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Uncertainty quantification in LES of channel flow

International Journal for Numerical Methods in Fluids

Safta, Cosmin S.; Blaylock, Myra L.; Templeton, Jeremy A.; Domino, Stefan P.; Sargsyan, Khachik S.; Najm, H.N.

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.

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TYPE Journal Article YEAR 2017
ScopusOSTIDOI

Performance scaling variability and energy analysis for a resilient ULFM-based PDE solver

Proceedings of ScalA 2016: 7th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems - Held in conjunction with SC16: The International Conference for High Performance Computing, Networking, Storage and Analysis

Morris, K.; Rizzi, F.; Cook, B.; Mycek, P.; LeMaitre, O.; Knio, O.M.; Sargsyan, Khachik S.; Dahlgren, K.; Debusschere, Bert D.

We present a resilient task-based domain-decomposition preconditioner for partial differential equations (PDEs) built on top of User Level Fault Mitigation Message Passing Interface (ULFM-MPI). The algorithm reformulates the PDE as a sampling problem, followed by a robust regression-based solution update that is resilient to silent data corruptions (SDCs). We adopt a server-client model where all state information is held by the servers, while clients only serve as computational units. The task-based nature of the algorithm and the capabilities of ULFM complement each other to support missing tasks, making the application resilient to clients failing.We present weak and strong scaling results on Edison, National Energy Research Scientific Computing Center (NERSC), for a nominal and a fault-injected case, showing that even in the presence of faults, scalability tested up to 50k cores is within 90%. We then quantify the variability of weak and strong scaling due to the presence of faults. Finally, we discuss the performance of our application with respect to subdomain size, server/client configuration, and the interplay between energy and resilience.

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TYPE Conference Poster YEAR 2017
ScopusOSTI

Inference of reaction rate parameters based on summary statistics from experiments

Proceedings of the Combustion Institute

Khalil, Mohammad K.; Chowdhary, K.; Safta, Cosmin S.; Sargsyan, Khachik S.; Najm, H.N.

Bayesian inference and maximum entropy methods were employed for the estimation of the joint probability density for the Arrhenius rate parameters of the rate coefficient of the H2/O2-mechanism chain branching reaction H + O2 → OH + O. A consensus joint posterior on the parameters was obtained by pooling the posterior parameter densities given each consistent data set. Efficient surrogates for the OH concentration were constructed using a combination of Padé and polynomial approximants. Gauss-Hermite quadrature with Gaussian proposal probability density functions for moment computation were used resulting in orders of magnitude speedup in data likelihood evaluation. The consistent data sets resulted in nearly Gaussian conditional parameter probability density functions. The resulting pooled parameter probability density function was propagated through stoichiometric H2-air auto-ignition computations to illustrate the necessity for correlation among the Arrhenius rate parameters of one reaction and across rate parameters of different reactions to be considered.

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TYPE Journal Article YEAR 2017
ScopusOSTIDOI

Uncertainty Quantification in LES Computations of Turbulent Multiphase Combustion in a Scramjet Engine ? ScramjetUQ ?

Najm, H.N.; Debusschere, Bert D.; Safta, Cosmin S.; Sargsyan, Khachik S.; Huan, Xun H.; Oefelein, Joseph C.; Lacaze, Guilhem M.; Vane, Zachary P.; Eldred, Michael S.; Geraci, Gianluca G.; Knio, Omar K.; Sraj, I.S.; Scovazzi, G.S.; Colomes, O.C.; Marzouk, Y.M.; Zahm, O.Z.; Menhorn, F.M.; Ghanem, R.G.; Tsilifis, P.T.

Abstract not provided.

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TYPE Conference Poster YEAR 2016
OSTI

Uncertainty Quantification in LES Computations of Turbulent Multiphase Combustion in a Scramjet Engine

Najm, H.N.; Debusschere, Bert D.; Safta, Cosmin S.; Sargsyan, Khachik S.; Huan, Xun H.; Oefelein, Joseph C.; Lacaze, Guilhem M.; Vane, Zachary P.; Eldred, Michael S.; Geraci, G.G.; Knio, O.K.; Sraj, I.S.; Scovazzi, G.S.; Colomes, O.C.; Marzouk, Y.M.; Zahm, O.Z.; Augustin, F.A.; Menhorn, F.M.; Ghanem, R.G.; Tsilifis, P.T.

Abstract not provided.

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TYPE Conference Poster YEAR 2016
OSTI

UQTk Version 3.0 User Manual

Sargsyan, Khachik S.; Safta, Cosmin S.; Chowdhary, Kamaljit S.; Castorena, Sarah C.; de Bord, Sarah d.; Debusschere, Bert D.

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncer- tainty in numerical model predictions. Version 3.0 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity anal- ysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

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TYPE SAND Report YEAR 2016
OSTIDOI
Results 76–100 of 235
Results 76–100 of 235