A new dynamic stall prediction framework based on symbiosis of experimental and simulation data

Dynamic stall requires both accurate and efficient predictions. To model the unsteady aerodynamics of dynamic stall, a symbiosis method for dynamic stall prediction is proposed through fusing experimental data and numerical simulations based on computational fluid dynamics. With only a fraction of wind tunnel test data of the National Advisory Committee for Aeronautics 0012 airfoil, the proposed framework is able to predict the lift and moment coefficients of dynamic stall under different balanced angles of attacks, amplitudes, and reduced frequencies. Results indicate that compared with the Unsteady Reynolds-Averaged Navier–Stokes simulation, the proposed model reduces the prediction error about two to five times. In addition, a posteriori analysis shows that with efficient hyperparameter optimization, the framework can separate the dynamics for attached and separated flows adaptively. The proposed data fusion model provides a way to combine the physics of the dynamic stall phenomenon to prediction models for the aerodynamic loading at high angles of attack.