空气动力学学报2024，Vol.42Issue(6)：56-65,10.DOI:10.7638/kqdlxxb-2023.0146
基于LSTM的超临界机翼抖振边界预测方法
Buffeting boundary prediction method of supercritical wing using LSTM
摘要
Abstract
The buffeting of supercritical airfoils significantly impacts the safety and stability of transport aircraft.Efficient and accurate determination of buffeting boundaries has been a focal point of research.In this study,a prediction framework for buffeting boundaries of supercritical airfoils was developed using Long Short-Term Memory(LSTM)neural networks,focusing on the CHN-T1 transport aircraft model.Utilizing computational data from the CHN-T1 model,LSTM-based models for predicting aerodynamic coefficients and determining buffeting onset angles were designed.These models forecast changes in aerodynamic coefficients accurately at a given Mach numbers and rapidly determine buffeting onset angles at a given Mach numbers.Integration of buffeting onset angle data ultimately defined the buffeting boundaries of the CHN-T1 model,validated with wind tunnel experimental data.The results demonstrated the LSTM model's excellent predictive capabilities for aerodynamic coefficient trends,maintaining a RMSE within 2%.Furthermore,the model exhibited outstanding performance in buffeting onset angle determination,with errors remaining within 2%.These findings validate the reliability and accuracy of this approach in buffeting boundary prediction,providing robust support for research on supercritical airfoil buffeting.关键词
超临界机翼/抖振边界/气动力系数预测/长短时记忆Key words
supercritical wings/buffeting boundary/aerodynamic coefficient prediction/LSTM分类
航空航天引用本文复制引用
王紫浩,李滚,刘大伟,陈德华,张书俊..基于LSTM的超临界机翼抖振边界预测方法[J].空气动力学学报,2024,42(6):56-65,10.基金项目
旋翼空气动力学重点实验室研究开放课题(2104RAL202102-1) （2104RAL202102-1）
