Multifidelity aerodynamic shape optimization for airfoil dynamic stall mitigation using manifold mapping

Aerodynamic shape optimization for dynamic stall mitigation is computationally challenging, requiring multiple costly CFD evaluations. This work proposes a multifidelity modeling technique to efficiently mitigate dynamic stall over an airfoil, particularly manifold mapping (MM) within a trust-region-based optimization framework to find an optimal shape defined by six PARSEC parameters. The high-fidelity (HF) responses are modeled using the unsteady Reynolds-averaged Navier-Stokes equations with Menter's shear stress transport turbulence model at a Reynolds number of 135,000, Mach number of 0.1, and reduced frequency of 0.05. The low-fidelity responses are acquired from the Kriging regression (KR) trained over the design space. The MM with the pattern-search algorithm is utilized to find optimal designs in two different test cases. The result yields two distinct optimal shapes with a higher thickness, larger leading-edge radius, and more aft camber than the baseline airfoil (NACA 0012), indicating the presence of multiple valid designs. Both designs delay the dynamic stall angle over 3 deg while mitigating peak lift and pitching moment magnitudes. The current approach delivers a computational saving of over 70% compared to HF -KR and Cokriging regression techniques. These findings present an efficient multifidelity strategy with potential application to other computationally expensive optimization problems.