Back to Search
Start Over
Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function
- Source :
-
Aerospace Science & Technology . Mar2013, Vol. 25 Issue 1, p177-189. 13p. - Publication Year :
- 2013
-
Abstract
- 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. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 12709638
- Volume :
- 25
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Aerospace Science & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 86157729
- Full Text :
- https://doi.org/10.1016/j.ast.2012.01.006