03.4 - Applied Aerodynamics
DYNAMIC STALL PREDICTION THROUGH COMBINING PHYSICAL MODELS AND MACHINE LEARNING
W. Zhang¹, X. Wang¹, J. Kou¹, Z. Liu, The China Aerodynamics Research and Development Center, China; ¹ Northwestern Polytechnical University, China
We propose new multi-fidelity modeling framework to improve the accuracy and efficiency for dynamic stall prediction. The framework combines the linear dynamic derivative model and machine learning, achieving higher accuracy under sparse experimental states. We validate the framework using wind tunnel data for the pitching motions of NASA Common Research Model (CRM) at high angles of attack.