Fast and high-precision compressible flowfield inference method of transonic airfoils based on attention UNet

Abstract

Traditional numerical simulation methods for airfoil flowfields are complex and time-consuming, and deep learning-based inference methods for Reynolds-averaged Navier–Stokes equations (RANS) solutions of transonic airfoils have limitations in terms of their robustness and generalization. A novel data-driven inference method named as attention UNet (AU)-RANS is proposed for efficient and accurate prediction of flowfields around airfoils with strong compressibility and large-scale turbulent separation. First, to enhance the learning the boundary flow information and inference of the entire flowfield solution, an innovative data preprocessing method is proposed to convert the physical quantities and coordinate information of RANS solutions into neural network spatial information. Second, an attention mechanism is introduced in UNet to suppress feature responses in irrelevant background regions and enhance sensitivity to the geometrical features of the input airfoil and varying inflow conditions. The quantitative and qualitative analyses of AU-RANS inference results demonstrate that the well-trained model can effectively infer RANS solutions for airfoil flowfield and can accurately predict the shock waves and flow separation phenomena under high Mach number conditions with a large angle of attack.