Prediction of unsteady flow fields at high angles of attack for symmetrical airfoils using deep neural operators

Rapid prediction of unsteady flow fields around airfoils is crucial for aerodynamic design and optimization of aircraft. However, traditional wind tunnel experiments and numerical simulation methods incur high computational costs for flow field calculations. This paper proposes an improved method based on deep neural operator networks for rapidly predicting unsteady flow fields of different symmetric airfoils under high angle-of-attack conditions. The method uses incoming flow conditions and airfoil parameters as inputs, reconstructing the flow field through spatiotemporal coordinates to achieve joint prediction of pressure and velocity. Experimental results demonstrate that this method accurately captures the unsteady flow field characteristics of different symmetrical airfoils at various high angles of attack. The inference time for a single time-step case takes only about 0.6 s, which is significantly faster than traditional numerical simulation methods. The proposed method can substantially enhance computational efficiency while maintaining flow prediction accuracy, offering a viable new approach for rapid flow field prediction and aerodynamic design optimization of aircraft.