A deep learning approach for the transonic flow field predictions around airfoils

Learning from data offers new opportunities for developing computational methods in research fields, such as fluid dynamics, which constantly accumulate a large amount of data. This study presents a deep learning approach for the transonic flow field predictions around airfoils. The physics of transonic flow is integrated into the neural network model by utilizing Reynolds-averaged Navier–Stokes (RANS) simulations. A detailed investigation on the performance of the model is made both qualitatively and quantitatively. The flow features associated with the transonic effects and the angle of attack variation, such as the shock waves and the flow separation, are well predicted. Furthermore, predicted flowfield data are used to compute the aerodynamic coefficients. The findings indicate that the presented model may allow avoiding time-consuming computational fluid dynamics (CFD) simulations, especially in the design optimization studies with a slight loss of accuracy. •Providing CNN-based airfoil predictions for transonic flows.•Providing CNN-based airfoil predictions, including flows with shocks.•Providing CNN-based airfoil predictions involving stall conditions.•Each angle of attack cases is considered as a new shape in the flow field.