南方科技大学知识苑(SUSTech KC): Artificial neural network-based nonlinear algebraic models for large eddy simulation of turbulence

题名	Artificial neural network-based nonlinear algebraic models for large eddy simulation of turbulence
作者	Xie，Chenyue; Yuan，Zelong; Wang，Jianchun
通讯作者	Wang，Jianchun
发表日期	2020-11-01
DOI	10.1063/5.0025138
发表期刊	PHYSICS OF FLUIDS 影响因子和分区
ISSN	1070-6631
EISSN	1089-7666
卷号	32 期号:11
摘要	In this work, artificial neural network-based nonlinear algebraic models (ANN-NAMs) are developed for the subgrid-scale (SGS) stress in large eddy simulation (LES) of turbulence at the Taylor Reynolds number Reλ ranging from 180 to 250. An ANN architecture is applied to construct the coefficients of the general NAM for the SGS anisotropy stress. It is shown that the ANN-NAMs can reconstruct the SGS stress accurately in the a priori test. Furthermore, the ANN-NAMs are analyzed by calculating the average, root mean square values, and probability density functions of dimensionless model coefficients. In an a posteriori analysis, we compared the performance of the dynamic Smagorinsky model (DSM), dynamic mixed model (DMM), and ANN-NAM. The ANN-NAM yields good agreement with a filtered direct numerical simulation dataset for the spectrum, structure functions, and other statistics of velocity. Besides, the ANN-NAM predicts the instantaneous spatial structures of SGS anisotropy stress much better than the DSM and DMM. The NAM based on the ANN is a promising approach to deepen our understanding of SGS modeling in LES of turbulence.
相关链接	[Scopus记录]
收录类别	SCI ; EI
语种	英语
学校署名	第一 ; 通讯
资助项目	National Numerical Windtunnel Project[NNW2019ZT1-A04] ; National Natural Science Foundation of China (NSFC)[91952104][11702127][91752201] ; Technology and Innovation Commission of Shenzhen Municipality[KQTD20180411143441009][JCYJ20170412151759222] ; Department of Science and Technology of Guangdong Province[2019B21203001] ; Young Elite Scientist Sponsorship Program by CAST[2016QNRC001]
WOS研究方向	Mechanics ; Physics
WOS类目	Mechanics ; Physics, Fluids & Plasmas
WOS记录号	WOS:000589619000001
出版者	AMER INST PHYSICS
EI入藏号	20204609481228
EI主题词	Probability density function ; Reynolds number ; Neural networks ; Algebra ; Anisotropy ; Turbulence
EI分类号	Fluid Flow:631 ; Fluid Flow, General:631.1 ; Mathematics:921 ; Algebra:921.1 ; Probability Theory:922.1 ; Physical Properties of Gases, Liquids and Solids:931.2
ESI学科分类	PHYSICS
Scopus记录号	2-s2.0-85095756904
来源库	Scopus
引用统计	被引频次[WOS]：65
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/209122
专题	工学院_力学与航空航天工程系
作者单位	Guangdong Provincial Key Laboratory of Turbulence Research and Applications,Center for Complex Flows and Soft Matter Research,Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Shenzhen,518055,China
第一作者单位	力学与航空航天工程系
通讯作者单位	力学与航空航天工程系
第一作者的第一单位	力学与航空航天工程系
推荐引用方式 GB/T 7714	Xie，Chenyue,Yuan，Zelong,Wang，Jianchun. Artificial neural network-based nonlinear algebraic models for large eddy simulation of turbulence[J]. PHYSICS OF FLUIDS,2020,32(11).
APA	Xie，Chenyue,Yuan，Zelong,&Wang，Jianchun.(2020).Artificial neural network-based nonlinear algebraic models for large eddy simulation of turbulence.PHYSICS OF FLUIDS,32(11).
MLA	Xie，Chenyue,et al."Artificial neural network-based nonlinear algebraic models for large eddy simulation of turbulence".PHYSICS OF FLUIDS 32.11(2020).