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

Zhong Hua Han*, Stefan Goertz, Ralf Zimmermann

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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.

Original languageEnglish
JournalAerospace Science and Technology
Volume25
Issue number1
Pages (from-to)177-189
Number of pages13
ISSN1270-9638
DOIs
Publication statusPublished - Mar 2013
Externally publishedYes

Keywords

  • Computational fluid dynamics
  • Kriging model
  • Surrogate model
  • Variable-fidelity model

Fingerprint

Dive into the research topics of 'Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function'. Together they form a unique fingerprint.

Cite this