An OpenMETA model for the conceptual design of high-powered rocket for the NASA Student Launch Competition
Check out our blog for the full report on this project.
OpenRocket and all Python scripts that interact with it are licensed under the GNU GPL. For license information see the file LICENSE.TXT.
- Summary
- NASA Student Launch
- OpenRocket
- Setup
- Getting Started with the OpenMETA Rocket Model
- Viewing ORK Files in OpenRocket
- Future Plans
- Further Documentation
- Helpful Links
Using the NASA Student Launch competition as a case study, OpenRocket was integrated into the OpenMETA framework to demonstrate the strengths of a Model Based Systems Engineering (MBSE) in complex design fields. The MBSE approach allows an engineer to develop a small number of models, and continuously simulate different variations until an desirable solution is found. In this case study, 972 original rocket designs were considered. The OpenMETA project in this repo allowed us to narrow down this large design space to a single final design after several rounds of analysis.
This example assumes some knowledge of OpenMETA and the coding language Python 2.7. If this is your first time using OpenMETA, we recommend that you complete this LED Tutorial to develop a basic understanding of the tools. For help with Python 2.7, Code Academy has some great tutorials. Additionally, the official reference material is available online at Python.org.
NASA Student Launch (NSL) is a STEM outreach initiative hosted by Marshall Space Flight center in Huntsville, AL. Now in its 18th year, this exciting rocketry competition provides research-based, experiential learning for students while simultaneously producing relevant, cost-effective research for NASA. Over a period of 8 months, middle school, high school, and university student teams from about 23 states design and test high-powered rockets containing an experimental payload.
For this case study, a rocket was designed as if we were entering the 2018 collegiate-level NSL competition.
OpenRocket is an open source Java application that allows users to design and simulate model rockets before building them. It features an extensive catalog of components and materials, along with the ability to create custom components and materials. Its robust simulations provide easily extractable results including both time series data (altitude, stability, etc) and summary scalar values (maximum velocity, apogee, etc). Many NSL teams use this exact software to simulate their designs before launch day.
With just a few simple source code changes and a Java-Python bridge library called JPype, this application was integrated into the OpenMETA workflow using OpenMETA Python Components. For specific details on these source code modifications, see the OpenRocket Modifications section in the blog post. This modified source code is available on MetaMorph's OpenRocket fork.
- Download the latest version of OpenMETA from https://openmeta.metamorphsoftware.com/.
- Open the installer.
- Agree to the license terms and conditions.
- Click Install.
- Open Git Bash in your desired project directory.
- Run the following command in Git Bash:
git clone git@github.com:metamorph-inc/openmeta-rocket.git
- While in the main repo folder, click the green "Clone or download" button at the top right.
- Select Download ZIP to download the repo to your Downloads folder.
- Once the download is complete, unzip the repo to your desired project directory.
OpenRocket is Java application, so Java x86 (version 6 or later) must be installed on your machine to run simulations with it.
- Download and install the latest version of the Java SDK for Windows x86. (Java SDK 8)
Note: You must accept the license agreement and make an account to download the SDK.
- Create a new environment variable called "JAVA_HOME" with the path to your x86 Java JRE (For example: "C:\Program Files (x86)\Java\jre1.8.0_161") as its value.
Python Components are used to run OpenRocket from within OpenMETA, so the JPype library is used to allow the Python scripts to access the Java functions in OpenRocket. For more information, see the JPype documentation.
Note: The Java SDK must be installed before installing the JPype library.
- Open a Command Prompt (Run as Administrator) and navigate to this repo's JPype folder.
- Enter:
"C:\Program Files (x86)\META\bin\Python27\Scripts\python.exe" -m pip install jpypex-0.5.4.2-cp27-cp27m-win32.whl -t "C:\Program Files (x86)\META\bin\Python27\Lib\site-packages" - To test installation, run
"C:\Program Files (x86)\META\bin\Python27\Scripts\python.exe" jpype_test.py
Once installation is complete, the JPype folder can be deleted from your machine.
- Open the openmeta-rocket folder.
- Double-click on the openmeta-rocket.xme file.
- GME will open and display a pop-up Import to new project window. Select Create project file and click Next >.
- The Save As window will open. Click Save to save openmeta-rocket.mga inside the openmeta-rocket folder.
- Within GME, to your right, there should be a GME Browser window with a single RootFolder object inside. Click on the + to expand the root folder.
- Left-click on the + next to Testing to expand the testing folder.
- Left-click on the + next to ParametricExploration to expand the parametric exploration folder.
- You should now see a number of PETs.
.
- Within the GME Browser window, double-click on OR_PET to open it.
- Left-click the CyPhy Master Interpreter button located on the top toolbar.
- The CyPhy Master Interpreter window will open. On the right side of this window, there is a list under the heading Please select configurations. These are the 972 discrete rocket designs generated by OR_PETs's Testbench. Select any one of these configurations for testing.
- Make sure Post to META Job Manager is checked and left-click OK.
- The Results Browser window will open. The running PET will be listed under the Active Jobs tab. Yellow highlighting means that the job has been queued, blue means the Master Interpreter is currently running the job, red means the Master Interpreter failed, and green means that the Master Interpreter succeeded.
