Abstract
In order 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 condition. A rapid optimization method for a complex grain of solid rocket motors based on parametric modeling and GA-BP is proposed. 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 Von Mises strain is reduced by 9.8% while the constraints are satisfied.
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