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

Abstract

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.