推进剂
燃烧
火箭(武器)
火箭推进剂
摩尔分数
分数(化学)
人工神经网络
替代模型
计算机科学
工艺工程
热力学
化学
工程类
航空航天工程
机器学习
物理
有机化学
作者
Min Young Jung,Jae Hun Chang,Min Oh,Chang Ha Lee
标识
DOI:10.1016/j.combustflame.2023.112649
摘要
Understanding the dynamic combustion behavior of a solid propellant with reaction mechanisms is essential for designing rocket engines and safely disposing of expired propellants. In this study, a rigorous mathematical model was applied to predict the dynamic behavior of propellant combustion (HMX/GAP). Because accurate and fast prediction can contribute to saving design time and enhancing operational safety, a deep neural network surrogate model was also developed to predict propellant combustion characteristics, such as burning rate, gas phase temperature, and mole fraction. After the mathematical model with a moving boundary approach was validated with reported data, the DNN surrogate model was trained and tested with data generated through a stochastic simulation using the mathematical model. The prediction accuracies of the developed surrogate model were 96.46%, 99.72%, and 97.62% for the burning rate, gas phase temperature, and mole fraction, respectively. Compared with the dynamic simulation, the computational time of the surrogate model was 596 times faster for the prediction of the burning rate and gas phase temperature and 590 times faster for the mole fraction. The developed framework can be applied to the design of the optimal composition of solid propellants for the desired combustion behavior. It will also help in the safe incineration of expired propellants in real systems.
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