A new dynamic subgrid-scale model using artificial neural network for compressible flow

doi:10.1016/j.taml.2022.100359

	A new dynamic subgrid-scale model using artificial neural network for compressible flow
	Qi H(齐涵); Li XL(李新亮); Luo, Ning 2; Yu ZP(于长平)
发表期刊	THEORETICAL AND APPLIED MECHANICS LETTERS
	2022-05
卷号	12 期号:4
ISSN	2095-0349
摘要	The subgrid-scale (SGS) kinetic energy has been used to predict the SGS stress in compressible flow and it was resolved through the SGS kinetic energy transport equation in past studies. In this paper, a new SGS eddy-viscosity model is proposed using artificial neural network to obtain the SGS kinetic energy precisely, instead of using the SGS kinetic energy equation. Using the infinite series expansion and reserving the first term of the expanded term, we obtain an approximated SGS kinetic energy, which has a high correlation with the real SGS kinetic energy. Then, the coefficient of the modelled SGS kinetic energy is resolved by the artificial neural network and the modelled SGS kinetic energy is more accurate through this method compared to the SGS kinetic energy obtained from the SGS kinetic energy equation. The coefficients of the SGS stress and SGS heat flux terms are determined by the dynamic procedure. The new model is tested in the compressible turbulent channel flow. From the a posterior tests, we know that the new model can precisely predict the mean velocity, the Reynolds stress, the mean temperature and turbulence intensities, etc. (c) 2022 The Authors. Published by Elsevier Ltd on behalf of The Chinese Society of Theoretical and Applied Mechanics. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
关键词	Subgrid-scale kinetic energy Eddy-viscosity model Compressible flow
DOI	10.1016/j.taml.2022.100359
收录类别	CSCD
语种	英语
WOS研究方向	Mechanics
项目资助者	National Key Research and Development Program of China [2020YFA0711800, 2019YFA0405302] ; NSFC [12072349, 91852203] ; National Numerical Windtunnel Project, Science Challenge Project [TZ2016001] ; Strategic Priority Re-search Program of Chinese Academy of Sciences [XDC01000000]
论文分区	二类
力学所作者排名	1
RpAuthor	Yu, CP (corresponding author), Chinese Acad Sci, Inst Mech, LHD, Beijing 100190, Peoples R China. ; Luo, N (corresponding author), China Univ Min & Technol, State Key Lab Geomech & Deep Underground Engn, Xuzhou 221116, Jiangsu, Peoples R China.
引用统计
文献类型	期刊论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/93690
专题	高温气体动力学国家重点实验室
作者单位	1.Chinese Acad Sci, Inst Mech, LHD, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China 3.China Univ Min & Technol, State Key Lab Geomech & Deep Underground Engn, Xuzhou 221116, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Qi H,Li XL,Luo, Ning,et al. A new dynamic subgrid-scale model using artificial neural network for compressible flow[J]. THEORETICAL AND APPLIED MECHANICS LETTERS,2022,12,4,.
APA	齐涵,李新亮,Luo, Ning,&于长平.(2022).A new dynamic subgrid-scale model using artificial neural network for compressible flow.THEORETICAL AND APPLIED MECHANICS LETTERS,12(4).
MLA	齐涵,et al."A new dynamic subgrid-scale model using artificial neural network for compressible flow".THEORETICAL AND APPLIED MECHANICS LETTERS 12.4(2022).

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