Title
Recovering turbulent flow field from local quantity measurement: turbulence modeling using ensemble-Kalman-filter-based data assimilation.
Abstract
This paper is focused on the recovery of the global flow field through data assimilation of local flow quantity measurement and Reynolds-averaged Navier–Stokes (RANS) modeling. Particular attention is given to the optimization of various RANS model constants using the ensemble Kalman filter (EnKF) approach. To this end, a free round jet at Reynolds number Re = 6000 is experimentally measured using particle image velocimetry (PIV), serving as the observation data and validation purpose. A total of four different RANS models are separately employed as system models in the data assimilation, i.e., the Spalart–Allmaras, \(k - \varepsilon\), \(k - \omega\), and shear stress transport models. The results convincingly demonstrate that all models with EnKF augmentation are considerably improved compared with their original counterparts. Among all models, the \(k - \varepsilon\) model with EnKF augmentation showed the best performance in predicating the time-averaged flow quantities. Subsequently, the \(k - \varepsilon\) model with EnKF augmentation is examined to demonstrate its robustness and sensitivity for different observational data. Three different selection strategies of observational data are documented here: the velocity distributions in a region, along a line, and at a single point. For all of these selections, the observational data in the jet transition region are shown to be the best candidate for flow field recovery.
Year
DOI
Venue
2018
10.1007/s12650-018-0508-0
J. Visualization
Keywords
Field
DocType
Data assimilation,Ensemble Kalman filter,RANS models,Turbulent jet flow,Turbulence model constants optimization,PIV
Statistical physics,Turbulence,Turbulence modeling,Data assimilation,Ensemble Kalman filter,Classical mechanics,Physics
Journal
Volume
Issue
ISSN
21
6
1343-8875
Citations 
PageRank 
References 
0
0.34
3
Authors
4
Name
Order
Citations
PageRank
Zhiwen Deng100.34
Chuangxin He202.37
xin wen311.64
Yingzheng Liu436.21