2023 Fiscal Year Final Research Report
Deterministic stress modeling using machine learning and its application to design optimization
| Project/Area Number |
21K03869
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 19010:Fluid engineering-related
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| Research Institution | Iwate University |
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Principal Investigator |
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Keywords | 数値解析 / 非定常効果 / 決定論的応力 / 流体最適設計 / 機械学習 |
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| Outline of Final Research Achievements |
The design of fluid machines requires advanced technology, and numerical analysis is utilized as one of its crucial means. However, current design primarily relies on steady flow analysis without considering unsteady effects in the fluid machine. To advance the design technology, new numerical analysis techniques considering unsteady effects are necessary. It is expected that this will pave the way for innovative design. In this study, we elucidated the unsteady flow phenomena related to unsteady effects, and then we modeled unsteady effects with the idea of the deterministic stress and verified the prediction accuracy of the analysis results by conducting steady RANS analysis incorporating this modeling.
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| Free Research Field |
流体工学
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| Academic Significance and Societal Importance of the Research Achievements |
本研究では，非定常効果に関連する流動を明らかにするとともに，非定常効果をモデル化することで流体解析の高度化を試みた．本研究の成果は流体設計技術の発展へと展開され，産業界における技術革新と競争力の向上につながると期待される．流体設計の改善により，エネルギー効率が向上し環境負荷の低減に貢献し，新たな流体設計技術が普及することにより産業の発展に寄与すると期待される．
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