, + vec &sigma_out, mat &Lt_out, mat &L_out, + const vec &props, const double &theta_deg) +{ + tensor4 L_mat(L_isotrans(props(0), props(1), props(2), props(3), props(4), 1), + Tensor4Type::stiffness); + + Rotation R = Rotation::from_axis_angle(theta_deg * datum::pi / 180.0, 3); + tensor4 L_glob = L_mat.rotate(R); // dispatches to QS * L * QS^T + + // compliance rotation uses QE automatically — no manual dispatch + tensor4 M_glob = L_mat.inverse().rotate(R); // QE * M * QE^T + + tensor2 sigma = L_glob * strain(Etot + DEtot)); + sigma_out = sigma.voigt(); + Lt_out = L_out = L_glob.mat(); +} diff --git a/src/Continuum_mechanics/Umat/Thermomechanical/Viscoelasticity/Zener_fast.cpp b/src/Continuum_mechanics/Umat/Thermomechanical/Viscoelasticity/Zener_fast.cpp index c9c093415..401c23741 100755 --- a/src/Continuum_mechanics/Umat/Thermomechanical/Viscoelasticity/Zener_fast.cpp +++ b/src/Continuum_mechanics/Umat/Thermomechanical/Viscoelasticity/Zener_fast.cpp @@ -249,7 +249,7 @@ void umat_zener_fast_T(const vec &Etot, const vec &DEtot, vec &sigma, double &r, double N_theta = 0.; vec A_v = L1*EV1; - vec dA_dEv = L1; + mat dA_dEv = L1; if(DTime < 1.E-12) { r = 0.;