%% 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: . % % Once the Python environment is configured, RocketPy needs to installed using % |pip| as outlined in RocketPy's 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(latitude, longitude, elevation); env = rocketpy.Environment(pyargs(... '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( ... 'motor', Pro75M1670, ... '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, ... 'rail_length', 5.2, ... '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), $v_x$ (m/s), $v_y$ (m/s), $v_z$ (m/s), $q_0$, $q_1$, % $q_2$, $q_3$, $\omega_1$ (rad/s), $\omega_2$ (rad/s), $\omega_3$ (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.angle_of_attack.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!