| 题名 | Practical framework for data-driven RANS modeling with data augmentation |
| 作者 | |
| 通讯作者 | Xia, Zhenhua |
| 发表日期 | 2022
|
| DOI | |
| 发表期刊 | |
| ISSN | 0567-7718
|
| EISSN | 1614-3116
|
| 卷号 | 37页码:1748-1756 |
| 摘要 | Inspired by the iterative procedure of computing mean fields with known Reynolds stresses (Guo et al., Theor Appl Mech Lett, 2021), we proposed a way to achieve data augmentation by utilizing the intermediate mean fields after proper selections. We also proposed modifications to the Tensor Basis Neural Network (Ling et al., J Fluid Mech, 2016) model. With the modification of the learning targets and the inclusions of wall distance and logarithm of normalized eddy viscosity in the model inputs, the modified version of the model with augmented training datasets shows better performance on Reynolds stress predictions for two dimensional incompressible flow over periodic hills under different geometries. Furthermore, better propagated mean velocity fields can be achieved, showing better agreements with the direct numerical simulations (DNS) results. |
| 关键词 | |
| 相关链接 | [来源记录] |
| 收录类别 | |
| 语种 | 英语
|
| 学校署名 | 其他
|
| 资助项目 | National Natural Science Foundation of China[11822208,11988102,11772297,91852205]
|
| WOS研究方向 | Engineering
; Mechanics
|
| WOS类目 | Engineering, Mechanical
; Mechanics
|
| WOS记录号 | WOS:000745753800001
|
| 出版者 | |
| EI入藏号 | 20220511545435
|
| EI主题词 | Incompressible flow
; Machine learning
; Navier Stokes equations
; Turbulent flow
; Velocity
|
| EI分类号 | Fluid Flow, General:631.1
; Calculus:921.2
|
| ESI学科分类 | ENGINEERING
|
| 来源库 | Web of Science
|
| 引用统计 |
被引频次[WOS]：9
|
| 成果类型 | 期刊论文 |
| 条目标识符 | http://sustech.caswiz.com/handle/2SGJ60CL/272760 |
| 专题 | 工学院_力学与航空航天工程系 |
| 作者单位 | 1.Peking Univ, Coll Engn, State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China 2.Zhejiang Univ, Dept Engn Mech, Hangzhou 310027, Peoples R China 3.Southern Univ Sci & Technol, Dept Mech & Aerosp Engn, Shenzhen 518055, Peoples R China |
| 推荐引用方式 GB/T 7714 |
Guo, Xianwen,Xia, Zhenhua,Chen, Shiyi. Practical framework for data-driven RANS modeling with data augmentation[J]. ACTA MECHANICA SINICA,2022,37:1748-1756.
|
| APA |
Guo, Xianwen,Xia, Zhenhua,&Chen, Shiyi.(2022).Practical framework for data-driven RANS modeling with data augmentation.ACTA MECHANICA SINICA,37,1748-1756.
|
| MLA |
Guo, Xianwen,et al."Practical framework for data-driven RANS modeling with data augmentation".ACTA MECHANICA SINICA 37(2022):1748-1756.
|
| 条目包含的文件 | 条目无相关文件。 | |||||
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