2023 Fiscal Year Final Research Report
Study on liquid water content fluctuation in high Reynolds number turbulence for early detection of rapidly developing clouds
| Project/Area Number |
20K04298
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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 | Japan Agency for Marine-Earth Science and Technology |
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Principal Investigator |
Matsuda Keigo 国立研究開発法人海洋研究開発機構, 付加価値情報創生部門(地球情報科学技術センター), 副主任研究員 (50633880)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Keywords | 雲乱流 / 慣性粒子クラスタリング / 直接数値計算 / テセレーション解析 / ウェーブレット解析 |
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| Outline of Final Research Achievements |
Non-uniform distributions of cloud droplets in atmospheric turbulence, i.e., turbulent clustering, have been analyzed based on direct numerical simulation (DNS) of turbulent flows laden with many particles in order to clarify the clustering influence on the reflectivity in radar cloud observations. Novel large-scale clustering has been discovered by increasing the scale of DNS to be closer to actual atmospheric turbulence. In addition, the tessellation-based technique to analyze particle divergence and convergence has been developed to understand the clustering formation mechanisms. Multiscale structures of the clustering and the dynamics have been clarified using the Fourier, wavelet, and a newly developed multiresolution techniques.
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| Free Research Field |
熱流体工学
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| Academic Significance and Societal Importance of the Research Achievements |
雲粒の乱流クラスタリングがレーダー反射強度を顕著に増加させる効果を利用することで，積雲や積乱雲の中の乱流状態を推定できる可能性があり，突発的豪雨の探知に応用できる可能性がある．本研究で発見された比較的大スケールのクラスタリング構造は，レーダー反射強度に影響を及ぼし得るほか，雲粒の成長過程のモデリングにおいても重要となる可能性がある．雲粒の挙動を適切に数式モデル化するためにはその物理機構の理解が不可欠であり，そのために開発した粒子の収束・発散の数理解析ツールは様々な粒子挙動への応用も期待される．
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