Multi-Objective Design Optimization of Solid Rocket Motors via Surrogate Modeling

Abstract

To reduce the high computational cost and lengthy design cycles of traditional solid rocket motor (SRM) development, this paper proposes an efficient surrogate-assisted multi-objective optimization approach. A comprehensive performance model was first established, integrating internal ballistics, grain structural integrity, and cost estimation, to enable holistic assessment of the coupled effects of key motor components. A parametric analysis framework was then developed to automate the model, facilitating seamless data exchange and coordination among sub-models through chain coupling. Leveraging this framework, a large-scale, high-fidelity dataset was generated via uniform sampling of the design space. The Kriging surrogate model with the highest global fitting accuracy was subsequently employed to replicate the integrated model’s complex responses and reveal underlying design principles. Finally, an enhanced NSGA-III algorithm incorporating a phased hybrid crossover operator was applied to improve global search performance and guide solution evolution along the Pareto front. Applied to a specific SRM, the proposed method achieved a 4.72% increase in total impulse and a 6.73% reduction in cost compared with the initial design, while satisfying all constraints.