Multigrid sequential data assimilation for the Large Eddy Simulation of a massively separated bluff-body flow

被引:0
作者
Moldovan, Gabriel-Ionut [1 ]
Mariotti, Alessandro [2 ]
Cordier, Laurent [3 ]
Lehnasch, Guillaume [3 ]
Salvetti, Maria-Vittoria [2 ]
Meldi, Marcello [4 ]
机构
[1] European Ctr Medium Range Weather Forecasts ECMWF, Shinfield Pk, Reading RG2 9AX, England
[2] Univ Pisa, DICI, Pisa, Italy
[3] Univ Poitiers, Inst Pprime, Dept Fluides Therm & Combust, CNRS,ENSMA, F-86360 Futuroscope, France
[4] Univ Lille, CNRS, Arts & Metiers ParisTech, ONERA,Cent Lille,UMR 9014,LMFL,ampe Feriet, F-59000 Lille, France
关键词
Data Assimilation; LES; MGEnKF; BARC; RECTANGULAR; 5/1; CYLINDER; ENSEMBLE KALMAN FILTER; SENSITIVITY-ANALYSIS; DATA-DRIVEN; MODEL; ERRORS; LES;
D O I
10.1016/j.compfluid.2024.106385
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The potential of sequential Data Assimilation (DA) techniques to improve the numerical accuracy of Large Eddy Simulation (LES) performed on coarse grid is assessed. Specifically, this paper evaluates the performance of the Multigrid Ensemble Kalman Filter (MGEnKF) method, recently introduced by Moldovan, Lehnasch, Cordier and Meldi (Journal of Computational Physics, 2021). The international benchmark referred to as BARC (Benchmark of the Aerodynamics of a Rectangular 5:1 Cylinder) is chosen as test configuration, as it includes several complex flow dynamics encountered in turbulence studies. The results for the statistical moments of the velocity and pressure flow field show that the data-driven techniques employed are able to significantly improve the predictive features of the solver for reduced grid resolution. In addition, it was observed that, despite the sparse and asymmetric distribution of observation in the data-driven process, the DA augmented LES exhibits symmetric statistics and a significantly improved accuracy also far from the observation zone.
引用
收藏
页数:19
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