Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence
文献类型：期刊论文
| 作者 | Xie, Chenyue3; Wang, Jianchun3; Li, Ke2; Ma, Chao1 |
| 刊名 | PHYSICAL REVIEW E
 |
| 出版日期 | 2019-05-21 |
| 卷号 | 99期号:5页码:21 |
| ISSN号 | 2470-0045 |
| DOI | 10.1103/PhysRevE.99.053113 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000469027500006 |
| 出版者 | AMER PHYSICAL SOC |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/34828]  |
| 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.Princeton Univ, Program Appl & Computat Math, Princeton, NJ 08544 USA 2.Chinese Acad Sci, Inst Computat Math & Sci Engn Comp, Beijing 100190, Peoples R China 3.Southern Univ Sci & Technol, Dept Mech & Aerosp Engn, Shenzhen 518055, Peoples R China |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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