Fast flow prediction of airfoil dynamic stall based on Fourier neural operator

Abstract

Dynamic stall on airfoil is of great importance in engineering applications. In the present work, Fourier neural operator (FNO) is applied to predict flow fields during the dynamic stall process of the NACA0012 airfoil. Two cases with different angles of attack are simulated by Reynolds averaged numerical simulation with the Spalart–Allmaras (SA) model at Re=4×104. A prediction model is directly constructed between the flow fields at several previous time nodes and that at the future time node by FNO. The prediction of sequence flow fields based on the iterative prediction strategy is achieved for the dynamic stall. The results show that FNO can achieve a fast and accurate prediction of streamwise velocity, normal velocity, pressure, and vorticity for both cases. The dynamics of vortices around the airfoil is analyzed to demonstrate the prediction accuracy of FNO. In addition, FNOs with different configurations are tested to achieve a lower error and a shorter training time-consuming.