Monte Carlo Aircraft Noise Simulation Dashboard.
Built a MATLAB-based Monte Carlo workflow and App Designer dashboard to model EPNL noise variation across flight, engine, and environmental parameters.
Built a probabilistic aeroacoustic simulation tool to quantify uncertainty in aircraft noise predictions.
During my co-op at Pratt & Whitney Canada, I developed a MATLAB Monte Carlo simulation workflow that converted deterministic aircraft-noise inputs into probabilistic input distributions. The tool repeatedly sampled flight, engine, and environmental parameters, ran each case through an existing aeroacoustic model, and summarized the resulting EPNL variation using percentile-based uncertainty bands.
I also built an App Designer interface around the workflow so engineers could configure active uncertainty parameters, run large simulation batches, view statistical outputs, and export plots without manually editing input files or scripts.
Public technical summary only. Proprietary model details, datasets, and internal validation methods are excluded.
- 01Built a MATLAB Monte Carlo workflow that sampled flight, engine, and environmental parameters and ran each case through an existing aeroacoustic model to produce probabilistic EPNL distributions.
- 02Wrote a percentile-based uncertainty summary that converted thousands of raw simulation runs into bounded EPNL bands engineers could compare against deterministic baselines.
- 03Built an App Designer interface so engineers could toggle active uncertainty parameters, launch large simulation batches, inspect statistical outputs, and export plots without editing scripts.