Rapid optimization method of solid rocket motor propellant with structural strength

Abstract

To design a solid rocket motor with a high mass ratio, it is necessary to find out the parameters of the grain shape to meet the structural strength under volume loading fraction and initial burning areas conditions. A rapid optimization method for complex grain of solid rocket motors based on parametric modelling and GA‐BP is proposed. Through the sensitivity analysis of the geometric parameters related to the grain, the sensitive parameters are determined. 30 groups of parameters are determined by the mixed horizontal orthogonal test and the learning samples of the neural network are obtained by FEA. The artificial neural network is established based on the FEA results, and the accuracy of the network is verified by 10 groups of test data sampled by the LHS method. The comparison results show that the prediction error of the maximum Von Mises strain is 5.09 %, the prediction error of the volume loading fraction is 0.16 %, and the prediction error of the initial burning surface is 0.865 %. Based on this, the neural network prediction results are optimized as a function of fitness in GA, and the optimization results show that the maximum strain is reduced by 20.41 % while the constraints are satisfied.