Super-resolution reconstruction of turbulent flows with machine learning
We use machine learning to perform super-resolution analysis of grossly under-resolved turbulent flow field data to reconstruct the high-resolution flow field. Two machine learning models are developed, namely, the convolutional neural network (CNN) and the hybrid downsampled skip-connection/multi-s...
Uložené v:
| Vydané v: | Journal of fluid mechanics Ročník 870; s. 106 - 120 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Cambridge, UK
Cambridge University Press
10.07.2019
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| Predmet: | |
| ISSN: | 0022-1120, 1469-7645 |
| On-line prístup: | Získať plný text |
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| Abstract | We use machine learning to perform super-resolution analysis of grossly under-resolved turbulent flow field data to reconstruct the high-resolution flow field. Two machine learning models are developed, namely, the convolutional neural network (CNN) and the hybrid downsampled skip-connection/multi-scale (DSC/MS) models. These machine learning models are applied to a two-dimensional cylinder wake as a preliminary test and show remarkable ability to reconstruct laminar flow from low-resolution flow field data. We further assess the performance of these models for two-dimensional homogeneous turbulence. The CNN and DSC/MS models are found to reconstruct turbulent flows from extremely coarse flow field images with remarkable accuracy. For the turbulent flow problem, the machine-leaning-based super-resolution analysis can greatly enhance the spatial resolution with as little as 50 training snapshot data, holding great potential to reveal subgrid-scale physics of complex turbulent flows. With the growing availability of flow field data from high-fidelity simulations and experiments, the present approach motivates the development of effective super-resolution models for a variety of fluid flows. |
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| AbstractList | We use machine learning to perform super-resolution analysis of grossly under-resolved turbulent flow field data to reconstruct the high-resolution flow field. Two machine learning models are developed, namely, the convolutional neural network (CNN) and the hybrid downsampled skip-connection/multi-scale (DSC/MS) models. These machine learning models are applied to a two-dimensional cylinder wake as a preliminary test and show remarkable ability to reconstruct laminar flow from low-resolution flow field data. We further assess the performance of these models for two-dimensional homogeneous turbulence. The CNN and DSC/MS models are found to reconstruct turbulent flows from extremely coarse flow field images with remarkable accuracy. For the turbulent flow problem, the machine-leaning-based super-resolution analysis can greatly enhance the spatial resolution with as little as 50 training snapshot data, holding great potential to reveal subgrid-scale physics of complex turbulent flows. With the growing availability of flow field data from high-fidelity simulations and experiments, the present approach motivates the development of effective super-resolution models for a variety of fluid flows. |
| Author | Taira, Kunihiko Fukagata, Koji Fukami, Kai |
| Author_xml | – sequence: 1 givenname: Kai surname: Fukami fullname: Fukami, Kai email: kai.fukami@keio.jp organization: Department of Mechanical Engineering, Keio University, Yokohama, 223-8522, Japan – sequence: 2 givenname: Koji orcidid: 0000-0003-4805-238X surname: Fukagata fullname: Fukagata, Koji organization: Department of Mechanical Engineering, Keio University, Yokohama, 223-8522, Japan – sequence: 3 givenname: Kunihiko orcidid: 0000-0002-3762-8075 surname: Taira fullname: Taira, Kunihiko organization: Department of Mechanical Engineering, Florida State University, Tallahassee, FL 32310, USA |
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| Title | Super-resolution reconstruction of turbulent flows with machine learning |
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