OpenRocket Monte Carlo

Overview

This project supports Monte Carlo style runs around an OpenRocket based flight model to study how uncertainty affects outcomes. Typical inputs include variability in mass properties, aerodynamic parameters, launch conditions, and wind profiles. Typical outputs include distributions of apogee and other flight metrics.

OpenRocket Monte Carlo workflow landing interface

Artifact classification

Category: Academic artifact
Skill themes: Technical and analytical growth, uncertainty quantification

Repo link

https://github.com/NCSU-High-Powered-Rocketry-Club/OpenRocket-Monte-Carlo

Highlights

Runs many randomized or swept simulations to estimate dispersion rather than a single nominal case
Organizes input uncertainty sources like wind, mass, launch angle, and model parameters
Produces structured outputs suitable for plots and reports
Supports repeatable experiments through configuration and seeded randomness
Enables comparisons across design options by keeping the workflow consistent

My Contribution

Utlized base code from the University of Waterloo, and created a simulation wrapper around it
Added many more features to vary due to issues seen with my Club’s past launches. THey range from varying wind speed, turbulance, and mass to Cd and simulting wind gusts.
Overhauled the landing dispersion with a 6DOF model that is translated from Python

Reflection Prompt

In this Project I learned how to code with a vision in mind and carry out the ideas I wanted to explore. My club has had issues with the reliability of simulations from prior years so I took it upon myself to rectify these. I took some exsisting, trusted code base and modified it to fit our clubs needs. Now it has proven its worth with this prior VDF launch where we were able to predict the apogee and landing location within a 2 sigma confidence.

Technical Highlights

Uncertainty modeling with configurable distributions and bounds
Batch execution across many trials
Output aggregation and post processing hooks
Reproducibility features such as fixed seeds and saved configs

Disturbance configuration view for Monte Carlo inputs

How to Run

Start here in the repository

Read the repo README
Install the required runtime listed there
Run a small trial count first to validate your environment
Increase trial count once outputs look correct

Results and Outputs

Typical outputs for a rocketry Monte Carlo workflow include

Apogee distribution plots and summary statistics
Time series or event metrics per run such as burnout, deployment events, and max velocity
Sensitivity views such as correlation of apogee to input uncertainties
Exported CSV or JSON artifacts for report generation

Monte Carlo Slideshow

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