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

荻野 琢己 飯田 明由 大田 浩嗣 杉田 光弘

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.IS-29, 2019 (Released:2020-07-25)

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The aim of this investigation is to estimate drag force acting on a bicycle helmet and power required for wind speed of bicycle race. It is known that a helmet of bicycle race has not only role to protect the head but also reduce aerodynamic drag force. Since the required power of caused by aerodynamic force acting on the helmet and head is about 18% of the total power of cycling, development of the low drag force helmet is required. To evaluate and understand the unsteady aerodynamic force and its generation mechanism, LES analysis was performed. As a result, the streamlined shape helmet placed in straight against the flow has low drag, but the drag force rapidly increases in the posture during goal sprinting. Therefore, the power required for top speed of bicycle race is more than three times that in steady state. Since, the flow around the helmet is unsteady, the maximum fluid force reaches 3.5 times that of the average. For this reason, it is required to develop robust shape helmet against changes in posture with small fluctuating drag force.

著者

石原 裕二

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.IS-28, 2019 (Released:2020-07-25)