基于LSTM的超临界机翼抖振边界预测方法OA北大核心CSTPCD

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.