Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function

Authors: Zhong-Hua Han, Stefan Görtz, Ralf Zimmermann

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, direct Gradient-Enhanced Kriging (GEK) and a newly developed Generalized Hybrid Bridge Function (GHBF) have been combined in order to improve the efficiency and accuracy of the existing Variable-Fidelity Modeling (VFM) approach. The new algorithms and features are demonstrated and evaluated for analytical functions and are subsequently used to construct a global surrogate model for the aerodynamic coefficients and drag polar of an RAE 2822 airfoil. It is shown that the gradient-enhanced GHBF proposed in this paper is very promising and can be used to significantly improve the efficiency, accuracy and robustness of VFM in the context of aero-loads prediction.

Keywords:
Surrogate model
Variable-fidelity model
Kriging model
Computational fluid dynamics

Published in: Aerospace Science and Technology  (Volumes 25, Issue 1, March, 2013)

Publisher: Elsevier

ISSN Information: 1270-9638

Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function

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