| Project/Area Number |
17K14589
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Fluid engineering
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| Research Institution | Nagasaki University |
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Principal Investigator |
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| Project Period (FY) |
2017-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2018: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2017: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
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| Keywords | 乱流 / 多目的最適化 / ニューラルネットワーク / 遺伝的アルゴリズム / マルチスケール格子乱流 / 直接数値シミュレーション / 最適化 / 直接数値計算 / 統計理論 / クロージャー理論 / 流体工学 / 混合 / 等方性乱流 |
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| Outline of Final Research Achievements |
We developed the multi-objective optimization method using artificial neural network and genetic algorithm, and direct numerical simulation for multiscale grid.
Under the constraint of same blockage ratio, we carried out the geometry optimization of multiscale grid for the sake of increaing turbulent Reynolds number at upstream (production region) and downstream (fully developed region).
As a consequence, we found that turbulent Reynolds number strongly depends on the grid geometry, whereas pressure drop is not dependent on the grid geometry. We also explored the grid geometry which can generate high Reynolds number flow than fractral square grid.
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| Academic Significance and Societal Importance of the Research Achievements |
乱流の騒音や圧力損失といった問題点を抑え，混合促進などの利点を生かした最適な格子形状の探索が，社会のニーズとして求められている．
本研究では，圧力損失が小さく，強い乱れを生成できる格子形状の探索を機械学習を通して実現した．
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