Georgia Tech Experimental Rocketry · 2025-2026
GTXR: Mach 'N' Roll
I developed machine-learning fin-flutter surrogates that accelerated genetic rocket-configuration optimization.
Expanded technical stack
Overview
Our simulations team evaluates candidate configurations for a high-altitude student rocket. Fin-flutter analysis was a major bottleneck inside the genetic optimization loop.
My contribution
I developed machine-learning surrogate models for fin-flutter prediction and integrated them into a genetic rocket-configuration optimizer.
Challenge
A roughly one-minute flutter evaluation made population-scale rocket configuration search impractical, so I needed a faster way to screen candidates.
Technical approach
I trained and integrated an ML surrogate for fin-flutter prediction so the genetic optimizer could screen candidate rocket configurations without invoking the expensive full flutter evaluation each time.
Result / outcome
I reduced each candidate evaluation from approximately one minute to under one second, enabling much faster exploration of the configuration space.