Monte Carlo Estimation of Moments: Nonlinearity & Dimensionality
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Publications
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Jump to search filtersBenchmark Problems for Uncertainty Quantification in CFD -- Reacting Flow --
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Highlights of QUEST Developments and Partnership Activities
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Analysis and Reduction of Chemical Models under Uncertainty
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Model Reduction in Reacting Flow
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Probabilistic Approaches for Communication Avoidance and Resilience in Exascale Simulations
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Chemical Kinetic Rate Coefficient Estimation Given Partial Information
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DALEC calibration with daily NEE Data at Harvard Forest
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Parameter Estimation with Partial Information
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UQ tutorial slides
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Enforcing Positivity in Intrusive PC-UQ Methods for Reactive ODE Systems
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Parameter Estimation with Missing Data
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Copy of Inference-based Assessment of Ab Initio Rate Coefficient Calculations
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QUEST SNL Activities
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Probabilistic Schwarz Coupling for Fault-Tolerance and Scalability
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MODEL REDUCTION IN REACTING FLOW
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Analysis and Reduction of Chemical Models under Uncertainty
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Validating extreme-scale HPC simulation through Bayesian inference uncertainty quantification
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Dimensionality Reduction and Global Sensitivity Analysis for the Community Land Model
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DOE HQ BES discussion
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DOE HQ BES discussion
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Big Data Meets Big Models An Uncertainty Quantification Perspective
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Hybrid Discrete/Continuum Algorithms for Stochastic Reaction Networks
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Uncertainty quantification in the ab initio rate-coefficient calculation for the CH3CH(OH)CH3 + OH → CH3C (OH)CH3 + H2O reaction
Proceedings of the Combustion Institute
Theoretical methods to obtain rate coefficients are essential to fundamental combustion chemistry research, yet the associated uncertainties are largely unexplored in a systematic manner. In this paper we focus on the study of parametric uncertainties for a hydrogen-atom-abstraction reaction, CH 3CH(OH)CH3 + OH → CH3C (OH)CH3 + H2O, which bears significant importance in low-temperature alcohol combustion and especially in autoignition models. After identifying the parameters causing significant uncertainty in the rate-coefficient calculations, Bayesian inference is employed to determine the joint probability density function (PDF) thereof using the experimental data of Dunlop and Tully (1993) [6] on isopropanol + OH. The inferred PDFs are compared to the various parameter values obtained from high-level electronic-structure calculations in order to assess the limitations of current methodologies. To gain insight on modeling the kinetic isotope effect (KIE), the reaction of the hydroxyl radical with deuterated isopropanol is also investigated. © 2012 The Combustion Institute. Published by Elsevier Inc. All rights reserved.
KinBot 1.0: A code for automatic PES exploration
8th US National Combustion Meeting 2013
Optimization of new transportation fuels and engine technologies requires the characterization of the combustion chemistry of a wide range of fuel classes. Theoretical studies of elementary reactions - the building blocks of complex reaction mechanisms - are essential to accurately predict important combustion processes such as autoignition of biofuels. The current bottleneck for these calculations is a user-intensive exploration of the underlying potential energy surface (PES), which relies on the "chemical intuition" of the scientist to propose initial guesses for the relevant chemical configurations. For newly emerging fuels, this approach cripples the rate of progress because of the system size and complexity. The KinBot program package aims to accelerate the detailed chemical kinetic description of combustion, and enables large-scale systematic studies on the sub-mechanism level.
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