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Artificial neural network based nonlinear algebraic models for large eddy simulation of compressible wall bounded turbulence

文献类型：期刊论文

作者Xu, Dehao; Wang, Jianchun3; Yu ZP(于长平); Chen, Shiyi1,3
刊名JOURNAL OF FLUID MECHANICS
出版日期2023-03-29
卷号960页码:A4
ISSN号0022-1120
关键词machine learning turbulence modelling
DOI10.1017/jfm.2023.179
英文摘要In this paper, we propose artificial neural network based (ANN based) nonlinear algebraic models for the large eddy simulation (LES) of compressible wall bounded turbulence. An innovative modification is applied to the invariants and the tensor bases of the nonlinear algebraic models through using the local grid widths along each direction to normalise the corresponding gradients of the flow variables. Furthermore, the dimensionless model coefficients are determined by the ANN method. The modified ANN based nonlinear algebraic model (MANA model) has much higher correlation coefficients and much lower relative errors than the dynamic Smagorinsky model (DSM), Vreman model and wall adapting local eddy viscosity model in the a priori test. The significantly more accurate estimations of the mean subgrid scale (SGS) fluxes of the kinetic energy and temperature variance are also obtained by the MANA models in the a priori test. Furthermore, in the a posteriori test, the MANA model can give much more accurate predictions of the flow statistics and the mean SGS fluxes of the kinetic energy and the temperature variance than other traditional eddy viscosity models in compressible turbulent channel flows with untrained Reynolds numbers, Mach numbers and grid resolutions. The MANA model has a better performance in predicting the flow statistics in supersonic turbulent boundary layer. The MANA model can well predict both direct and inverse transfer of the kinetic energy and temperature variance, which overcomes the inherent shortcoming that the traditional eddy viscosity models cannot predict the inverse energy transfer. Moreover, the MANA model is computationally more efficient than the DSM.
分类号一类/力学重要期刊
WOS研究方向Mechanics ; Physics, Fluids & Plasmas
语种英语
WOS记录号WOS:000960160100001
资助机构NSFC Basic Science Center Program [11988102] ; National Natural Science Foundation of China (NSFC) [91952104, 92052301, 12172161, 91752201] ; Technology and Innovation Commission of Shenzhen Municipality [KQTD20180411143441009, JCYJ20170412151759222] ; Department of Science and Technology of Guangdong Province [2019B21203001] ; Center for Computational Science and Engineering of Southern University of Science and Technology
其他责任者Chen, SY (corresponding author), Peking Univ, Coll Engn, State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China. ; Wang, JC ; Chen, SY (corresponding author), Southern Univ Sci & Technol, Dept Mech & Aerosp Engn, Shenzhen 518055, Peoples R China. ; Chen, SY (corresponding author), Eastern Inst Adv Study, Ningbo 315200, Peoples R China.
源URL[http://dspace.imech.ac.cn/handle/311007/91813]  
专题力学研究所_高温气体动力学国家重点实验室
作者单位1.Chinese Acad Sci, Inst Mech, Lab High Temp Gas Dynam, Beijing 100190, Peoples R China
2.Southern Univ Sci & Technol, Dept Mech & Aerosp Engn, Shenzhen 518055, Peoples R China
3.Peking Univ, Coll Engn, State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China
4.Eastern Inst Adv Study, Ningbo 315200, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Dehao,Wang, Jianchun,Yu ZP,et al. Artificial neural network based nonlinear algebraic models for large eddy simulation of compressible wall bounded turbulence[J]. JOURNAL OF FLUID MECHANICS,2023,960:A4.
APA Xu, Dehao,Wang, Jianchun,于长平,&Chen, Shiyi.(2023).Artificial neural network based nonlinear algebraic models for large eddy simulation of compressible wall bounded turbulence.JOURNAL OF FLUID MECHANICS,960,A4.
MLA Xu, Dehao,et al."Artificial neural network based nonlinear algebraic models for large eddy simulation of compressible wall bounded turbulence".JOURNAL OF FLUID MECHANICS 960(2023):A4.

入库方式： OAI收割

来源：力学研究所

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