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Uncertainty quantification of reaction mechanisms accounting for correlations introduced by rate rules and fitted Arrhenius parameters

Combustion and Flame

Prager, Jens; Najm, Habib N.; Sargsyan, Khachik; Safta, Cosmin

We study correlations among uncertain Arrhenius rate parameters in a chemical model for hydrocarbon fuel-air combustion. We consider correlations induced by the use of rate rules for modeling reaction rate constants, as well as those resulting from fitting rate expressions to empirical measurements arriving at a joint probability density for all Arrhenius parameters. We focus on homogeneous ignition in a fuel-air mixture at constant-pressure. We outline a general methodology for this analysis using polynomial chaos and Bayesian inference methods. We examine the uncertainties in both the Arrhenius parameters and in predicted ignition time, outlining the role of correlations, and considering both accuracy and computational efficiency. © 2013 The Combustion Institute.

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TYPE Journal Article YEAR 2013
OSTIScopus

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

Prager, Jens; Najm, Habib N.; Zador, Judit

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.

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TYPE Conference YEAR 2013
OSTIScopus

KinBot 1.0: A code for automatic PES exploration

8th US National Combustion Meeting 2013

Zador, Judit; Najm, Habib N.

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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TYPE Conference YEAR 2013
OSTIScopus

Multiparameter spectral representation of noise-induced competence in bacillus subtilis

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Sargsyan, Khachik; Safta, Cosmin; Debusschere, Bert; Najm, Habib N.

In this work, the problem of representing a stochastic forward model output with respect to a large number of input parameters is considered. The methodology is applied to a stochastic reaction network of competence dynamics in Bacillus subtilis bacterium. In particular, the dependence of the competence state on rate constants of underlying reactions is investigated. We base our methodology on Polynomial Chaos (PC) spectral expansions that allow effective propagation of input parameter uncertainties to outputs of interest. Given a number of forward model training runs at sampled input parameter values, the PC modes are estimated using a Bayesian framework. As an outcome, these PC modes are described with posterior probability distributions. The resulting expansion can be regarded as an uncertain response function and can further be used as a computationally inexpensive surrogate instead of the original reaction model for subsequent analyses such as calibration or optimization studies. Furthermore, the methodology is enhanced with a classification-based mixture PC formulation that overcomes the difficulties associated with representing potentially nonsmooth input-output relationships. Finally, the global sensitivity analysis based on the multiparameter spectral representation of an observable of interest provides biological insight and reveals the most important reactions and their couplings for the competence dynamics. © 2013 IEEE.

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TYPE Journal Article YEAR 2012
ScopusOSTIDOI

Multiparameter spectral representation of noise-induced competence in bacillus subtilis

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Sargsyan, Khachik; Safta, Cosmin; Debusschere, Bert; Najm, Habib N.

In this work, the problem of representing a stochastic forward model output with respect to a large number of input parameters is considered. The methodology is applied to a stochastic reaction network of competence dynamics in Bacillus subtilis bacterium. In particular, the dependence of the competence state on rate constants of underlying reactions is investigated. We base our methodology on Polynomial Chaos (PC) spectral expansions that allow effective propagation of input parameter uncertainties to outputs of interest. Given a number of forward model training runs at sampled input parameter values, the PC modes are estimated using a Bayesian framework. As an outcome, these PC modes are described with posterior probability distributions. The resulting expansion can be regarded as an uncertain response function and can further be used as a computationally inexpensive surrogate instead of the original reaction model for subsequent analyses such as calibration or optimization studies. Furthermore, the methodology is enhanced with a classification-based mixture PC formulation that overcomes the difficulties associated with representing potentially nonsmooth input-output relationships. Finally, the global sensitivity analysis based on the multiparameter spectral representation of an observable of interest provides biological insight and reveals the most important reactions and their couplings for the competence dynamics. © 2013 IEEE.

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TYPE Journal Article YEAR 2012
DOIOSTIScopus

A stochastic multiscale coupling scheme to account for sampling noise in atomistic-to-continuum simulations

Multiscale Modeling and Simulation

Salloum, Maher; Sargsyan, Khachik; Jones, Reese E.; Debusschere, Bert; Najm, Habib N.; Adalsteinsson, Helgi

We present a methodology to assess the predictive fidelity of multiscale simulations by incorporating uncertainty in the information exchanged between the atomistic and continuum simulation components. Focusing on uncertainty due to finite sampling in molecular dynamics (MD) simulations, we present an iterative stochastic coupling algorithm that relies on Bayesian inference to build polynomial chaos expansions for the variables exchanged across the atomistic-continuum interface. We consider a simple Couette flow model where velocities are exchanged between the atomistic and continuum components. To alleviate the burden of running expensive MD simulations at every iteration, a surrogate model is constructed from which samples can be efficiently drawn as data for the Bayesian inference. Results show convergence of the coupling algorithm at a reasonable number of iterations. The uncertainty associated with the exchanged variables significantly depends on the amount of data sampled from the MD simulations and on the width of the time averaging window used in the MD simulations. Sequential Bayesian updating is also implemented in order to enhance the accuracy of the stochastic algorithm predictions. © 2012 Society for Industrial and Applied Mathematics.

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TYPE Journal Article YEAR 2012
OSTIScopus

Automated exploration of the mechanism of elementary reactions

Najm, Habib N.; Zador, Judit

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.

More Details
TYPE SAND Report YEAR 2012
DOIOSTI
Results 251–300 of 433
Results 251–300 of 433