IMECH-IR  > 流固耦合系统力学重点实验室
Application of deep learning method to Reynolds stress models of channel flow based on reduced-order modeling of DNS data
Zhang Z(张珍); Song XD; Ye SR; Wang YW(王一伟); Huang CG(黄晨光); An YR; Chen YS
Source PublicationJOURNAL OF HYDRODYNAMICS
2019-02-01
Volume31Issue:1Pages:58-65
ISSN1001-6058
Abstract

Recently, the methodology of deep learning is used to improve the calculation accuracy of the Reynolds-averaged Navier-Stokes (RANS) model. In this paper, a neural network is designed to predict the Reynolds stress of a channel flow of different Reynolds numbers. The rationality and the high efficiency of the neural network is validated by comparing with the results of the direct numerical simulation (DNS To further enhance the prediction accuracy, three methods are developed by using several algorithms and simplified models in the neural network. In the method 1, the regularization is introduced and it is found that the oscillation and the overfitting of the results are effectively prevented. In the method 2, y(+) is embedded in the input variable while the combination of the invariants is simplified in the method 3. From the predicted results, it can be seen that by using the first two methods, the errors are reduced. Moreover, the method 3 shows considerable advantages in the DNS trend and the smoothness of a curve. Consequently, it is concluded that the DNNs can predict effectively the anisotropic Reynolds stress and is a promising technique of the computational fluid dynamics.

KeywordDeep neural network channel flow turbulence model Reynolds stress
DOI10.1007/s42241-018-0156-9
Indexed BySCI ; EI ; CSCD
Language英语
WOS IDWOS:000459199400006
WOS KeywordNUMERICAL-SIMULATION ; TURBULENT-FLOW ; VERIFICATION ; DYNAMICS
WOS Research AreaMechanics
WOS SubjectMechanics
Funding OrganizationNational Key RD Program [2016YFC0301601]
CSCD IDCSCD:6426636
ClassificationQ3
Ranking1
Citation statistics
Document Type期刊论文
Identifierhttp://dspace.imech.ac.cn/handle/311007/78484
Collection流固耦合系统力学重点实验室
Affiliation1.{Zhang, Zhen、Ye, Shu-ran、Wang, Yi-wei、Huang, Chen-guang} Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China
2.{Zhang, Zhen、Ye, Shu-ran、Wang, Yi-wei、Huang, Chen-guang} Univ Chinese Acad Sci, Coll Engn Sci, Beijing 100049, Peoples R China
3.{Song, Xu-dong、An, Yi-ran、Chen, Yao-song} Peking Univ, Coll Engn, Beijing 100871, Peoples R China
Recommended Citation
GB/T 7714
Zhang Z,Song XD,Ye SR,et al. Application of deep learning method to Reynolds stress models of channel flow based on reduced-order modeling of DNS data[J]. JOURNAL OF HYDRODYNAMICS,2019,31(1):58-65.
APA Zhang Z.,Song XD.,Ye SR.,Wang YW.,Huang CG.,...&Chen YS.(2019).Application of deep learning method to Reynolds stress models of channel flow based on reduced-order modeling of DNS data.JOURNAL OF HYDRODYNAMICS,31(1),58-65.
MLA Zhang Z,et al."Application of deep learning method to Reynolds stress models of channel flow based on reduced-order modeling of DNS data".JOURNAL OF HYDRODYNAMICS 31.1(2019):58-65.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhang Z(张珍)]'s Articles
[Song XD]'s Articles
[Ye SR]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhang Z(张珍)]'s Articles
[Song XD]'s Articles
[Ye SR]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhang Z(张珍)]'s Articles
[Song XD]'s Articles
[Ye SR]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.