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RISS 인기검색어
- 검색키워드
- 저자 : Kazuomi Yamamoto (검색결과 2 건)
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Design Exploration of High-Lift Airfoil Using Kriging Model and Data Mining Technique
Kanazaki, Masahiro,Yamamoto, Kazuomi,Tanaka, Kentaro,Jeong, Shin-Kyu The Korean Society for Aeronautical and Space Scie 2007 International Journal of Aeronautical and Space Sc Vol.8 No.2
A multi-objective design exploration for a three-element airfoil consisted of a slat, a main wing, and a flap was carried out. The lift curve improvement is important to design high-lift system, thus design has to be performed with considered multi-angle. The objective functions considered here are to maximize the lift coefficient at landing and near stall conditions simultaneously. Kriging surrogate model which was constructed based on several sample designs is introduced. The solution space was explored based on the maximization of Expected Improvement (EI) value corresponding to objective functions on the Krigingmodels. The improvement of the model and the exploration of the optimum can be advanced at the same time by maximizing EI value. In this study, a total of 90 sample points are evaluated using the Reynolds averaged Navier-Stokes simulation(RANS) for the construction of the Kriging model. In order to obtain the information of the design space, two data mining techniques are applied to design result. One is functional Analysis of Variance(ANOVA) which can show quantitative information and the other is Self-Organizing Map(SOM) which can show qualitative information.
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Design Exploration of High-Lift Airfoil Using Kriging Model and Data Mining Technique
Masahiro Kanazaki,Kazuomi Yamamoto,Kentaro Tanaka,Shinkyu Jeong 한국항공우주학회 2007 International Journal of Aeronautical and Space Sc Vol.8 No.2
A multi-objective design exploration for a three-element airfoil consisted of a slat, a main wing, and a flap was carried out. The lift curve improvement is important to design high-lift system, thus design has to be performed with considered multi-angle. The objective functions considered here are to maximize the lift coefficient at landing and near stall conditions simultaneously. Kriging surrogate model which was constructed based on several sample designs is introduced. The solution space was explored based on the maximization of Expected Improvement (EI) value corresponding to objective functions on the Krigingmodels. The improvement of the model and the exploration of the optimum can be advanced at the same time by maximizing EI value. In this study, a total of 90 sample points are evaluated using the Reynolds averaged Navier-Stokes simulation (RANS) for the construction of the Kriging model. In order to obtain the information of the design space, two data mining techniques are applied to design result. One is functional Analysis of Variance (ANOVA) which can show quantitative information and the other is Self-Organizing Map (SOM) which can show qualitative information.
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