New Design Method of Solid Propellant Grain Using Machine Learning

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...

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Bibliographic Details
Published in:Processes 2021, Vol.9 (6), p.910
Main Authors: Oh, Seok-Hwan, Lee, Hyoung Jin, Roh, Tae-Seong
Format: Article
Language:English
Subjects:
Burning
Configuration management
Design optimization
Design techniques
Grain design
Learning algorithms
Machine learning
Methods
Numerical analysis
Optimization techniques
Propellant grains
Solid propellants
Standard deviation
Support vector machines
Variables
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Summary: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.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr9060910