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

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...

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Bibliographic Details
Published in:Aerospace science and technology Vol. 25; no. 1; pp. 177 - 189
Main Authors: Han, Zhong-Hua, Görtz, Stefan, Zimmermann, Ralf
Format: Journal Article
Language:English
Published: Issy-les-Moulineaux Elsevier SAS 01.03.2013
Elsevier
Subjects:
Aerodynamics
Applied fluid mechanics
Computational fluid dynamics
Computational methods in fluid dynamics
Exact sciences and technology
Fluid dynamics
Fundamental areas of phenomenology (including applications)
Kriging model
Physics
Surrogate model
Variable-fidelity model
Kriging model
Surrogate model
Variable-fidelity model
Computational fluid dynamics
Drag
Digital simulation
Aerodynamics
Airfoils
Modelling
Mesh generation
Kriging
ISSN:1270-9638, 1626-3219
Online Access:Get full text
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Summary: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.
ISSN:1270-9638
1626-3219
DOI:10.1016/j.ast.2012.01.006