Study of the Performance/Cost Bi-Objective Optimization Problem for Solid Rocket Motors

Historically, in the initial stages of solid rocket motor (SRM) development, performance parameters, such as specific impulse, total impulse, mass, and thrust, have been prioritized, with cost considerations often treated as secondary. Consequently, SRM performance optimization under cost constraints has emerged as a central objective in aerospace propulsion. To address this gap, this study establishes a cost–performance evaluation model for SRMs. A Kriging surrogate model, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are leveraged to minimize the manufacturing cost and maximize the terminal velocity of SRM engines, subject to constraints associated with the maximum operating pressure of the combustion chamber and burn time. First, a cost–performance calculation model for an SRM is developed and validated. Subsequently, Pearson correlation analysis and Sobol-based global sensitivity analysis are combined to reduce the dimensionality of the design parameters, and optimal Latin hypercube sampling is used to generate the training samples. Building on this foundation, a Kriging surrogate model is constructed. The cost–performance model of the SRM is subjected to multi-objective optimization using NSGA-II and TOPSIS to support decision-making. The results indicate that the proposed cost–performance calculation model achieves an error below 5%, demonstrating high accuracy. Among the design parameters, the combustion chamber length, nozzle outlet area, and expansion ratio significantly influence the cost and performance of SRMs. The surrogate models exhibit strong predictive accuracy, with coefficients of determination exceeding 0.9. The optimized TOPSIS scheme yields a performance improvement of 10.94% with a cost increase of 4.15% compared with the reference scheme. In summary, the cost–performance evaluation and optimization framework established in this work provides quantitative decision support for SRM design under cost constraints, and the integrated methodology can be extended to other aerospace propulsion systems or complex engineering equipment. This contributes to achieving synergistic optimization of performance and cost under resource limitations, and offers practical guidance for advancing affordability-driven design in propulsion engineering.