南京航空航天大学学报2024，Vol.56Issue(4)：639-649,11.DOI:10.16356/j.1005-2615.2024.04.006
基于注意力机制的火箭涡轮泵支承刚度辨识
An Attention Mechanism-Based Support Stiffness Prediction for Rocket Turbopumps
摘要
Abstract
As an important dynamics parameter,stiffness has a key role in turbopump vibration reduction.Therefore,a prediction model that incorporates attention mechanism and bi-directional long short-term memory(BiLSTM)neural network is proposed.Vibration information is input and the time-related historical features are effectively extracted by the LSTM network.Subsequently,the BiLSTM network is built by the inverse superposition of the two-layer LSTM network.This is to accommodate the complex and lengthy sequences of dynamics information,and thus the nonlinear features between parameters are extracted.The weights of the features are obtained by introducing the Attention layer,which will enhance the key information.Finally,the prediction model is trained with turbopump dynamics data.The results show that for turbopump stiffness characteristics,the Attention-BiLSTM model has a significant advantage in sequence data processing,with a mean absolute percentage error(MAPE)of 2.194 5%.In contrast,the MAPEs of RNN,LSTM,and BiLSTM models are 10.497 7%,5.497 3%,and 2.798 6%,respectively.It can be seen that the method effectively avoids the complex dynamical inverse problem solving and achieves the dynamic identification of nonlinear parameters.关键词
液体火箭发动机/涡轮泵/支承刚度/长短期记忆网络/注意力机制Key words
liquid rocket engine/turbopump/support stiffness/long short-term memory network/attention mechanism分类
航空航天引用本文复制引用
苏越,许开富,金路,王伟,侯理臻..基于注意力机制的火箭涡轮泵支承刚度辨识[J].南京航空航天大学学报,2024,56(4):639-649,11.基金项目
中央高校基本科研业务费(D5000210486). （D5000210486）
