南方科技大学知识苑(SUSTech KC): Artificial neural network model for large-eddy simulation of compressible turbulence 基于人工神经网络的可压缩湍流大涡模拟模型

题名	Artificial neural network model for large-eddy simulation of compressible turbulence 基于人工神经网络的可压缩湍流大涡模拟模型
作者	Xie，Chenyue1,2 ; Wang，Jianchun1,2 ; Wan，Minping1,2 ; Chen，Shiyi1,2
通讯作者	Wang，Jianchun
发表日期	2021-09-25
DOI	10.7527/S1000-6893.2021.25723
发表期刊	航空学报影响因子和分区
ISSN	1000-6893
卷号	42 期号:9
摘要	The Spatial Artificial Neural Network (SANN) model is applied to perform Large Eddy Simulations (LES) of highly compressible turbulence at high turbulent Mach numbers of 0.6, 0.8 and 1.0 under the National Numerical Windtunnel (NNW) Project. In our previous studies, we developed the SANN model for incompressible and weakly compressible turbulence based on multi-scale spatial structures of turbulence. However, generations of shock waves in highly compressible turbulence pose great challenges to LES. This paper discusses the applicability of the SANN models for LES of highly compressible turbulence. It has been demonstrated that the correlation coefficients of the SANN model can be larger than 0.995. The relative errors of the SANN model can be smaller than 11%, which are much smaller than those of the traditional gradient model and the approximate deconvolution model in an a priori analysis for highly compressible turbulence. In an a posteriori analysis, we make a comparison of the results of the SANN model, the Implicit Large Eddy Simulation (ILES), the Dynamic Smagorinsky Model (DSM) and the Dynamic Mixed Model (DMM). It is shown that the SANN model performs better in the prediction of spectra and statistical properties of velocity and temperature, and instantaneous flow structures for highly compressible turbulence. The artificial neural network model with consideration of spatial features can deepen our understanding of subgrid-scale modeling for LES of highly compressible turbulence. At the same time, the model can contribute to the construction of the turbulence models of the NNW Project.
关键词	Artificial neural network Compressible turbulence Large eddy simulation National Numerical Windtunnel (NNW) Project Turbulence model
相关链接	[Scopus记录]
收录类别	EI
语种	中文
学校署名	第一 ; 通讯
EI入藏号	20214411107903
EI主题词	Incompressible flow ; Mach number ; Neural networks ; Shock waves ; Turbulence models
EI分类号	Fluid Flow:631 ; Mathematics:921 ; Classical Physics; Quantum Theory; Relativity:931 ; Mechanical Variables Measurements:943.2
Scopus记录号	2-s2.0-85118298344
来源库	Scopus
引用统计	被引频次[WOS]：0
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/254853
专题	工学院_力学与航空航天工程系工学院
作者单位	1.Department of Mechanics and Aerospace Engineering,College of Engineering,Southern University of Science and Technology,Shenzhen,518055,China 2.Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications,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; 工学院; 南方科技大学
通讯作者单位	力学与航空航天工程系; 工学院; 南方科技大学
第一作者的第一单位	力学与航空航天工程系; 工学院
推荐引用方式 GB/T 7714	Xie，Chenyue,Wang，Jianchun,Wan，Minping,等. Artificial neural network model for large-eddy simulation of compressible turbulence 基于人工神经网络的可压缩湍流大涡模拟模型[J]. 航空学报,2021,42(9).
APA	Xie，Chenyue,Wang，Jianchun,Wan，Minping,&Chen，Shiyi.(2021).Artificial neural network model for large-eddy simulation of compressible turbulence 基于人工神经网络的可压缩湍流大涡模拟模型.航空学报,42(9).
MLA	Xie，Chenyue,et al."Artificial neural network model for large-eddy simulation of compressible turbulence 基于人工神经网络的可压缩湍流大涡模拟模型".航空学报 42.9(2021).