| Predicting the near-wall velocity of wall turbulence using a neural network for particle image velocimetry | |
Wang HP(王洪平); Yang ZX(杨子轩); Li BL(李秉霖) ; Wang SZ(王士召)
| |
| Source Publication | PHYSICS OF FLUIDS
 |
| 2020-11-01 | |
| Volume | 32Issue:11Pages:115105 |
| ISSN | 1070-6631 |
| Abstract | Near-wall velocity prediction for wall-bounded turbulence is useful for constructing a wall model and estimating dissipation and wall shear stress. A convolutional neural network is developed to improve the near-wall velocity prediction and spatial resolution for wall-bounded turbulent velocity fields obtained using particle image velocimetry (PIV). To establish the relationship between the low-resolution and high-resolution fields, this machine learning model is trained on a synthetic PIV dataset generated based on velocity fields obtained from the direct numerical simulation of turbulent channel flows at Re-tau = 1000. Using a test dataset with a higher Reynolds number of Re-tau = 5200, the performance of this model is assessed in terms of instantaneous fields, error analysis, velocity statistics, and energy spectra. The influences of the interrogation window, image resolution, and particle concentration on the performance of this network are also considered. We further apply this network to practical PIV data from a turbulent boundary layer at Re-tau = 2200 to assess the network performance under real experimental conditions. The results indicate that the proposed machine-learning-based model can predict missing near-wall velocity fields and enhance the spatial resolution of PIV fields, but the accuracy for Reynolds shear stress prediction needs to be further improved. The presented approach shows the potential ability to predict the near-wall instantaneous velocity of high-Reynolds-number turbulence from low-Reynolds-number flow fields. |
| Keyword | CHANNEL FLOW BOUNDARY-LAYER FRICTION MODEL GENERATION DATABASE REGION |
| DOI | 10.1063/5.0023786 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000589660100001 |
| 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} ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [11702302, 11922214, 91752118} ; Strategic Priority Research ProgramChinese Academy of Sciences [XDB22040104} ; Key Research Program of Frontier Sciences of the Chinese Academy of Sciences [QYZDJ-SSW-SYS002} |
| Classification | 一类/力学重要期刊 |
| Ranking | 1 |
| Contributor | Wang, SZ |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/85432 |
| Collection | 非线性力学国家重点实验室 |
| Affiliation | {Wang Hongping, Yang Zixuan, Li Binglin, Wang Shizhao} Chinese Acad Sci State Key Lab Nonlinear Mech Inst Mech Beijing 100190 Peoples R China |
| Recommended Citation GB/T 7714 | Wang HP,Yang ZX,Li BL,et al. Predicting the near-wall velocity of wall turbulence using a neural network for particle image velocimetry[J]. PHYSICS OF FLUIDS,2020,32,11,:115105.Rp_Au:Wang, SZ |
| APA | 王洪平,杨子轩,李秉霖,&王士召.(2020).Predicting the near-wall velocity of wall turbulence using a neural network for particle image velocimetry.PHYSICS OF FLUIDS,32(11),115105. |
| MLA | 王洪平,et al."Predicting the near-wall velocity of wall turbulence using a neural network for particle image velocimetry".PHYSICS OF FLUIDS 32.11(2020):115105. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| Jp2020391.pdf（5162KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment