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

文献类型：期刊论文

作者Qi H(齐涵); Li XL(李新亮); Luo, Ning2; Yu ZP(于长平)
刊名THEORETICAL AND APPLIED MECHANICS LETTERS
出版日期2022-05
卷号12期号:4
ISSN号2095-0349
关键词Subgrid-scale kinetic energy Eddy-viscosity model Compressible flow
DOI10.1016/j.taml.2022.100359
英文摘要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/)
分类号二类
WOS研究方向Mechanics
语种英语
资助机构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]
其他责任者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.
源URL[http://dspace.imech.ac.cn/handle/311007/93690]  
专题力学研究所_高温气体动力学国家重点实验室
作者单位1.China Univ Min & Technol, State Key Lab Geomech & Deep Underground Engn, Xuzhou 221116, Jiangsu, Peoples R China
2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Mech, LHD, Beijing 100190, 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).

入库方式： OAI收割

来源：力学研究所

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