THERMAL SCIENCE

International Scientific Journal

Authors of this Paper

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Jian Teng

,

Zhenlong Wu

,

Limei Lu

,

Yiqing Li

RAPID PREDICTION OF SOLID ROCKET IGNITION TRANSIENT PROCESS USING ARTIFICIAL NEURAL NETWORKS

ABSTRACT
Solid rocket motors have been a critical component of space exploration, military operations, and numerous other applications for decades. The ability to accurately predict the ignition transient behavior of solid rocket motors is crucial for ensuring safe and reliable operations. In this study, ANN are employed to predict the ignition transient process of a model solid rocket motor. The training and validation data for the ANN are obtained through simulations of a validated quasi-1-D model. Results show that with the inputs of axial co-ordinate and igniting time, the ANN can predict density, axial velocity, temperature, and pressure in internal ballistic within 0.039 relative error and a correlation coefficient above 0.994 compared to the quasi-1-D simulations in millisecond level. With the increase of hidden layers and neural numbers in the ANN, prediction accuracy increases. When the hidden layers exceed four, prediction accuracy cannot improve significantly. When test data is out of the temporal range of the training and validation data, prediction accuracy decreases evidently. The trained ANN model can be used to predict solid rocket motors with increased internal ballistic spatial resolution within 0.007 relative error and to predict solid rocket motors with increased temporal resolution within 0.107 relative error.
KEYWORDS
Solid Rocket Motor, Ignition Transient, Quasi-One Dimensional Model, machine learning, artificial neural networks
PAPER SUBMITTED: 2024-04-16
PAPER REVISED: 2024-06-20
PAPER ACCEPTED: 2024-07-02
PUBLISHED ONLINE: 2024-08-18
DOI REFERENCE: https://doi.org/10.2298/TSCI240416176T
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2025, VOLUME 29, ISSUE Issue 1, PAGES [251 - 265]
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Rapid prediction of solid rocket ignition transient process using artificial neural networks

2025 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence