| Project/Area Number |
20K04312
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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 19020:Thermal engineering-related
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| Research Institution | University of Tsukuba |
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Principal Investigator |
Shen Biao 筑波大学, システム情報系, 助教 (80730811)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2022: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2021: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2020: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | Nucleate boiling / Subatmospheric / Wettability / Surface cavity / Vapor trapping / Artificial intelligence / Infrared thermography / Machine learning / CNN / Boiling heat transfer / Contact line / Surface wettability |
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| Outline of Research at the Start |
The present research project proposes a comprehensive study of triple-phase contact line (TPCL) dynamics in pool boiling on surfaces featured with heterogeneous texturing and wettability patterning. For the first time, artificial intelligence (AI) technology will be used to analyze the visualization data. The results are expected to shed light on TPCL behavior under the influences of contrasting surface topography and wettability and lead to enhanced surface design for low-pressure boiling.
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| Outline of Final Research Achievements |
The present study focused on enhancement of boiling heat transfer under low pressures. The role of complex contact-line dynamics during bubble release from the boiling surface was elucidated, which led to development of hybrid surface combining wettability and structural engineering. The hydrophobic-coated cavities introduced stronger contact-line pinning, which was responsible for more vapor trapping compared with flat surface. Consequently, the enhanced surface led to significantly delayed transition to deteriorating intermittent boiling at very low pressures.
Additionally, a Convolutional Neural Network-based model was built to achieve fast processing visualization data for analysis of boiling characteristics. Trained on time series of high-speed infrared thermographic images of the boiling surface, the model was able to identify the existence of microlayer at an accuracy of 90%.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は、効率的な沸騰伝熱を持続させるための蒸気トラップの重要性について新たな見方を提供した。ハイブリッド表面設計は、気泡離脱時の接触線のピンニングを利用し、減圧下の沸騰を著しく向上させ、コンピュータなどの次世代冷却ソリューションの開発にポジティブな影響を与えることができる。一方、機械学習ベースの解析フレームワークは、可視化データの処理を加速するために使用でき、沸騰現象に対する強力な研究ツールを加える。
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