On Improving Efficiency and Accuracy of Variable-Fidelity Surrogate Modeling in Aero-data for Loads Context

Abstract

Variable-fidelity surrogate modeling offers an efficient way to generate aerodynamic data for aero-loads prediction based on a set of CFD methods with varying degree of fidelity and computational expense. In this paper, new algorithms, such as a Gradient-Enhanced Kriging method (direct GEK) and a generalized hybrid bridge function, have been developed to improve the efficiency and accuracy of the existing Variable-Fidelity Modeling (VFM) approach. These new algorithms and features are demonstrated and evaluated for analytical functions and used to construct a global surrogate model for the aerodynamic coefficients and drag polar of an RAE 2822 airfoil. It is preliminarily shown in this paper that they are very promising and can be used to significantly improve the efficiency and accuracy of VFM in the context of aero-loads prediction.