| Using machine learning to detect the turbulent region in flow past a circular cylinder | |
Li BL(李秉霖) ; Yang ZX(杨子轩); Zhang X(张星); He GW(何国威); Deng BQ; Shen L
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| Source Publication | JOURNAL OF FLUID MECHANICS
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| 2020-12-25 | |
| Volume | 905Pages:A10 |
| ISSN | 0022-1120 |
| Abstract | Detecting the turbulent/non-turbulent interface is a challenging topic in turbulence research. In the present study, machine learning methods are used to train detectors for identifying turbulent regions in the flow past a circular cylinder. To ensure that the turbulent/non-turbulent interface is independent of the reference frame of coordinates and is physics-informed, we propose to use invariants of tensors appearing in the transport equations of velocity fluctuations, strain-rate tensor and vortical tensor as the input features to identify the flow state. The training samples are chosen from numerical simulation data at two Reynolds numbers, and 3900. Extreme gradient boosting (XGBoost) is utilized to train the detector, and after training, the detector is applied to identify the flow state at each point of the flow field. The trained detector is found robust in various tests, including the applications to the entire fields at successive snapshots and at a higher Reynolds number . The objectivity of the detector is verified by changing the input features and the flow region for choosing the turbulent training samples. Compared with the conventional methods, the proposed method based on machine learning shows its novelty in two aspects. First, no threshold value needs to be specified explicitly by the users. Second, machine learning can treat multiple input variables, which reflect different properties of turbulent flows, including the unsteadiness, vortex stretching and three-dimensionality. Owing to these advantages, XGBoost generates a detector that is more robust than those obtained from conventional methods. |
| Keyword | wakes LAGRANGIAN COHERENT STRUCTURES turbulent transition DATA-DRIVEN INTERFACE DYNAMICS CLOSURE |
| DOI | 10.1017/jfm.2020.725 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000583498500001 |
| WOS Research Area | Mechanics ; Physics |
| WOS Subject | Mechanics ; Physics, Fluids & Plasmas |
| Funding Organization | NSFC Basic Science Center Program for 'Multiscale Problems in Nonlinear Mechanics' [11988102} ; Strategic Priority Research ProgramChinese Academy of Sciences [XDB22040104} |
| Classification | 一类/力学重要期刊 |
| Ranking | 1 |
| Contributor | Yang, ZX |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/85410 |
| Collection | 非线性力学国家重点实验室 |
| Affiliation | 1.{Li Binglin, Yang Zixuan, Zhang Xing, He Guowei} Chinese Acad Sci Inst Mech State Key Lab Nonlinear Mech Beijing 100190 Peoples R China 2.{Li Binglin, Yang Zixuan, Zhang Xing, He Guowei} Univ Chinese Acad Sci Sch Engn Sci Beijing 101408 Peoples R China 3.{Deng Bing-Qing, Shen Lian} Univ Minnesota Dept Mech Engn Minneapolis MN 55455 USA 4.{Deng Bing-Qing, Shen Lian} Univ Minnesota St Anthony Fall Lab Minneapolis MN 55455 USA |
| Recommended Citation GB/T 7714 | Li BL,Yang ZX,Zhang X,et al. Using machine learning to detect the turbulent region in flow past a circular cylinder[J]. JOURNAL OF FLUID MECHANICS,2020,905:A10.Rp_Au:Yang, ZX |
| APA | 李秉霖,杨子轩,张星,何国威,Deng BQ,&Shen L.(2020).Using machine learning to detect the turbulent region in flow past a circular cylinder.JOURNAL OF FLUID MECHANICS,905,A10. |
| MLA | 李秉霖,et al."Using machine learning to detect the turbulent region in flow past a circular cylinder".JOURNAL OF FLUID MECHANICS 905(2020):A10. |
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| Jp2020367.pdf（1681KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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