A Study on the Optimal Design Method for Star-Shaped Solid Propellants through a Combination of Genetic Algorithm and Machine Learning

Abstract

This study was focused on the configuration design of a star grain by using machine learning in the optimal design process. The key to optimizing the grain design is aimed at obtaining a set of configuration variables that satisfy the requirements. The optimization problem consists of an objective area profile subject to certain constraints and an objective function that quantitatively calculates the design level. Designers must formulate suitable optimization problems to achieve an optimal design. However, because a method to alleviate the influence of the sliver section is not yet available, the optimization problem is typically solved based on experience, which is time- and effort-intensive. Consequently, a more practical and objective grain design method must be developed. In this study, an optimal design method using machine learning was developed to increase the convenience and success rate. A support vector machine was used to train a classification model that predicts a class. The classification model was used to alleviate the influence of the sliver zone and correct the search problem to ensure that an optimal solution existed in the region satisfying the requirements. The proposed method was validated through star grain optimal design using the genetic algorithm. The optimization was performed considering the area profiles, and the effectiveness of the proposed method was demonstrated by the enhanced accuracy.