Uncertainty quantification in LES of channel flow

Safta, Cosmin; Blaylock, Myra; Templeton, Jeremy; Domino, Stefan; Sargsyan, Khachik; Najm, Habib

doi:10.1002/fld.4272

Title: Uncertainty quantification in LES of channel flow

Journal Article · Tue Jul 12 00:00:00 EDT 2016 · International Journal for Numerical Methods in Fluids

DOI:https://doi.org/10.1002/fld.4272· OSTI ID:1427243

Safta, Cosmin ^[1]; Blaylock, Myra ^[1]; Templeton, Jeremy ^[1]; Domino, Stefan ^[2]; Sargsyan, Khachik ^[1]; Najm, Habib ^[1]

Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

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.

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Research Organization:: Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sandia National Lab. (SNL-NM), 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, Vol. 83, Issue 4; ISSN 0271-2091

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 11 works

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References (43)

Grid-independent large-eddy simulation using explicit filtering Bose, Sanjeeb T.; Moin, Parviz; You, Donghyun Physics of Fluids, Vol. 22, Issue 10 https://doi.org/10.1063/1.3485774	journal	October 2010
The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data Fox, Andrew; Williams, Mathew; Richardson, Andrew D. Agricultural and Forest Meteorology, Vol. 149, Issue 10 https://doi.org/10.1016/j.agrformet.2009.05.002	journal	October 2009
Numerical analysis of the Burgers’ equation in the presence of uncertainty Pettersson, Per; Iaccarino, Gianluca; Nordström, Jan Journal of Computational Physics, Vol. 228, Issue 22 https://doi.org/10.1016/j.jcp.2009.08.012	journal	December 2009
Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics Najm, Habib N. Annual Review of Fluid Mechanics, Vol. 41, Issue 1 https://doi.org/10.1146/annurev.fluid.010908.165248	journal	January 2009
A filtered-wall formulation for large-eddy simulation of wall-bounded turbulence Bhattacharya, Amitabh; Das, Arup; Moser, Robert D. Physics of Fluids, Vol. 20, Issue 11 https://doi.org/10.1063/1.3025884	journal	November 2008
Modeling of structural uncertainties in Reynolds-averaged Navier-Stokes closures Emory, Michael; Larsson, Johan; Iaccarino, Gianluca Physics of Fluids, Vol. 25, Issue 11 https://doi.org/10.1063/1.4824659	journal	November 2013
Uncertainty quantification for porous media flows Christie, Mike; Demyanov, Vasily; Erbas, Demet Journal of Computational Physics, Vol. 217, Issue 1 https://doi.org/10.1016/j.jcp.2006.01.026	journal	September 2006
Bridging between eddy-viscosity-type and second-order turbulence models through a two-scale turbulence theory Yoshizawa, Akira Physical Review E, Vol. 48, Issue 1 https://doi.org/10.1103/PhysRevE.48.273	journal	July 1993
Bayesian Posterior Predictive Checks for Complex Models Lynch, Scott M.; Western, Bruce Sociological Methods & Research, Vol. 32, Issue 3 https://doi.org/10.1177/0049124103257303	journal	February 2004
Bayesian uncertainty analysis with applications to turbulence modeling Cheung, Sai Hung; Oliver, Todd A.; Prudencio, Ernesto E. Reliability Engineering & System Safety, Vol. 96, Issue 9 https://doi.org/10.1016/j.ress.2010.09.013	journal	September 2011
The Homogeneous Chaos Wiener, Norbert American Journal of Mathematics, Vol. 60, Issue 4 https://doi.org/10.2307/2371268	journal	October 1938
The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations Xiu, Dongbin; Karniadakis, George Em SIAM Journal on Scientific Computing, Vol. 24, Issue 2 https://doi.org/10.1137/S1064827501387826	journal	January 2002
