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An Artificial Neural Network-Assisted Genetic Algorithm with Application to Multi-Objective Transonic Airfoil Shape Optimization
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ja
人工ニューラルネットワーク支援型遺伝的アルゴリズムの多目的遷音速翼型形状最適化への応用
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HARIANSYAH, Muhammad Alfiyandy
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Description |
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第53回流体力学講演会/第39回航空宇宙数値シミュレーション技術シンポジウム (2021年6月30日-7月2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)
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The 53rd Fluid Dynamics Conference / the 39th Aerospace Numerical Simulation Symposium (June 30 - July 2, 2021. The Japan Society for Aeronautical and Space Sciences : Japan Aerospace Exploration Agency (JAXA), Online meeting)
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Evolutionary algorithms (EAs) have been widely used in design optimization that often requires expensive evaluations (e.g., computational fluid dynamics: CFD). An artificial neural network (ANN) based surrogate model is used in the EA optimization routines to reduce the time for these expensive evaluations. The ANN can model the relationship between many design variables and objective functions in a single surrogate model, unlike other surrogate models (e.g., Kriging). In this study, a genetic algorithm (GA) coupled with a dynamically retrained ANN is proposed and applied to multi-objective transonic airfoil shape optimization where aerodynamic performances are evaluated with CFD. The proposed method is shown to converge more quickly towards the Pareto-optimal front with fewer CFD evaluations compared to a stand-alone GA, proving the efficacy of ANN as the surrogate model in the GA.
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形態: カラー図版あり
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Physical characteristics: Original contains color illustrations
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資料番号: AA2130027008
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レポート番号: JAXA-SP-21-008
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Publisher |
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宇宙航空研究開発機構(JAXA)
Japan Aerospace Exploration Agency (JAXA)
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Date |
Created2022-02-07
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Issued2022-02-14
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conference paper |
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URI
http://id.nii.ac.jp/1696/00048365/
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Journal |
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宇宙航空研究開発機構特別資料: 第53回流体力学講演会/第39回航空宇宙数値シミュレーション技術シンポジウム論文集 = JAXA Special Publication: Proceedings of the 53rd Fluid Dynamics Conference / the 39th Aerospace Numerical Simulation Symposium
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Volume NumberJAXA-SP-21-008
Page Start115
Page End124
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Oaidate |
2023-04-03 |