| A new dynamic subgrid-scale model using artificial neural network for compressible flow | |
Qi H(齐涵); Li XL(李新亮) ; Luo, Ning2; Yu ZP(于长平)
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| Source Publication | THEORETICAL AND APPLIED MECHANICS LETTERS
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| 2022-05 | |
| Volume | 12Issue:4 |
| ISSN | 2095-0349 |
| Abstract | 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/) |
| Keyword | Subgrid-scale kinetic energy Eddy-viscosity model Compressible flow |
| DOI | 10.1016/j.taml.2022.100359 |
| Indexed By | CSCD |
| Language | 英语 |
| WOS Research Area | Mechanics |
| Funding Organization | 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] |
| Classification | 二类 |
| Ranking | 1 |
| Contributor | 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. |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/93690 |
| Collection | 高温气体动力学国家重点实验室 |
| Affiliation | 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 |
| Recommended Citation 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,.Rp_Au: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. |
| 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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| File Name/Size | DocType | Version | Access | License | ||
| TAML007.pdf（2851KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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