Remarks on a Multivariate Transformation Rosenblatt, Murray The Annals of Mathematical Statistics, Vol. 23, Issue 3 https://doi.org/10.1214/aoms/1177729394	journal	September 1952
Risk Assessment of Scramjet Unstart Using Adjoint-Based Sampling Methods Wang, Qiqi; Duraisamy, Karthik; Alonso, Juan J. AIAA Journal, Vol. 50, Issue 3 https://doi.org/10.2514/1.J051264	journal	March 2012
Subgrid length-scales for large-eddy simulation of stratified turbulence Schumann, Ulrich Theoretical and Computational Fluid Dynamics, Vol. 2, Issue 5-6 https://doi.org/10.1007/BF00271468	journal	August 1991
Statistical theory for compressible turbulent shear flows, with the application to subgrid modeling Yoshizawa, Akira Physics of Fluids, Vol. 29, Issue 7 https://doi.org/10.1063/1.865552	journal	January 1986
Boundary procedures for the time-dependent Burgers' equation under uncertainty Pettersson, Per; Nordström, Jan; Iaccarino, Gianluca Acta Mathematica Scientia, Vol. 30, Issue 2 https://doi.org/10.1016/S0252-9602(10)60061-6	journal	March 2010
Stochastic Finite Elements: A Spectral Approach Ghanem, Roger G.; Spanos, Pol D. Springer New York, NY https://doi.org/10.1007/978-1-4612-3094-6	book	January 1991
Numerical Challenges in the Use of Polynomial Chaos Representations for Stochastic Processes Debusschere, Bert J.; Najm, Habib N.; Pébay, Philippe P. SIAM Journal on Scientific Computing, Vol. 26, Issue 2 https://doi.org/10.1137/S1064827503427741	journal	January 2004
Theoretically based optimal large-eddy simulation Moser, Robert D.; Malaya, Nicholas P.; Chang, Henry Physics of Fluids, Vol. 21, Issue 10 https://doi.org/10.1063/1.3249754	journal	October 2009
Accounting for uncertainty in the analysis of overlap layer mean velocity models Oliver, Todd A.; Moser, Robert D. Physics of Fluids, Vol. 24, Issue 7 https://doi.org/10.1063/1.4733455	journal	July 2012
Direct numerical simulation of turbulent channel flow up to Reτ=590 Moser, Robert D.; Kim, John; Mansour, Nagi N. Physics of Fluids, Vol. 11, Issue 4 https://doi.org/10.1063/1.869966	journal	April 1999
Approximate deconvolution boundary conditions for large eddy simulation Borggaard, J.; Iliescu, T. Applied Mathematics Letters, Vol. 19, Issue 8 https://doi.org/10.1016/j.aml.2005.08.022	journal	August 2006
On the Statistical Calibration of Physical Models: STATISTICAL CALIBRATION OF PHYSICAL MODELS Sargsyan, K.; Najm, H. N.; Ghanem, R. International Journal of Chemical Kinetics, Vol. 47, Issue 4 https://doi.org/10.1002/kin.20906	journal	February 2015
Measuring and testing dependence by correlation of distances Székely, Gábor J.; Rizzo, Maria L.; Bakirov, Nail K. The Annals of Statistics, Vol. 35, Issue 6 https://doi.org/10.1214/009053607000000505	journal	December 2007
Gaussian Hilbert Spaces Janson, Svante https://doi.org/10.1017/CBO9780511526169	book	January 1997
A posteriori analysis of numerical errors in subfilter scalar variance modeling for large eddy simulation Kaul, C. M.; Raman, V. Physics of Fluids, Vol. 23, Issue 3 https://doi.org/10.1063/1.3556097	journal	March 2011
An Adaptive Metropolis Algorithm Haario, Heikki; Saksman, Eero; Tamminen, Johanna Bernoulli, Vol. 7, Issue 2 https://doi.org/10.2307/3318737	journal	April 2001
Bayesian Analysis of Errors-in-Variables Regression Models Dellaportas, Petros; Stephens, David A. Biometrics, Vol. 51, Issue 3 https://doi.org/10.2307/2533007	journal	September 1995
