+
-
- -
-
-
+Getting Started with RocketPy in MATLAB®
-
- In this Live Script, you will learn how to run RocketPy using the
- MATLAB® environment.
-
-
- First things first: clone/download RocketPy's repository and set it
- as your MATLAB® working directory so that you can run this live script
- without any issues.
-
-
- After that, we start by configuring our Python environment. You can
- do so by following the guidelines presented in the MATLAB®
- documentation: Configure Your System to Use Python - MATLAB® & Simulink
- (mathworks.com).
-
-
- Once the Python environment is configured, RocketPy needs to
- installed using pip as outlined in RocketPy's documentation: Installation — RocketPy documentation.
-
-
- Finally, all the prerequisites are complete and you can comeback to
- MATLAB®! We just need to set the execution mode as out of process and
- start working. MATLAB® can run Python scripts and functions in a
- separate process. Running Python in a separate process enables you
- to:
-
- -
-
- - Use some third-party libraries in the Python code that are not - compatible with MATLAB®. - -
- - Isolate the MATLAB® process from crashes in the Python code. - -
-
-
-
-
- pyenv('ExecutionMode','OutOfProcess');
-
-
- Note: if MATLAB® is not able to find Python automatically, you may
- have to run the command line above including a path to the Python
- exectuable installed on your computer:
-
-
-
-
-
-
- % pyenv('ExecutionMode','OutOfProcess', 'Version',
- '/path/to/python/executable');
-
-
- Now, we will go through a simplified rocket trajectory simulation to
- get you started. Let's start by importing the rocketpy module.
-
-
-
-
-
-
- rocketpy = py.importlib.import_module('rocketpy');
-
- Setting Up a Simulation
-
- Creating an Environment for Spaceport America
-
-
-
-
-
-
- % rocketpy.Environment(rail_length, latitude, longitude,
- elevation);
-
-
-
-
- env = rocketpy.Environment(pyargs(...
-
-
-
-
- 'rail_length', 5.2, ...
-
-
-
-
- 'latitude', 32.990254, ...
-
-
-
-
- 'longitude',-106.974998, ...
-
-
-
-
- 'elevation', 1400 ...
-
-
-
-
- ));
-
-
- To get weather data from the GFS forecast, available online, we run
- the following lines.
-
- First, we set tomorrow's date.
-
-
-
-
-
- Tomorrow = datetime('tomorrow');
-
-
-
-
- env.set_date({int32(Tomorrow.Year), int32(Tomorrow.Month),
- int32(Tomorrow.Day), int32(12)}) % Hour given in UTC time (noon UTC)
-
-
- Now, we tell our Environment object to retrieve a weather forecast
- for our specified location and date using GFS:
-
-
-
-
-
-
- env.set_atmospheric_model(pyargs( ...
-
-
-
-
- 'type', "Forecast", ...
-
-
-
-
- 'file', "GFS" ...
-
-
-
-
- ))
-
-
- We can see what the weather will look like by calling the info
- method!
-
-
-
-
-
-
- env.info()
-
-
-
-
-
- Plots will open in a separate window, so be sure to run this last
- cell to see them!
-
- Creating a Motor
-
- A solid rocket motor is used in this case. To create a motor, the
- SolidMotor class is used and the required arguments are given.
-
-
- The SolidMotor class requires the user to have a thrust curve ready.
- This can come either from a .eng file for a commercial motor, such as
- below, or a .csv file from a static test measurement.
-
-
- Besides the thrust curve, other parameters such as grain properties
- and nozzle dimensions must also be given.
-
-
-
-
-
-
- Pro75M1670 = rocketpy.SolidMotor(pyargs( ...
-
-
-
-
- 'thrust_source', "../../data/motors/Cesaroni_M1670.eng", ...
-
-
-
-
- 'burn_time', 3.9, ...
-
-
-
-
- 'grains_center_of_mass_position', -0.85704, ...
-
-
-
-
- 'grain_number', int32(5), ...
-
-
-
-
- 'grain_separation', 5 / 1000, ...
-
-
-
-
- 'grain_density', 1815, ...
-
-
-
-
- 'grain_outer_radius', 33 / 1000, ...
-
-
-
-
- 'grain_initial_inner_radius', 15 / 1000, ...
-
-
-
-
- 'grain_initial_height', 120 / 1000, ...
-
-
-
-
- 'nozzle_radius', 33 / 1000, ...
-
-
-
-
- 'throat_radius', 11 / 1000, ...
-
-
-
-
- 'interpolation_method', "linear" ...
