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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| Vydáno v: | Aerospace science and technology Ročník 25; číslo 1; s. 177 - 189 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Issy-les-Moulineaux
Elsevier SAS
01.03.2013
Elsevier |
| Témata: | |
| ISSN: | 1270-9638, 1626-3219 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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. |
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| ISSN: | 1270-9638 1626-3219 |
| DOI: | 10.1016/j.ast.2012.01.006 |