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Uncertainty quantification in LES of channel flow
Author(s) -
Safta Cosmin,
Blaylock Myra,
Templeton Jeremy,
Domino Stefan,
Sargsyan Khachik,
Najm Habib
Publication year - 2016
Publication title -
international journal for numerical methods in fluids
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.938
H-Index - 112
eISSN - 1097-0363
pISSN - 0271-2091
DOI - 10.1002/fld.4272
Subject(s) - large eddy simulation , turbulence , calibration , direct numerical simulation , scale (ratio) , mathematics , statistical physics , filter (signal processing) , open channel flow , isotropy , channel (broadcasting) , grid , flow (mathematics) , noise (video) , algorithm , physics , computer science , mechanics , statistics , geometry , optics , artificial intelligence , computer network , quantum mechanics , reynolds number , computer vision , image (mathematics)
Summary In this paper, we present a Bayesian framework for estimating joint densities for large eddy simulation (LES) sub‐grid scale model parameters based on canonical forced isotropic turbulence direct numerical simulation (DNS) data. The framework accounts for noise in the independent variables, and we present alternative formulations for accounting for discrepancies between model and data. To generate probability densities for flow characteristics, posterior densities for sub‐grid scale model parameters are propagated forward through LES of channel flow and compared with DNS data. Synthesis of the calibration and prediction results demonstrates that model parameters have an explicit filter width dependence and are highly correlated. Discrepancies between DNS and calibrated LES results point to additional model form inadequacies that need to be accounted for. Copyright © 2016 John Wiley & Sons, Ltd.