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A semi-implicit discrepancy model of Reynolds stress in a higher-order tensor basis framework for Reynolds-averaged Navier-Stokes simulations

文献类型：期刊论文

作者Zhang, Zhen3,4; Ye, Shuran3,4; Yin, Bo3,4; Song, Xudong2; Wang, Yiwei1,3,4; Huang, Chenguang1,3,4; Chen, Yaosong2; Huang CG(黄晨光); Zhang Z(张珍); Zhang Z(张珍)
刊名AIP ADVANCES
出版日期2021-04-01
卷号11期号:4页码:17
DOI10.1063/5.0033109
通讯作者Wang, Yiwei(wangyw@imech.ac.cn)
英文摘要With the rapid development of artificial intelligence, machine learning algorithms are becoming more widely applied in the modification of turbulence models. In this paper, with the aim of improving the prediction accuracy of the Reynolds-averaged Navier-Stokes (RANS) model, a semi-implicit treatment of Reynolds stress anisotropy discrepancy model is developed using a higher-order tensor basis. A deep neural network is constructed and trained based on this discrepancy model. The trained model parameters are embedded in a computational fluid dynamics solver to modify the original RANS model. Modification computations are performed for two cases: one interpolation and one extrapolation of different Reynolds numbers. For these two cases, the ability of the modified model to capture anisotropic features has been improved. Moreover, when compared with the mean velocity of large eddy simulations (LES), the root mean square error of the modified model is significantly lower than the original RANS model. Meanwhile, the modified model can better simulate flow field separation and fluctuation in the shear layer and has better prediction accuracy for the reattachment point and the mean velocity profile compared with the original RANS model. In addition, the modified model also improves the prediction accuracy for the mean pressure coefficient and mean friction coefficient of the underlying wall surface. The previously trained model is also directly performed for the modification computation of the two massive separation periodic hill flows. It is shown that the results simulated by the modified model and LES approach are more consistent in both trend and magnitude than the original RANS model and LES approach.
WOS关键词TURBULENCE ; FLOW ; NETWORK ; CLOSURE ; CHANNEL
资助项目National Natural Science Foundation of China[11772340] ; National Natural Science Foundation of China[11802311] ; National Natural Science Foundation of China[11672315] ; Youth Innovation Promotion Association CAS[2015015]
WOS研究方向Science & Technology - Other Topics ; Materials Science ; Physics
语种英语
WOS记录号WOS:000642011000001
资助机构National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS
源URL[http://dspace.imech.ac.cn/handle/311007/86578]  
专题力学研究所_流固耦合系统力学重点实验室(2012-)
通讯作者Wang, Yiwei
作者单位1.Univ Chinese Acad Sci, Sch Future Technol, Beijing 100049, Peoples R China
2.Peking Univ, Coll Engn, Beijing 100871, Peoples R China
3.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Zhen,Ye, Shuran,Yin, Bo,et al. A semi-implicit discrepancy model of Reynolds stress in a higher-order tensor basis framework for Reynolds-averaged Navier-Stokes simulations[J]. AIP ADVANCES,2021,11(4):17.
APA Zhang, Zhen.,Ye, Shuran.,Yin, Bo.,Song, Xudong.,Wang, Yiwei.,...&王一伟.(2021).A semi-implicit discrepancy model of Reynolds stress in a higher-order tensor basis framework for Reynolds-averaged Navier-Stokes simulations.AIP ADVANCES,11(4),17.
MLA Zhang, Zhen,et al."A semi-implicit discrepancy model of Reynolds stress in a higher-order tensor basis framework for Reynolds-averaged Navier-Stokes simulations".AIP ADVANCES 11.4(2021):17.

入库方式： OAI收割

来源：力学研究所

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