Efficient optimization design method using kriging model

Shinkyu Jeong, Mitsuhiro Murayama, Kazuomi Yamamoto

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    24 Citations (Scopus)


    The Kriging-based genetic algorithm is applied to aerodynamic design problems. The Kriging model, one of the response surface models, represents a relationship between the objective function (output) and design variables (input) using stochastic process. The kriging model drastically reduces the computational time required for objective function evaluation in the optimization (optimum searching) process. 'Expected improvement (EI)' is used as a criterion to select additional sample points. This makes it possible not only to improve the accuracy of the response surface but also to explore the global optimum efficiently. The functional analysis of variance (ANOVA) is conducted to evaluate the influence of each design variable and their interactions to the objective function. Based on the result of the functional ANOVA, designers can reduce the number of design variables by eliminating those that have small effect on the objective function. In this paper, the present method is applied to a two-dimensional airfoil design and the prediction of flap's position in a multi-element airfoil, where the lift-to-drag ratio (L/D) is maximized.

    Original languageEnglish
    Title of host publicationAIAA Paper
    Number of pages10
    Publication statusPublished - 2004
    Event42nd AIAA Aerospace Sciences Meeting and Exhibit - Reno, NV, United States
    Duration: 2004 Jan 52004 Jan 8


    Other42nd AIAA Aerospace Sciences Meeting and Exhibit
    Country/TerritoryUnited States
    CityReno, NV

    ASJC Scopus subject areas

    • Engineering(all)


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