New Design Method of Solid Propellant Grain Using Machine Learning

Seok-Hwan Oh,Hyoung Jin Lee,Tae-Seong Roh

doi:10.3390/pr9060910

Abstract

The correlation between solid propellant grain configuration and burning surface area profile is a complicated nonlinear problem. Nonlinear optimization has been adopted to design grain configurations that satisfied the objective area profiles. However, as conventional design methods are impractical, with limited performance, it is necessary to investigate alternatives. Useful information for grain design can be obtained by analyzing the aforementioned correlation. However, this aspect has not been studied owing to the requirement of large amounts of data and analysis techniques. In this study, machine learning was used to develop a new design method. The objective of machine learning was to train a model to classify classes of data. The database stores various sets of configuration variables and their classes. The proposed Gaussian kernel-based support vector machine model predicts the class of newly designed grains. The results verified that the model accurately predicted the class of the set of configuration variables and can be used to modify the set of configuration variables to satisfy the requirement. Thus, it was confirmed that machine learning is an appropriate approach to grain design; however, further research is needed to analyze its practicality.

Highlights

Grain design involves the process of obtaining a set of configuration variables that can satisfy the desired burning surface area profile with burning time, which is referred to as burn-back analysis
This study developed a new grain design method and confirmed its feasibility
The analysis results were defined as class 1 if it satisfied the requirement and class −1 if it did not

Summary

Introduction

Grain design involves the process of obtaining a set of configuration variables that can satisfy the desired burning surface area profile with burning time, which is referred to as burn-back analysis. Sufficient design experience is required and a redesign, based on the existing data, is necessary to modify the configuration variables to obtain the area profile [2]. Because the correlation between the configuration variable and the area profile is not clearly identified, it is difficult to modify the configuration variable; this significantly impedes solid propellant grain design. The deterministic method has good search performance for the optimum solution but converges on the local solution. The stochastic method exhibits high performance for global solutions but has difficulty converging to an optimum solution.

Objectives

Methods

Results

Conclusion