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Assimilation of disparate data for enhanced reconstruction of turbulent mean flows

文献类型：期刊论文

作者Zhang, Xin-Lei2,3; Xiao, Heng1; He GW(何国威)2,3; Wang SZ(王士召)2,3
刊名COMPUTERS & FLUIDS
出版日期2021-06-30
卷号224页码:14
关键词Turbulent flow reconstruction Disparate data sources Ensemble Kalman method Data assimilation
ISSN号0045-7930
DOI10.1016/j.compfluid.2021.104962
通讯作者He, Guo-Wei(hgw@lnm.imech.ac.cn)
英文摘要Reconstruction of turbulent flow based on data assimilation methods is of significant importance for improving the estimation of flow characteristics by incorporating limited observations. Existing works mainly focus on using only one observation data source, e.g., velocity, wall pressure, lift or drag force, to reconstruct the flow. In practical applications observations are disparate data sources that often vary in dimension and quality. Simultaneously incorporating these disparate data is worth investigation to improve the flow reconstruction. In this work, we investigate the disparate data assimilation with ensemble methods to enhance the reconstruction of turbulent mean flows. Specifically, a regularized ensemble Kalman method is employed to incorporate the observation of velocity and different sources of wall quantities (e.g., wall shear stress, wall pressure distribution, lift and drag force). Three numerical examples are used to demonstrate the capability of the proposed framework for assimilating disparate observation data. The first two cases, i.e., a one-dimensional planar channel flow and a two-dimensional transitional flow over plate, are used to incorporate both the sparse velocity and wall friction. In the third case of the flow over periodic hills, the wall pressure distribution and the lift and drag force are regarded as observation in addition to velocity, to recover the flow fields. The results demonstrate the merits of incorporating various disparate data sources to improve the accuracy of the flow-field estimation. The ensemble-based method can assimilate disparate data non-intrusively and robustly without requiring significant changes to the model simulation codes. The method demonstrated here opens up possibilities for assimilating realistic experimental data, which are often disparate. (C) 2021 Elsevier Ltd. All rights reserved.
分类号二类
WOS关键词STATE ESTIMATION ; KALMAN-FILTER ; MODEL ; SIMULATION ; CHANNEL
资助项目NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics[11988102] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB22040104] ; Key Research Program of Frontier Sciences of the Chinese Academy of Sciences[QYZDJ-SSWSYS002]
WOS研究方向Computer Science ; Mechanics
语种英语
WOS记录号WOS:000654237100004
资助机构NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Key Research Program of Frontier Sciences of the Chinese Academy of Sciences
其他责任者He, Guo-Wei
源URL[http://dspace.imech.ac.cn/handle/311007/86901]  
专题力学研究所_非线性力学国家重点实验室
作者单位1.Virginia Tech, Kevin T Crofton Dept Aerosp & Ocean Engn, Blacksburg, VA 24060 USA
2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China;
3.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China;
推荐引用方式
GB/T 7714		 Zhang, Xin-Lei,Xiao, Heng,He GW,et al. Assimilation of disparate data for enhanced reconstruction of turbulent mean flows[J]. COMPUTERS & FLUIDS,2021,224:14.
APA Zhang, Xin-Lei,Xiao, Heng,何国威,&王士召.(2021).Assimilation of disparate data for enhanced reconstruction of turbulent mean flows.COMPUTERS & FLUIDS,224,14.
MLA Zhang, Xin-Lei,et al."Assimilation of disparate data for enhanced reconstruction of turbulent mean flows".COMPUTERS & FLUIDS 224(2021):14.

入库方式： OAI收割

来源：力学研究所

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