Stress-release boot can effectively improve the structural integrity of SRM (solid rocket motor), but it will also influence the loading fraction and interior ballistic performance, so the purpose of this paper is to propose a multiobjective optimization method for stress-release boot. The design variables are the front and rear depth of the stress-release boot, and four optimization variables were determined according to the analysis of SRM performance. To optimize a SRM with star and finocyl grain, the RBF (radial basis functions) model that satisfies the accuracy requirements was established based on parametric modeling technology and the OPLHS (Optimal Latin Hypercube Sampling) method. Subsequently, the Pareto front was obtained based on the NCGA-II algorithm. And an optimal solution was obtained based on the evolutionary algorithm and weighted method. Compared with the initial SRM, the maximum Von Mises strain of the grain, the maximum principal stress of the insulator/cladding interface, the maximum axial displacement, and the volume increment decreased by 19.92%, 35.33%, 4.80%, and 4.42%, respectively. The optimization design method proposed in this paper has significant advantages in computational efficiency for the optimization of SRM and can take into account various performances of SRM, which not only is suitable for the optimization design of stress-release boot but also provides guidance for the optimization design of other shape parameters of SRM.

To improve the performance of a solid rocket motor (SRM), a multiobjective optimal design method that can consider the structural integrity, internal ballistic performance, and loading performance of the SRM was proposed based on parametric modeling and surrogate modeling technology. Firstly, the parametric modeling technology was introduced into the field of structural integrity analysis for a high-loading SRM, based on which the influences of load and geometric parameters on the maximum von Mises strain of the SRM grain were analyzed, which effectively improved the sampling speed and prediction accuracy of the surrogate model. Combining the calculation models of the combustion surface area and volume loading fraction of the SRM, the Pareto optimal solution set was obtained based on the NSGA-II algorithm. Under the constraints of the optimization model, the maximum von Mises strain can be reduced by up to 26.72% and the volume loading fraction can be increased by up to 1.83% compared with the original. In addition, the optimal design method proposed in this paper is significantly superior in efficiency, capable of reducing both the single sampling time by more than 95% and the number of numerical simulations from 20,000 to 400, and the average prediction deviation is only 1.87%.