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

doi:10.1017/jfm.2023.179

	Artificial neural network based nonlinear algebraic models for large eddy simulation of compressible wall bounded turbulence
	Xu, Dehao ; Wang, Jianchun 1; Yu ZP(于长平); Chen, Shiyi 1,3
发表期刊	JOURNAL OF FLUID MECHANICS
	2023-03-29
卷号	960 页码:A4
ISSN	0022-1120
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.
关键词	machine learning turbulence modelling
DOI	10.1017/jfm.2023.179
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000960160100001
WOS研究方向	Mechanics ; Physics, Fluids & Plasmas
WOS类目	Mechanics ; Physics
项目资助者	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
论文分区	一类/力学重要期刊
力学所作者排名	3
RpAuthor	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.
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/91813
专题	高温气体动力学国家重点实验室
作者单位	1.Peking Univ, Coll Engn, State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China 2.Southern Univ Sci & Technol, Dept Mech & Aerosp Engn, Shenzhen 518055, Peoples R China 3.Chinese Acad Sci, Inst Mech, Lab High Temp Gas Dynam, Beijing 100190, 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.

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