A robust super-resolution reconstruction model of turbulent flow data based on deep learning
文献类型：期刊论文
| 作者 | Zhou, Zhideng; Li, Binglin ; Yang, Xiaolei; Yang, Zixuan2; Yang XL(杨晓雷); Li BL(李秉霖)
|
| 刊名 | COMPUTERS & FLUIDS
 |
| 出版日期 | 2022-05-15 |
| 卷号 | 239页码:15 |
| 关键词 | Super-resolution model Direct numerical simulation Large-Eddy simulation Isotropic turbulence Unresolved scales |
| ISSN号 | 0045-7930 |
| DOI | 10.1016/j.compfluid.2022.105382 |
| 通讯作者 | Yang, Zixuan(yangzx@imech.ac.cn) |
| 英文摘要 | A new super-resolution model, namely the turbulence volumetric super-resolution (TVSR) model, is developed based on convolutional neural network (CNN) to reconstruct three-dimensional high-resolution turbulent flow field data from low-resolution data. Direct numerical simulation (DNS) and corresponding filtered DNS (FDNS) data of homogeneous isotropic turbulence at various Reynolds numbers are used to train the TVSR model. The proposed model is a modification of Liu et al. (2020), aiming to provide an improved generalization capability of the super-resolution model. For this purpose, we propose a patchwise training strategy in consideration of the property of turbulence that the velocity correlation between two points diminishes as the separation becomes sufficiently large. Furthermore, data at various Reynolds numbers are combined together to train the model. In comparison with existing models, the present TVSR model shows a better generalization capability in two aspects. First, the TVSR model trained using data at low Reynolds numbers is found robust and accurate in the super-resolution reconstructions of flow fields at higher Reynolds numbers. Second, although only DNS data are used for training, the TVSR model is also robust in reconstructing high-resolution flow fields from low-resolution data obtained from large-eddy simulation (LES). This feature of the TVSR model provides a new access to obtain turbulent motions at unresolved scales in LES studies of turbulent flows. |
| WOS关键词 | LARGE-EDDY SIMULATIONS ; ISOTROPIC TURBULENCE ; TIME CORRELATIONS ; DECONVOLUTION ; ENRICHMENT ; SCALES |
| 资助项目 | National Natural Science Foundation of China (NSFC)[11988102] ; NSFC project[11972038] ; NSFC project[12002345] ; National Key Project[GJXM92579] ; Strategic Priority Research Program[XDB22040104] |
| WOS研究方向 | Computer Science ; Mechanics |
| 语种 | 英语 |
| WOS记录号 | WOS:000793060900001 |
| 资助机构 | National Natural Science Foundation of China (NSFC) ; NSFC project ; National Key Project ; Strategic Priority Research Program |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/89315]  |
| 专题 | 力学研究所_非线性力学国家重点实验室 |
| 通讯作者 | Yang, Zixuan |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Zhideng,Li, Binglin,Yang, Xiaolei,et al. A robust super-resolution reconstruction model of turbulent flow data based on deep learning[J]. COMPUTERS & FLUIDS,2022,239:15. |
| APA | Zhou, Zhideng,Li, Binglin,Yang, Xiaolei,Yang, Zixuan,杨晓雷,&李秉霖.(2022).A robust super-resolution reconstruction model of turbulent flow data based on deep learning.COMPUTERS & FLUIDS,239,15. |
| MLA | Zhou, Zhideng,et al."A robust super-resolution reconstruction model of turbulent flow data based on deep learning".COMPUTERS & FLUIDS 239(2022):15. |
入库方式： OAI收割
来源：力学研究所
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