Prediction of hybrid airfoil leading edge pressure distribution based on deep learning

A prediction model on the leading edge pressure distribution of the hybrid airfoil based on deep learning was proposed. A convolutional neural network model (CNN) was established on the basis of the geometric feature extraction of the hybrid airfoil and the parameterization of the pressure distribution curve. A group of hybrid airfoils with different trailing edges were analyzed by a verified CFD method. The CFD results were used as the training set of the CNN. Results show that the goodness of fit of the calculation results of the two methods exceeds 0.98. The proposed prediction method based on deep learning takes 1.7 s and CFD method takes more than 50 s, computation time is greatly reduced. The proposed method can improve the computational efficiency with satisfying the calculation accuracy and it can be applied to the design processes of other airfoils.