Inference and Uncertainty Propagation of Atomistically Informed Continuum Constitutive Laws, part 2: Generalized Continuum Models Based on Gaussian Processes Salloum, Maher; Templeton, Jeremy A. International Journal for Uncertainty Quantification, Vol. 4, Issue 2 https://doi.org/10.1615/Int.J.UncertaintyQuantification.2014008154	journal	January 2014
Experimentally informed optimization of turbulent diffusivity for a discrete hole film cooling geometry Ling, Julia; Coletti, Filippo; Yapa, Sayuri D. International Journal of Heat and Fluid Flow, Vol. 44 https://doi.org/10.1016/j.ijheatfluidflow.2013.07.005	journal	December 2013
The effect of numerical errors and turbulence models in large-eddy simulations of channel flow, with and without explicit filtering Gullbrand, Jessica; Chow, Fotini Katopodes Journal of Fluid Mechanics, Vol. 495 https://doi.org/10.1017/S0022112003006268	journal	November 2003
Towards Wall-Normal Filtering for Large-Eddy Simulation Templeton, Jeremy A.; Shoeybi, Mohammad Multiscale Modeling & Simulation, Vol. 5, Issue 2 https://doi.org/10.1137/050645683	journal	January 2006
Numerical prediction of turbulent flows using Reynolds-averaged Navier-Stokes and large-eddy simulation with uncertain inflow conditions: NUMERICAL PREDICTION USING RANS AND LES WITH UNCERTAIN CONDITIONS Congedo, Pietro Marco; Duprat, Cedric; Balarac, Guillaume International Journal for Numerical Methods in Fluids, Vol. 72, Issue 3 https://doi.org/10.1002/fld.3743	journal	October 2012
Goal-oriented uncertainty propagation using stochastic adjoints Duraisamy, Karthik; Chandrashekar, Praveen Computers & Fluids, Vol. 66 https://doi.org/10.1016/j.compfluid.2012.05.013	journal	August 2012
A General Class of Commutative Filters for LES in Complex Geometries Vasilyev, Oleg V.; Lund, Thomas S.; Moin, Parviz Journal of Computational Physics, Vol. 146, Issue 1 https://doi.org/10.1006/jcph.1998.6060	journal	October 1998
Optimal large eddy simulation of turbulent channel flow based on direct numerical simulation statistical data Völker, S.; Moser, R. D.; Venugopal, P. Physics of Fluids, Vol. 14, Issue 10 https://doi.org/10.1063/1.1503803	journal	October 2002
On the convergence of generalized polynomial chaos expansions Ernst, Oliver G.; Mugler, Antje; Starkloff, Hans-Jörg ESAIM: Mathematical Modelling and Numerical Analysis, Vol. 46, Issue 2 https://doi.org/10.1051/m2an/2011045	journal	October 2011
Sensitivity analysis of large-eddy simulations to subgrid-scale-model parametric uncertainty using polynomial chaos Lucor, Didier; Meyers, Johan; Sagaut, Pierre Journal of Fluid Mechanics, Vol. 585 https://doi.org/10.1017/S0022112007006751	journal	August 2007
Database analysis of errors in large-eddy simulation Meyers, Johan; Geurts, Bernard J.; Baelmans, Martine Physics of Fluids, Vol. 15, Issue 9 https://doi.org/10.1063/1.1597683	journal	August 2003
Uncertainty quantification in reacting-flow simulations through non-intrusive spectral projection Reagan, Matthew T.; Najm, Habib N.; Ghanem, Roger G. Combustion and Flame, Vol. 132, Issue 3 https://doi.org/10.1016/S0010-2180(02)00503-5	journal	February 2003
Parameter and prediction uncertainty in an optimized terrestrial carbon cycle model: Effects of constraining variables and data record length Ricciuto, Daniel M.; King, Anthony W.; Dragoni, D. Journal of Geophysical Research, Vol. 116, Issue G1 https://doi.org/10.1029/2010JG001400	journal	January 2011
Data exploration of turbulence simulations using a database cluster Perlman, Eric; Burns, Randal; Li, Yi Proceedings of the 2007 ACM/IEEE conference on Supercomputing - SC '07 https://doi.org/10.1145/1362622.1362654	conference	January 2007

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