Fast Inverse Design of Transonic Airfoils by Combining Deep Learning and Efficient Global Optimization

In this paper, a deep learning model trained to generate well-posed pressure distributions at transonic speeds is coupled by the efficient global optimization (EGO) algorithm to speed up the inverse design process for transonic airfoils. First, the Wasserstein generative adversarial network (WGAN) is trained to generate well-posed pressure distributions at transonic speeds. Then, the EGO algorithm is used to pick up a pressure distribution in WGAN by solving the associated optimization problem defined for matching the prescribed pressure features, such as the suction peak and the shock-wave position. Finally, a deep convolutional neural network (DCNN) for nonlinear mapping is adopted to obtain the corresponding airfoil shape. Several cases with prescribed pressure features were performed to verify the feasibility and efficiency of the proposed method. Test cases indicate that the airfoil shape with the desired pressure distribution can be found in around one minute using a desktop computer with an Intel i5-9300H CPU.