著者

杉浦 正彦 田辺 安忠 菅原 瑛明 木村 桂大 竹川 国之 大山 聖 佐藤 允 金崎 雅博 岸 祐希 SUGIURA Masahiko TANABE Yasutada SUGAWARA Hideaki KIMURA Keita TAKEKAWA Kuniyuki OYAMA Akira SATO Makoto KANAZAKI Masahiro KISHI Yuki

出版者

宇宙航空研究開発機構(JAXA)

雑誌

宇宙航空研究開発機構特別資料: 流体力学講演会/航空宇宙数値シミュレーション技術シンポジウム2020オンライン論文集 = JAXA Special Publication: Proceedings of Fluid Dynamics Conference / Aerospace Numerical Simulation Symposium 2020 Online (ISSN:24332232)

巻号頁・発行日

vol.JAXA-SP-20-008, pp.157-164, 2021-02-08

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流体力学講演会/航空宇宙数値シミュレーション技術シンポジウム2020オンライン (2020年9月28日-30日. 日本航空宇宙学会 : 宇宙航空研究開発機構(JAXA)オンライン会議)

著者

岸 祐希 金崎 雅博 牧野 好和 松島 紀佐

出版者

一般社団法人 日本機械学会

雑誌

日本機械学会論文集 (ISSN:21879761)

巻号頁・発行日

vol.83, no.849, pp.16-00454-16-00454, 2017 (Released:2017-05-25)

参考文献数

20

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When a wing of airplane is designed, it is necessary to have knowledge regarding planforms considered their optimum airfoils in order to design efficiently, because each wing planform has both of advantage and disadvantage. In this study, the wing design problem for supersonic transport is carried out for different planforms for two different planforms. Multi-objective problem, which is minimization drags for two supersonic cruise conditions (transonic and supersonic flight) is solved to obtain knowledge of the supersonic airfoil from the viewpoint of the multi-point design. Two types of planforms are considered—a cranked arrow wing with a high sweep-back angle and a single tapered wing with a low sweep-back angle. Optimization problems are carried out by efficient global optimization, which is evolutionary algorithm based on the Kriging surrogate model. To acquire design knowledge, a parallel coordinate plot and functional analysis of variance (functional ANOVA) are applied. The design results showed the difference airfoil between two planforms. The optimum airfoil for the single tapered wing has a small or negative camber at the leading edge to minimize the supersonic cruising. On the other hand, the optimum airfoil for the cranked arrow wing has an airfoil with a lower thickness and larger camber at the leading edge.

著者

岸 祐希 アリヤリ アタフォン 金崎 雅博

出版者

進化計算学会

雑誌

進化計算学会論文誌 (ISSN:21857385)

巻号頁・発行日

vol.12, no.3, pp.137-147, 2021 (Released:2022-01-18)

参考文献数

32

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In this study, a multi-fidelity approach was developed based on the efficient global optimization (EGO) and integrated with multi-additional sampling. The developed approach was more efficient than the conventional multi-fidelity approach when applied to design problems. The effectiveness of the proposed approach was demonstrated by solving two test problems (a test problem in Van Valedhuizen’s test suite and a test problem with a convex Pareto front) before applying the approach to real-world problems. As a demonstration of solving real-world problem, we solved two objective airfoil design problems for a small unmanned airplane. The objective functions were the drag coefficient (for flight efficiency) and the thickness at the 75% chord position (for structural strength and manufacturability). The results of the test problems revealed that the proposed approach obtained more non-dominant solutions near the theoretical Pareto front than those obtained by the Original optimization approach at the same iteration number of EGO loop; this is because the proposed approach obtained more additional samples than the Original optimization approach (multi-objective multi-fidelity EGO without multi-additional sampling) per additional sampling loop. A comparison of the accuracies of surrogate models based on the proposed approach and the Original optimization approach using leave-one-out cross validation suggested that, depending on the optimization problem, one of the two approaches can yield greater accuracy. The airfoil design results, as well as the test problems, revealed that the proposed approach can obtain several better solutions than those obtained by the Original optimization approach when the number of iterations of additional sampling was the same between both approaches. The hypervolume in the proposed approach also increases more rapidly than that in the Original optimization approach.