-
-
-
-
- ));
-
-
- To see what our thrust curve looks like, along with other import
- properties, we invoke the info method yet again. You may try the
- all_info method if you want more information all at once!
-
-
-
-
-
-
- Pro75M1670.info()
-
-
-
-
-
- Plots will open in a separate window, so be sure to run this last
- cell to see them!
-
- Creating a Rocket
-
- A rocket is composed of several components. Namely, we must have a
- motor (good thing we have the Pro75M1670 ready), a couple of
- aerodynamic surfaces (nose cone, fins and tail) and parachutes (if we
- are not launching a missile).
-
-
- Let's start by initializing our rocket, named calisto, supplying it
- with the Pro75M1670 engine, entering its inertia properties, some
- dimensions and also its drag curves.
-
-
-
-
-
-
- calisto = rocketpy.Rocket(pyargs( ...
-
-
-
-
- 'radius', 127 / 2000, ...
-
-
-
-
- 'mass', 19.197 - 2.956, ...
-
-
-
-
- 'inertia_i', 6.60, ...
-
-
-
-
- 'inertia_z', 0.0351, ...
-
-
-
-
- 'power_off_drag', "../../data/calisto/powerOffDragCurve.csv", ...
-
-
-
-
- 'power_on_drag', "../../data/calisto/powerOnDragCurve.csv" ...
-
-
-
-
- 'nozzle_position', -1.255, ...
-
-
-
-
- 'coordinate_system_orientation', "nozzle_to_combustion_chamber", ...
-
-
-
-
- ));
-
-
-
-
-
- calisto.set_rail_buttons(0.2, -0.5)
-
-
-
-
-
- calisto.add_motor(Pro75M1670, position=-1.255)
-
- Adding Aerodynamic Surfaces
-
- Now we define the aerodynamic surfaces. They are really straight
- forward.
-
-
-
-
-
-
- NoseCone = rocketpy.Rocket.add_nose(pyargs( ...
-
-
-
-
- 'self', calisto, ...
-
-
-
-
- 'length', 0.55829, ...
-
-
-
-
- 'kind', "vonKarman", ...
-
-
-
-
- 'position', 1.278 ...
-
-
-
-
- ));
-
-
-
-
-
- fin_set = rocketpy.Rocket.add_trapezoidal_fins(pyargs( ...
-
-
-
-
- 'self', calisto, ...
-
-
-
-
- 'n', int32(4), ...
-
-
-
-
- 'span', 0.100, ...
-
-
-
-
- 'root_chord', 0.120, ...
-
-
-
-
- 'tip_chord', 0.040, ...
-
-
-
-
- 'position', -1.04956 ...
-
-
-
-
- ));
-
-
-
-
-
- Tail = rocketpy.Rocket.add_tail(pyargs( ...
-
-
-
-
- 'self', calisto, ...
-
-
-
-
- 'top_radius', 0.0635, ...
-
-
-
-
- 'bottom_radius', 0.0435, ...
-
-
-
-
- 'length', 0.060, ...
-
-
-
-
- 'position', -1.194656 ...
-
-
-
-
- ));
-
- Adding Parachutes
-
- Finally, we have parachutes! calisto will have two parachutes, Drogue
- and Main.
-
-
- Both parachutes are activated by some special algorithm, which is
- usually really complex and a trade secret. Most algorithms are based
- on pressure sampling only, while some also use acceleration
- info.
-
-
- RocketPy allows you to define a trigger function which will decide
- when to activate the ejection event for each parachute. This trigger
- function is supplied with pressure measurement at a predefined
- sampling rate. This pressure signal is usually noisy, so artificial
- noise parameters can be given. Call
-
-
-
-
-
-
-
- % py.help(rocketpy.Rocket.add_parachute)
-
-
- for more details. Furthermore, the trigger function also receives the
- complete state vector of the rocket, allowing us to use velocity,
- acceleration or even attitude to decide when the parachute event
- should be triggered.
-
-
- Here, we define our trigger functions rather simply using Python.
- Unfortunately, defining these with MATLAB® code is not yet
- possible.
-
-
-
-
-
-
- % Drogue parachute is triggered when vertical velocity is
- negative, i.e. rocket is falling past apogee
-
-
-
-
- drogue_trigger = py.eval("lambda p, y: y[5] < 0", py.dict);
-
-
-
-
-
- % Main parachute is triggered when vertical velocity is
- negative and altitude is below 800 AGL
-
-
-
-
-
- main_trigger = py.eval("lambda p, y: (y[5] < 0) and (y[2] < 800 + 1400)", py.dict);
-
-
- Now we add both the drogue and the main parachute to our
- rocket.
