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FourNetFlows, the abbreviation of Fourier Neural Network for Airfoil Flows,is an efficient model that provides quick and accurate predictions of steadyairfoil flows. We choose the Fourier Neural Operator (FNO) as the backbonearchitecture and utilize OpenFOAM to generate numerical solutions of airfoilflows for training. Our results indicate that FourNetFlows matches the accuracyof the Semi-Implicit Method for Pressure Linked Equations (SIMPLE) integratedwith the Spalart-Allmaras turbulence model, one of the numerical algorithms.FourNetFlows is also used to predict flows around an oval whose shape isdefinitely different from samples in the training set. We note that bothqualitative and quantitative results are consistent with the numerical results.Meanwhile, FourNetFlows solves thousands of solutions in seconds, orders ofmagnitude faster than the classical numerical method. Surprisingly,FourNetFlows achieves model flows with zero-shot super-resolution when it istrained under a lower resolution. And the inferring time is almost constantwhen the resolution of solutions is increasing.

FourNetFlows: An efficient model for steady airfoil flows prediction