- Once the OR_PET finishes running, left-click the PET tab of the Results Browser.
- Information from the PET run will be displayed to your right within the Results Browser window.
- Left-click the Launch in Visualizer button in the bottom-right corner of the Results Browser window to view the results in the PET Visualizer.
- The Visualizer will open in a web browser window. Left-click the Explore>Single Plot tab.
- Under the Variables section, set the X-Axis to WindSpeed and the Y-Axis to Apogee. Notice the inverse trend this plot reveals between Wind Speed and the rocket's apogee.
Note: Plot markers can be modified as shown here via the Markers tab.
OR_PET creates plots of the rocket's simulated trajectory and its motor's thrust curve for each run. These plots are saved in the results folder and can be viewed in the Visualizer.
- With OR_PET open in the Visualzer, click on the Explore>Point Details tab.
- This tab shows the Objective and Design Variable values for a specific PET record, along with the images saved for that record.
- Use the GUID drop-down menu to choose between PET records.
- Use the Images drop-down menu to switch between the trajectory plot and the thrust curve plot.
This PET was designed to test multiple unique rocket configurations in one PET. Its test bench includes 972 unique discrete rocket configurations to pick from, and its Parameter Study Driver covers a range of the rocket design's continuous variables. It exports several key simulation results and images of the rocket’s trajectory and thrust curve for each run.
This PET is functionally equivalent to OR_PET, but does not save images of the rocket's trajectory and thrust curve. As a result, it completes each run much faster, making this PET ideal for testing a large design space.
This PET is functionally equivalent to OR_PET_Fast, but its design space is specialized for the 2nd iteration of testing described in our blog post. (Its Test Bench is constrained to only the 256 discrete configurations that passed the first iteration of testing.)
This PET was designed to test a single rocket configuration over a range of launch conditions. Its inputs allow you to test the effect of parameters such as wind speed, launch altitude, and the launch rod length on your rocket design. Like OR_PET, it exports several key simulation results and images of the rocket’s trajectory and thrust curve for each run.
This PET is functionally equivalent to Full_Mission_PET, but does not save images of the rocket's trajectory and thrust curve. As a result, it completes each run much faster, making this PET ideal for testing a large design space.
All the Python component scripts used to build the openmeta-rocket PETs are located
in the openmeta-rocket/scripts folder, along with a guide for writing your own
OpenRocket scripts. To learn more about OpenMETA Python Components in general, see
OpenMETA's Python Wrapper documentation as well as OpenMDAO's documentation.
Once a specific rocket design is selected, it may be desirable to view it in OpenRocket.
- Download the official OpenRocket application. (The OpenRocket application in this repo does not support the GUI.)
- Open a command prompt and navigate to the directory where the OpenRocket JAR was saved.
- Enter
java -jar OpenRocket-VERSION.jarin the command prompt, replacing VERSION with the version you downloaded. - To open your rocket design, use the drop-down menus at the top to Select File > Open. Navigate to your .ork file and click Open.
For more information on the OpenRocket GUI, check out the developer's Wiki.
One large problem in the field of optimization is sampling. Currently, the design space is built so that every component is forced to have the same material and surface finish. This is a realistic simplification for rockets on our scale, but would quickly lead to failure for orbital rockets. If each component were allowed to have a different material and finish, the discrete space would allow for 8748 possible configurations. When scaled up to large rockets, the design space could explode into millions of possible combinations. But how can we efficiently sample millions of configurations? We are working on a way to sample a large, discrete design space based on the level of effect each decision has on overall system performance. The level of effect each decision has will be stored as a metric inside of the design container. When the configuration generator runs, it will follow a protocol to generate the most diverse designs by first varying the components that have the largest impact. This allows for the full range to be coarsely sampled, while keeping the total number of configurations low. Once a satisfactory performance range is determined, the process can be repeated iteratively allowing for more configurations within each refined range until a desirable design is found.
During the 2017-2018 competition year, we met with a student design team and discussed some of the problems they had while designing their rocket. They found that they had designed their rocket around a motor that had a ±20% uncertainty in the total impulse specified by the manufacturer, which led to them not being able to accurately predict how high their rocket would launch. We plan on countering this by designing a sensitivity analysis comparing motor impulse (and impulse curve) with apogee, rail clearance velocity, and other vehicle requirements.
In addition to improving the rocket model itself, changes could be implemented in OpenRocket to improve the design process in OpenMETA. The first of these would be upgrading to OpenRocket version 15.03. Currently, OpenRocket version 1.1.9 is being used, since the OpenRocket developers provided support for modifying the sources code and writing Python scripts for this version. Upgrading would involve changing the source code of version 15.03 and potentially rewriting the OpenRocket helper python library. Additionally, to enable visualization of the rocket during the design process, sketches of the rocket design could be saved along with the trajectory and thrust curve images during PETs. Such a design sketch can be accessed currently by opening a generated ork file in OpenRocket, but integrating this feature into the OpenMETA toolset would streamline the design process.
For additional information regarding the OpenMETA toolset, please consult the documentation.
Introduction
PET Tutorial
Results Browser
Visualizer