-
-
-
-
-
-
- Main = rocketpy.Rocket.add_parachute(pyargs( ...
-
-
-
-
- 'self', calisto, ...
-
-
-
-
- 'name', "Main", ...
-
-
-
-
- 'cd_s', 10.0, ...
-
-
-
-
- 'trigger', main_trigger, ...
-
-
-
-
- 'sampling_rate', 105, ...
-
-
-
-
- 'lag', 1.5, ...
-
-
-
-
- 'noise', py.tuple({0, 8.3, 0.5}) ...
-
-
-
-
- ));
-
-
-
-
-
- Drogue = rocketpy.Rocket.add_parachute(pyargs( ...
-
-
-
-
- 'self', calisto, ...
-
-
-
-
- 'name', "Drogue", ...
-
-
-
-
- 'cd_s', 1.0, ...
-
-
-
-
- 'trigger', drogue_trigger, ...
-
-
-
-
- 'sampling_rate', 105, ...
-
-
-
-
- 'lag', 1.5, ...
-
-
-
-
- 'noise', py.tuple({0, 8.3, 0.5}) ...
-
-
-
-
- ));
-
-
- Just be careful if you run this last cell multiple times! If you do
- so, your rocket will end up with lots of parachutes which activate
- together, which may cause problems during the flight simulation. We
- advise you to re-run all cells which define our rocket before running
- this, preventing unwanted old parachutes. Alternatively, you can run
- the following lines to remove parachutes.
-
-
-
-
-
-
- % calisto.parachutes.remove(Drogue)
-
-
-
-
- % calisto.parachutes.remove(Main)
-
- - Simulating a Flight -
-
- Simulating a flight trajectory is as simple as initializing a Flight
- class object givin the rocket and environnement set up above as
- inputs. The launch rail inclination and heading are also given
- here.
-
-
-
-
-
-
- test_flight = rocketpy.Flight(pyargs(...
-
-
-
-
- 'rocket', calisto, ...
-
-
-
-
- 'environment',env, ...
-
-
-
-
- 'inclination', 85, ...
-
-
-
-
- 'heading', 0 ...
-
-
-
-
- ));
-
- - Analyzing the Results -
-
- RocketPy gives you many plots, thats for sure! They are divided into
- sections to keep them organized. Alternatively, see the Flight class
- documentation to see how to get plots for specific variables only,
- instead of all of them at once.
-
-
-
-
-
-
- test_flight.all_info()
-
-
-
-
-
- Plots will open in a separate window, so be sure to run this last
- cell to see them!
-
- - Working with Data Generated by RocketPy in MATLAB® -
-
- You can access the entire trajectory solution matrix with the
- following line of code. The returned matrix contain the following
- columns: time t (s), x (m), y (m), z (m), 
(m/s), 
(m/s), 
(m/s), 
, 
, 
, 
, 
(rad/s), 
(rad/s), 
(rad/s).
-
- (m/s), (m/s), (m/s), , , , , (rad/s), (rad/s), (rad/s).
-
-
-
-
-
- solution_matrix =
- double(py.numpy.array(test_flight.solution))
-
-
-
-
-
- Support for accessing secondary values calculated during post
- processing, such as energy, mach number, and angle of attack, is also
- available for all versions of RocketPy greater than or equal to
- version 0.11.0.
-
-
- To showcase this, let's get the angle of attack of the rocket and
- plot it using MATLAB®:
-
-
-
-
-
-
- angle_of_attack =
- double(test_flight.angleOfAttack.source)
-
-
-
-
-
-
-
- plot(angle_of_attack(:,1), angle_of_attack(:,2)) % First column is time (s), second is the angle of attack
-
-
-
-
- ylabel('Angle of Attack (Deg)')
-
-
-
-
- xlabel('Time(s)')
-
-
-
-
- xlim([test_flight.out_of_rail_time, 10])
-
-
- 
-
-
-
- You can also convert data from other objects besides the Flight
- class, such as from the Environment. For example, let's say you want
- to get the wind velocity, both the x and y component:
-
-
-
-
-
-
- wind_velocity_x = double(env.wind_velocity_x.source) % First column is altitude (ASL m), while the second one is the
- speed (m/s)
-
-
-
-
-
-
-
- wind_velocity_y = double(env.wind_velocity_y.source) % First column is altitude (ASL m), while the second one is the
- speed (m/s)
-
-
-
-
- Time to Fly!
-
- This is all you need to get started using RocketPy in MATLAB®! Now it
- is time to play around and create amazing rockets. Have a great
- launch!
-
- - -