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Data-driven turbulence model for unsteady cavitating flow
Zhang Z(张珍); Wang JZ(王静竹); Huang RF(黄仁芳); Qiu RD(丘润荻); Chu, Xuesen; Ye SR(叶舒然); Wang YW(王一伟); Liu, Qingkuan
通讯作者Wang, Yiwei(wangyw@imech.ac.cn)
发表期刊PHYSICS OF FLUIDS
2023
卷号35期号:1页码:18
ISSN1070-6631
摘要Unsteady Reynolds-averaged Navier-Stokes (URANS) equations have been widely used in engineering fields to investigate cavitating flow owing to their low computational cost and excellent robustness. However, it is challenging to accurately obtain the unsteady characteristics of flow owing to cavitation-induced phase transitions. In this study, we propose an implicit data-driven URANS (DD-URANS) framework to analyze the unsteady characteristics of cavitating flow. In the DD-URANS framework, a basic computational model is developed by introducing a cavitation-induced phase transition into the equations of Reynolds stress. To improve the computational accuracy and generalization performance of the basic model, the linear and nonlinear parts of the anisotropic Reynolds stress are predicted through implicit and explicit methods, respectively. A data fusion approach, allowing the input and output of characterized parameters at multiple time points, is presented to obtain the unsteady characteristics of the cavitating flow. The DD-URANS model is trained using the numerical results obtained via large-eddy simulation. The training data consist of two parts: (i) the results obtained at cavitation numbers of 2.0, 2.2, and 2.7 for a Venturi flow, and (ii) those obtained at cavitation numbers of 0.8 and 1.5 for a National Advisory Committee for Aeronautics (NACA) 66 hydrofoil. The DD-URANS model is used to predict the cavitating flow at cavitation numbers of 2.5 for a Venturi flow and 0.8 for a Clark-Y hydrofoil. It is found that the DD-URANS model is superior to the baseline URANS model in predicting the instantaneous periodic shedding of a cavity and the mean flow fields.
DOI10.1063/5.0134992
收录类别SCI ; EI
语种英语
WOS记录号WOS:000912891900005
关键词[WOS]LARGE-EDDY SIMULATION ; UNCERTAINTY ; DYNAMICS ; TRANSPORT ; NETWORKS ; CLOSURE ; NUMBER
WOS研究方向Mechanics ; Physics
WOS类目Mechanics ; Physics, Fluids & Plasmas
资助项目National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS ; [12202291] ; [12122214] ; [12272382] ; [12293000] ; [12293003] ; [12293004] ; [2022019]
项目资助者National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS
论文分区一类/力学重要期刊
力学所作者排名1
RpAuthorWang, Yiwei
引用统计
被引频次:10[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://dspace.imech.ac.cn/handle/311007/91544
专题流固耦合系统力学重点实验室
推荐引用方式
GB/T 7714
Zhang Z,Wang JZ,Huang RF,et al. Data-driven turbulence model for unsteady cavitating flow[J]. PHYSICS OF FLUIDS,2023,35,1,:18.
APA 张珍.,王静竹.,黄仁芳.,丘润荻.,Chu, Xuesen.,...&Liu, Qingkuan.(2023).Data-driven turbulence model for unsteady cavitating flow.PHYSICS OF FLUIDS,35(1),18.
MLA 张珍,et al."Data-driven turbulence model for unsteady cavitating flow".PHYSICS OF FLUIDS 35.1(2023):18.
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