北京航空航天大学学报2026，Vol.52Issue(5)：1456-1466,11.DOI:10.13700/j.bh.1001-5965.2024.0182
基于深度学习的固体姿轨控发动机性能预示方法
A prediction method for solid divert and attitude control motor performance based on deep learning
杨慧欣 1王旭 1李响2
作者信息
- 1. 沈阳航空航天大学航空宇航学院,沈阳 110136
- 2. 西安交通大学现代设计及转子轴承系统教育部重点实验室,西安 710049
- 折叠
摘要
Abstract
A feature visualization convolutional neural network-based surrogate model is suggested to achieve the quick prediction of solid divert and attitude control motor performance and assess the influence of various parameters.After obtaining a number of simulation datasets for training from numerical simulation modeling of the solid divert and attitude control motor,a feature visualization convolutional neural network simulation surrogate model with fast inference characteristics is established,which can realize fast performance prediction.The results show that for pressure and thrust of the solid divert and attitude control motor,the model prediction error is 0.2%and 6%,respectively.For multi-parameters input problem of the solid divert and attitude control motor,the proposed method can identify the influence of parameters on performance.Pressure changes have a significant impact on the prediction results for solid divert and attitude control motor gas valve pressure during operation;for gas valve thrust,the pintle stroke has a greater impact on the thrust prediction result when the valve opening is less than 3.5 mm,while the inlet pressure has a greater impact when the valve opening is greater than 3.5 mm.The method has good generalizability and can directly evaluate the key parameters at the algorithmic level,while eliminating the cumbersome comparison of multiple sets of simulation experiments,which can greatly improve the research efficiency.关键词
固体姿轨控发动机/代理模型/性能预示/深度学习/特征可视化Key words
solid divert and attitude control motor/surrogate model/performance prediction/deep learning/feature visualization分类
航空航天引用本文复制引用
杨慧欣,王旭,李响..基于深度学习的固体姿轨控发动机性能预示方法[J].北京航空航天大学学报,2026,52(5):1456-1466,11.
