Multifidelity modeling similarity conditions for airfoil dynamic stall prediction with manifold mapping

摘要

Purpose The purpose of this work is to investigate the similarity requirements for the application of multifidelity modeling (MFM) for the prediction of airfoil dynamic stall using computational fluid dynamics (CFD) simulations. Design/methodology/approach Dynamic stall is modeled using the unsteady Reynolds-averaged Navier-Stokes equations and Menter's shear stress transport turbulence model. Multifidelity models are created by varying the spatial and temporal discretizations. The effectiveness of the MFM method depends on the similarity between the high- (HF) and low-fidelity (LF) models. Their similarity is tested by computing the prediction error with respect to the HF model evaluations. The proposed approach is demonstrated on three airfoil shapes under deep dynamic stall at a Mach number 0.1 and Reynolds number 135,000. Findings The results show that varying the trust-region (TR) radius (lambda) significantly affects the prediction accuracy of the MFM. The HF and LF simulation models hold similarity within small (lambda = 0.12) to medium (0.12 = lambda = 0.23) TR radii producing a prediction error less than 5%, whereas for large TR radii (0.23 = lambda = 0.41), the similarity is strongly affected by the time discretization and minimally by the spatial discretization. Originality/value The findings of this work present new knowledge for the construction of accurate MFMs for dynamic stall performance prediction using LF model spatial- and temporal discretization setup and the TR radius size. The approach used in this work is general and can be used for other unsteady applications involving CFD-based MFM and optimization.