基于深度学习的固体姿轨控发动机性能预示方法OA

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.