A flow feature detection method for modeling pressure distribution around a cylinder in non-uniform flows by using a convolutional neural network
文献类型：期刊论文
| 作者 | Ye SR(叶舒然)2,3; Zhang Z(张珍)2,3 ; Song XD1; Wang YW(王一伟)2,3; Chen YS1; Huang CG(黄晨光)2,3
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| 刊名 | SCIENTIFIC REPORTS
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| 出版日期 | 2020-03-10 |
| 卷号 | 10期号:1页码:10 |
| ISSN号 | 2045-2322 |
| DOI | 10.1038/s41598-020-61450-z |
| 通讯作者 | Wang, Yiwei(wangyw@imech.ac.cn) |
| 英文摘要 | In a myriad of engineering situations, we often hope to establish a model which can acquire load conditions around structures through flow features detection. A data-driven method is developed to predict the pressure on a cylinder from velocity distributions in its wake flow. The proposed deep learning neural network is constituted with convolutional layers and fully-connected layers: The convolutional layers can process the velocity information by features extraction, which are gathered by the fully-connected layers to obtain the pressure coefficients. By comparing the output data of the typical network with Computational Fluid Dynamics (CFD) results as reference values, it suggests that the present convolutional neural network (CNN) is able to predict the pressure coefficient in the vicinity of the trained Reynolds numbers with various inlet flow profiles and achieves a high overall precision. Moreover, a transfer learning approach is adopted to preserve the feature detection ability by keeping the parameters in the convolutional layers unchanged while shifting parameters in the fully-connected layers. Further results show that this transfer learning network has nearly the same precision while significantly lower cost. The active prospects of convolutional neural network in fluid mechanics have also been demonstrated, which can inspire more kinds of loads prediction in the future. |
| 分类号 | 二类/Q1 |
| 资助项目 | National Natural Science Foundation of China[11772340] ; National Natural Science Foundation of China[11672315] ; Youth Innovation Promotion Association CAS[2015015] |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:000563379300015 |
| 资助机构 | National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS |
| 其他责任者 | Wang, Yiwei |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/85025]  |
| 专题 | 力学研究所_流固耦合系统力学重点实验室(2012-) |
| 作者单位 | 1.Peking Univ, Coll Engn, Beijing 100871, Peoples R China 2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China; 3.Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Ye SR,Zhang Z,Song XD,et al. A flow feature detection method for modeling pressure distribution around a cylinder in non-uniform flows by using a convolutional neural network[J]. SCIENTIFIC REPORTS,2020,10(1):10. |
| APA | 叶舒然,张珍,Song XD,王一伟,Chen YS,&黄晨光.(2020).A flow feature detection method for modeling pressure distribution around a cylinder in non-uniform flows by using a convolutional neural network.SCIENTIFIC REPORTS,10(1),10. |
| MLA | 叶舒然,et al."A flow feature detection method for modeling pressure distribution around a cylinder in non-uniform flows by using a convolutional neural network".SCIENTIFIC REPORTS 10.1(2020):10. |
入库方式： OAI收割
来源：力学研究所
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