Uncertainty quantification in LES of channel flow
Abstract
Here, 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.
- Authors:
-
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1427243
- Report Number(s):
- SAND-2015-0938J
Journal ID: ISSN 0271-2091; 566911
- Grant/Contract Number:
- AC04-94AL85000
- Resource Type:
- Accepted Manuscript
- Journal Name:
- International Journal for Numerical Methods in Fluids
- Additional Journal Information:
- Journal Volume: 83; Journal Issue: 4; Journal ID: ISSN 0271-2091
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; large eddy simulation; Bayesian framework; calibration; model error; polynomial chaos; Rosenblatt transformation
Citation Formats
Safta, Cosmin, Blaylock, Myra, Templeton, Jeremy, Domino, Stefan, Sargsyan, Khachik, and Najm, Habib. Uncertainty quantification in LES of channel flow. United States: N. p., 2016.
Web. doi:10.1002/fld.4272.
Safta, Cosmin, Blaylock, Myra, Templeton, Jeremy, Domino, Stefan, Sargsyan, Khachik, & Najm, Habib. Uncertainty quantification in LES of channel flow. United States. https://doi.org/10.1002/fld.4272
Safta, Cosmin, Blaylock, Myra, Templeton, Jeremy, Domino, Stefan, Sargsyan, Khachik, and Najm, Habib. Tue .
"Uncertainty quantification in LES of channel flow". United States. https://doi.org/10.1002/fld.4272. https://www.osti.gov/servlets/purl/1427243.
@article{osti_1427243,
title = {Uncertainty quantification in LES of channel flow},
author = {Safta, Cosmin and Blaylock, Myra and Templeton, Jeremy and Domino, Stefan and Sargsyan, Khachik and Najm, Habib},
abstractNote = {Here, 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.},
doi = {10.1002/fld.4272},
journal = {International Journal for Numerical Methods in Fluids},
number = 4,
volume = 83,
place = {United States},
year = {Tue Jul 12 00:00:00 EDT 2016},
month = {Tue Jul 12 00:00:00 EDT 2016}
}
Web of Science
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