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.
Safta, Cosmin, et al. "Uncertainty quantification in LES of channel flow." International Journal for Numerical Methods in Fluids, vol. 83, no. 4, Jul. 2016. https://doi.org/10.1002/fld.4272
Safta, Cosmin, Blaylock, Myra, Templeton, Jeremy, Domino, Stefan, Sargsyan, Khachik, & Najm, Habib (2016). Uncertainty quantification in LES of channel flow. International Journal for Numerical Methods in Fluids, 83(4). https://doi.org/10.1002/fld.4272
Safta, Cosmin, Blaylock, Myra, Templeton, Jeremy, et al., "Uncertainty quantification in LES of channel flow," International Journal for Numerical Methods in Fluids 83, no. 4 (2016), https://doi.org/10.1002/fld.4272
@article{osti_1427243,
author = {Safta, Cosmin and Blaylock, Myra and Templeton, Jeremy and Domino, Stefan and Sargsyan, Khachik and Najm, Habib},
title = {Uncertainty quantification in LES of channel flow},
annote = {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},
url = {https://www.osti.gov/biblio/1427243},
journal = {International Journal for Numerical Methods in Fluids},
issn = {ISSN 0271-2091},
number = {4},
volume = {83},
place = {United States},
publisher = {Wiley},
year = {2016},
month = {07}}
Sandia National Laboratories (SNL-CA), Livermore, CA (United States); Sandia National Laboratories, Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000
OSTI ID:
1427243
Report Number(s):
SAND--2015-0938J; 566911
Journal Information:
International Journal for Numerical Methods in Fluids, Journal Name: International Journal for Numerical Methods in Fluids Journal Issue: 4 Vol. 83; ISSN 0271-2091