Shock Buffet Feature Extraction and Onset Prediction Based on Neural Network
Shock buffet is a self-excited oscillation phenomenon caused by shock wave-boundary layer interference,which may lead to structural fatigue failure and even cause flight safety issues.The accurate prediction of shock buffet onset boundary is of great engineering significance for the design of transport aircraft.This paper establishes a Characteristics-integrated Fully connected Neural Network(CFNN)model that incorporates features from steady flow field,achieving accurate prediction of shock buffet onset angle of attack.Taking the NACA0012 airfoil as the research object,a Convolutional Neural Network(CNN)model extracts features from the steady flow field before and after the onset of the shock buffet.Subsequently,the extracted low dimensional features are used as hidden layers in the Fully connected Neural Network(FNN)model to predict the onset angle of attack of the shock buffet.In the generalization prediction of higher Mach numbers,the average relative error of the shock buffet onset angle of attack predicted by the CFNN model is reduced by more than 70%compared to the fully connected Neural Network(NN)model without incorporating features.The research results indicate that the low dimensional features extracted from the steady flow field can assist in predicting the onset angle of attack for unsteady shock buffet problems and improve the performance of neural network models.
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