Accelerating High-Fidelity Airfoil Design via Physics-Informed Video Diffusion Model

Airfoil design remains a challenging multi-objective optimization problem requiring precise geometric control for optimal aerodynamic performance. Data-driven inverse design methods mitigate this complexity but often yield non-physical results when physical constraints are not explicitly incorporated. Here, we construct a high-fidelity NACA-Nek1000 datasets with 53 400 samples from direct numerical simulations to train a dual physics-informed video diffusion model (PVDM). The governing equations of incompressible flow are intrinsically coupled with the diffusion learning process, enabling the reconstruction of full-field velocity and pressure distributions from lift-to-drag response sequences. The PVDM achieves accurate low-Reynolds number airfoil reconstruction and real-time flow generation within 30 s per case, approximately 600 times faster than conventional simulations, while maintaining over 90% fidelity. The reconstructed airfoils are further modeled as propeller geometries and experimentally validated, which well fit the simulated lift-to-drag response, establishing a rapid and generalizable framework for high-fidelity, physics-informed inverse aerodynamic design.