著者

深見 開 深潟 康二 平 邦彦

出版者

一般社団法人 日本機械学会

雑誌

流体工学部門講演会講演論文集 2019 (ISSN:24242896)

巻号頁・発行日

pp.OS8-01, 2019 (Released:2020-07-25)

被引用文献数

4 4

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We use machine-learning-based super-resolution analysis to reconstruct high-resolution flow field data from grossly coarse low-resolution data, for three-dimensional fully developed turbulent channel flow at Reτ = 180. The training data is obtained by three-dimensional direct numerical simulation (DNS). We use an average pooling operation used commonly in image tasks, to prepare the coarse input data set. As a machine learning model, the hybrid downsampled skip-connection multi-scale (DSC/MS) model based on convolutional neural network is utilized in this study. Remarkable about this model are its robustness against rotation/translation of the flow images and its ability to consider multi-scale property of turbulence. The super-resolved flow fields recovered through the proposed machine learning model are in agreement with the reference DNS data in terms of velocity color distributions, root mean squared values of velocity fluctuations and L2 error norm defined as the difference between the reference DNS data and super-resolved flow field. The maximum wavenumbers of streamwise and spanwise energy spectrum recovered by machine learning are increased by the super-resolution reconstruction. The proposed method holds great potential for various applications in experimental and numerical situations to handle the fluid big data efficiently, e.g., PIV measurements and subgrid-scale modeling of large-eddy simulation.

著者

奥田 知明 梶野 瑞王 深潟 康二 岩田 歩

出版者

慶應義塾大学

雑誌

基盤研究(A)

巻号頁・発行日

2020-04-01

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有害性が懸念されるエアロゾル粒子の生体や地表面への沈着挙動を議論する上で、粒子の沈着現象に関わる重要なパラメータである粒子の帯電状態については、ほとんど研究が進んでいない。本研究では、生体や地表面へのエアロゾル粒子の沈着現象において、粒子のサイズや粒径分布および幾何学的形状等のパラメータ群と比較して、実環境大気エアロゾルの帯電状態がどの程度の影響を持つか、という問いに対して、観測と実験およびシミュレーションモデルの手法を駆使して明らかにすることを目指す。