Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence

doi:10.1103/PhysRevE.99.053113

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	Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence
	Xie, Chenyue 1; Wang, Jianchun 1; Li, Ke 2; Ma, Chao 3
	2019-05-21
发表期刊	PHYSICAL REVIEW E
ISSN	2470-0045
卷号	99 期号:5 页码:21
摘要	A subgrid-scale (SGS) model for large-eddy simulation (LES) of compressible isotropic turbulence is constructed by using a data-driven framework. An artificial neural network (ANN) based on local stencil geometry is employed to predict the unclosed SGS terms. The input features are based on the first-order and second-order derivatives of filtered velocity and temperature which appear in the second-order Taylor approximation of the SGS stress and heat flux. It is shown that the proposed ANN-7 model performs better than the gradient model in the a priori test. The correlation coefficient is larger and the relative error is smaller for ANN-7 model as compared to those of the gradient model in the a priori test. In an a posteriori analysis, the performance of ANN-7 model shows advantage over the dynamic Smagorinsky model and dynamic mixed model in the prediction of spectra and structure functions of velocity and temperature, and instantaneous flow structures. Artificial neural network is a promising tool for understanding the physical fundamentals of SGS unclosed terms with further improvement.
DOI	10.1103/PhysRevE.99.053113
语种	英语
资助项目	National Natural Science Foundation of China (NSFC)[11702127] ; National Natural Science Foundation of China (NSFC)[91752201] ; Technology and Innovation Commission of Shenzhen Municipality[JCYJ20170412151759222] ; Young Elite Scientist Sponsorship Program by CAST[2016QNRC001]
WOS研究方向	Physics
WOS类目	Physics, Fluids & Plasmas ; Physics, Mathematical
WOS记录号	WOS:000469027500006
出版者	AMER PHYSICAL SOC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/34829
专题	中国科学院数学与系统科学研究院
通讯作者	Wang, Jianchun
作者单位	1.Southern Univ Sci & Technol, Dept Mech & Aerosp Engn, Shenzhen 518055, Peoples R China 2.Chinese Acad Sci, Inst Computat Math & Sci Engn Comp, Beijing 100190, Peoples R China 3.Princeton Univ, Program Appl & Computat Math, Princeton, NJ 08544 USA
推荐引用方式 GB/T 7714	Xie, Chenyue,Wang, Jianchun,Li, Ke,et al. Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence[J]. PHYSICAL REVIEW E,2019,99(5):21.
APA	Xie, Chenyue,Wang, Jianchun,Li, Ke,&Ma, Chao.(2019).Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence.PHYSICAL REVIEW E,99(5),21.
MLA	Xie, Chenyue,et al."Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence".PHYSICAL REVIEW E 99.5(2019):21.