Prediction of Solid Propellant Burning Rate Characteristics Using Machine Learning Techniques

Author:

Klinger Daniel1ORCID,Casey Alex2,Manship Tim2ORCID,Son Steven2,Strachan Alejandro3

Affiliation:

1. Gambaro School of Aeronautics and Astronautics Purdue University 500 Allison Rd West Lafayette IN USA 47906

2. School of Mechanical Engineering Purdue University 500 Allison Rd West Lafayette IN USA 47906

3. School of Materials Engineering Purdue University 610 Purdue Mall West Lafayette IN USA 47907

Abstract

AbstractWhen formulating a new solid propellant, one of the most important aspects of its performance is the burning rate's response to a change in pressure. For this reason, it is useful to be able to predict the burning rate response of a given propellant before the propellant formulation is created such that experimental trade studies are minimized or reduced in scale. While many theoretical and phenomenological models have been proposed to explain various aspects of energetic material combustion, little work has been made publicly available in the application of machine learning models to predicting solid propellant burning rates. To facilitate model creation, the material formulation and burning rate parameters for over 600 publicly available propellant formulations have been collected into a coherent data set. This work utilizes the large amount of publicly available data to inform a random forest machine learning (ML) model in the prediction of solid propellant burning rate parameters. This ML model operates over a large parameter space including ammonium perchlorate composite, plastic bonded high explosive, and double‐base propellants. The model's accuracy, adaptability, and prediction capabilities are presented and discussed. The effects of different materials on a propellant's expected burning rate are examined.

Publisher

Wiley

Subject

General Chemical Engineering,General Chemistry

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