| Project/Area Number |
19K14899
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 19010:Fluid engineering-related
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| Research Institution | Tokyo University of Science (2021)
Japan Aerospace EXploration Agency (2019-2020) |
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Principal Investigator |
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| Project Period (FY) |
2019-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2021: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2020: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2019: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 壁面摩擦力 / 非点収差PTV / オイルフィルム / 壁面せん断応力 / 3次元3成分流速計測 / 壁面近傍流動 / 3次元流速計測 |
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| Outline of Research at the Start |
本研究では，大小様々なスケールの流れ場における壁面近傍の微細な流動形態に着目し，3次元3成分流速計測手法に基づく壁面せん断応力分布のセンシング手法を駆使して，壁面摩擦力を分布として高精度に定量化可能な手法を提案する．また，提案した手法による壁面摩擦力の詳細な分布計測を通じて，摩擦力の分布とレイノルズ数の関係を明らかにして定式化し，壁面近傍における流動構造のより正確な理解や予測に貢献する．
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| Outline of Final Research Achievements |
In this study, we devised the quantification technique of skin friction by measuring inside the oil film by astigmatism PTV (APTV). An oil film was painted on the bottom surface of a rectangular channel, and the three-dimensional velocity distribution inside oil film was measured by APTV. The skin friction force was then quantified from the velocity distribution. In this study, measurements were conducted by varying the Reynolds number in various range, and the results were compared with those obtained by conventional friction force measurement techniques.
Moreover, the calibration procedure of APTV using neural network technique was also devised in this study. The devised technique could determine the particles' depth position with small errors, and we thus concluded that the the technique is valid for three-dimensional velocity measurement inside oil film.
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| Academic Significance and Societal Importance of the Research Achievements |
画像計測に基づく流速計測手法に機械学習の要素を取り入れた高度化に取り組み，それが油膜のような高粘度液体内部の流速計測に有効であることを示した．また，ピトー管や熱線流速計などの大がかりな装置を用いて計測していた流動の壁面摩擦力を，光学的な計測手法を用いて，微小な領域における3次元的な流速分布を算出するだけで高精度に定量化できることを示すことができた．そのため，本研究は流体計測手法とその発展に大きく寄与するものであるといえる．
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