Implementation of a genetic algorithm for the prediction of dynamic rocket motor properties with experimental validation

The design of solid rocket motors (SRMs) involves complex trade-offs between performance, structural integrity, and safety. This study presents an open-source computational framework that couples the openMotor simulation environment with a genetic algorithm (GA) to automate SRM geometry optimization. The framework enables exploration of multidimensional design spaces defined by user-specified constraints, targeting improved total impulse and thrust-time characteristics while maintaining structural safety margins. The algorithm evaluates simulation outputs using a weighted normalized penalty function. To validate the optimization results, two SRM prototypes—a baseline and an optimized design—were fabricated and tested on a horizontal static test stand. The optimized motor achieved an experimentally measured specific impulse corresponding to 89% of the simulated prediction. The results demonstrate that coupling open-source simulation with heuristic optimization produces realistic designs and reduces manual tuning. This framework establishes a foundation for adaptive optimization of SRMs using experimental feedback and open-source computational tools. • An open-source framework integrating openMotor with a genetic algorithm for automated SRM geometry optimization. • Demonstrated reduction of design–test cycles by coupling simulation and optimization with experimental calibration. • Experimental validation demonstrated agreement between optimized motor performance and simulation predictions.