Deep learning based multistage method for inverse design of supercritical airfoil

In the preliminary aerodynamic design phase of transonic wing, inverse design of supercritical airfoils plays an important role in acquiring practical results. However, traditional inverse design approach in transonic regime highly dependents on the manual design experience and physical model simulation, which is time consuming and poses a challenge for its applications in engineering problems. In order to relieve these drawbacks, a novel method is proposed for inverse design of supercritical airfoils based on Wasserstein generative adversarial network (WGAN), genetic algorithm (GA) and deep convolutional neural network (DCNN). This method consists of three stages. First, design characteristics of transonic pressure coefficient (CP) distribution in physics are well captured and imitated by WGAN. Then, GA is used to select task-oriented CP via adjusting controllable latent variables in generator of WGAN. Next, physical-existing CP selected by GA is recovered to corresponding airfoil by DCNN with high efficiency and accuracy. Cases using the dataset sampled based on RAE2822 airfoil are carried out to show effectiveness of submodules involved. Finally, design performance of proposed multistage method is further verified by specific design goals. Test cases indicate that selected CP by GA according to the aerodynamic design criteria can be accurately reflected on inverse designed airfoils, which are confirmed by well agreements between high-fidelity simulation result and selected CP. The proposed method is a promising design approach that can quickly achieve optimal airfoils without significant loss of accuracy.