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Multiobjective Aerodynamic Optimization by Variable-Fidelity Models and Response Surface Surrogates

Abstrakt

A computationally efficient procedure for multiobjective design optimization with variable-fidelity models and response surface surrogates is presented. The proposed approach uses the multiobjective evolutionary algorithm that works with a fast surrogate model, obtained with kriging interpolation of the low-fidelity model data enhanced by space-mapping correction exploiting a few high-fidelity training points. The initial Pareto front generated by multiobjective optimization of the surrogate using the multiobjective evolutionary algorithm can be iteratively refined by local enhancements of the surrogate model. The latter are realized with a space-mapping response correction based on a limited number of high-fidelity training points allocated along the initial Pareto front. The proposed method allows us to obtain, at a low computational cost, a set of designs representing tradeoffs between the conflicting objectives. The current approach is illustrated using examples of airfoil design: one in transonic flow, involving aerodynamics tradeoffs; and another one in low-speed flow, involving tradeoffs between the aerodynamic and the aeroacoustic performances.

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Kategoria:
Publikacja w czasopiśmie
Typ:
artykuł w czasopiśmie wyróżnionym w JCR
Opublikowano w:
AIAA JOURNAL nr 54, wydanie 2, strony 531 - 541,
ISSN: 0001-1452
Język:
angielski
Rok wydania:
2016
Opis bibliograficzny:
Leifsson L., Kozieł S., Tesfahunegn Y.: Multiobjective Aerodynamic Optimization by Variable-Fidelity Models and Response Surface Surrogates// AIAA JOURNAL. -Vol. 54, iss. 2 (2016), s.531-541
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.2514/1.j054128
Weryfikacja:
Politechnika Gdańska

wyświetlono 123 razy

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