Artemis Simulation
Project Information:
During my capstone/internship, I engineered a Monte Carlo–based risk modeling system in Python to simulate astronaut fall rates during NASA EVA (extravehicular activity) operations. The system executed 10,000+ randomized EVA trajectories incorporating stochastic perturbations, astronaut biomechanics parameters, and environmental variability.
I implemented a modular simulation architecture using NumPy, Pandas, SciPy, and vectorized physics functions, enabling efficient sampling of multi-variable uncertainty spaces. Scenario parameters—such as center-of-mass drift, reaction force instability, and equipment interference—were parameterized and stress-tested through probabilistic sampling distributions.
To support analysis:
- I developed a full data-processing and visualization pipeline using Matplotlib/Seaborn to generate risk surfaces, fall-probability histograms, sensitivity plots, and statistical confidence intervals.
- I validated model outputs by cross-checking stability thresholds and scenario convergence metrics in Excel, ensuring alignment with theoretical expectations.
- As part of the engineering workflow, I collaborated with teammates to refine EVA assumptions, conduct model verification, and produce a technical report summarizing methodology, simulation architecture, and quantitative risk outcomes.
The project showcased the integration of probabilistic modeling, scientific computing, and aerospace operations analysis in a real engineering environment.
