Optimal Bayesian experimental design for priors of compact support with application to shock-tube experiments for combustion kinetics

Handle URI:
http://hdl.handle.net/10754/594703
Title:
Optimal Bayesian experimental design for priors of compact support with application to shock-tube experiments for combustion kinetics
Authors:
Bisetti, Fabrizio ( 0000-0001-5162-7805 ) ; Kim, Daesang; Knio, Omar; Long, Quan ( 0000-0002-0329-9437 ) ; Tempone, Raul ( 0000-0003-1967-4446 )
Abstract:
The analysis of reactive systems in combustion science and technology relies on detailed models comprising many chemical reactions that describe the conversion of fuel and oxidizer into products and the formation of pollutants. Shock-tube experiments are a convenient setting for measuring the rate parameters of individual reactions. The temperature, pressure, and concentration of reactants are chosen to maximize the sensitivity of the measured quantities to the rate parameter of the target reaction. In this study, we optimize the experimental setup computationally by optimal experimental design (OED) in a Bayesian framework. We approximate the posterior probability density functions (pdf) using truncated Gaussian distributions in order to account for the bounded domain of the uniform prior pdf of the parameters. The underlying Gaussian distribution is obtained in the spirit of the Laplace method, more precisely, the mode is chosen as the maximum a posteriori (MAP) estimate, and the covariance is chosen as the negative inverse of the Hessian of the misfit function at the MAP estimate. The model related entities are obtained from a polynomial surrogate. The optimality, quantified by the information gain measures, can be estimated efficiently by a rejection sampling algorithm against the underlying Gaussian probability distribution, rather than against the true posterior. This approach offers a significant error reduction when the magnitude of the invariants of the posterior covariance are comparable to the size of the bounded domain of the prior. We demonstrate the accuracy and superior computational efficiency of our method for shock-tube experiments aiming to measure the model parameters of a key reaction which is part of the complex kinetic network describing the hydrocarbon oxidation. In the experiments, the initial temperature and fuel concentration are optimized with respect to the expected information gain in the estimation of the parameters of the target reaction rate. We show that the expected information gain surface can change its “shape" dramatically according to the level of noise introduced into the synthetic data. The information that can be extracted from the data saturates as a logarithmic function of the number of experiments, and few experiments are needed when they are conducted at the optimal experimental design conditions. Furthermore, inversion of the legacy data indicates the validity and robustness of our designs. This article is protected by copyright. All rights reserved.
KAUST Department:
Center for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ); Clean Combustion Research Center
Citation:
Optimal Bayesian experimental design for priors of compact support with application to shock-tube experiments for combustion kinetics 2016:n/a International Journal for Numerical Methods in Engineering
Publisher:
Wiley-Blackwell
Journal:
International Journal for Numerical Methods in Engineering
Issue Date:
12-Jan-2016
DOI:
10.1002/nme.5211
Type:
Article
ISSN:
00295981
Additional Links:
http://doi.wiley.com/10.1002/nme.5211
Appears in Collections:
Articles; Clean Combustion Research Center

Full metadata record

DC FieldValue Language
dc.contributor.authorBisetti, Fabrizioen
dc.contributor.authorKim, Daesangen
dc.contributor.authorKnio, Omaren
dc.contributor.authorLong, Quanen
dc.contributor.authorTempone, Raulen
dc.date.accessioned2016-01-24T06:54:44Zen
dc.date.available2016-01-24T06:54:44Zen
dc.date.issued2016-01-12en
dc.identifier.citationOptimal Bayesian experimental design for priors of compact support with application to shock-tube experiments for combustion kinetics 2016:n/a International Journal for Numerical Methods in Engineeringen
dc.identifier.issn00295981en
dc.identifier.doi10.1002/nme.5211en
dc.identifier.urihttp://hdl.handle.net/10754/594703en
dc.description.abstractThe analysis of reactive systems in combustion science and technology relies on detailed models comprising many chemical reactions that describe the conversion of fuel and oxidizer into products and the formation of pollutants. Shock-tube experiments are a convenient setting for measuring the rate parameters of individual reactions. The temperature, pressure, and concentration of reactants are chosen to maximize the sensitivity of the measured quantities to the rate parameter of the target reaction. In this study, we optimize the experimental setup computationally by optimal experimental design (OED) in a Bayesian framework. We approximate the posterior probability density functions (pdf) using truncated Gaussian distributions in order to account for the bounded domain of the uniform prior pdf of the parameters. The underlying Gaussian distribution is obtained in the spirit of the Laplace method, more precisely, the mode is chosen as the maximum a posteriori (MAP) estimate, and the covariance is chosen as the negative inverse of the Hessian of the misfit function at the MAP estimate. The model related entities are obtained from a polynomial surrogate. The optimality, quantified by the information gain measures, can be estimated efficiently by a rejection sampling algorithm against the underlying Gaussian probability distribution, rather than against the true posterior. This approach offers a significant error reduction when the magnitude of the invariants of the posterior covariance are comparable to the size of the bounded domain of the prior. We demonstrate the accuracy and superior computational efficiency of our method for shock-tube experiments aiming to measure the model parameters of a key reaction which is part of the complex kinetic network describing the hydrocarbon oxidation. In the experiments, the initial temperature and fuel concentration are optimized with respect to the expected information gain in the estimation of the parameters of the target reaction rate. We show that the expected information gain surface can change its “shape" dramatically according to the level of noise introduced into the synthetic data. The information that can be extracted from the data saturates as a logarithmic function of the number of experiments, and few experiments are needed when they are conducted at the optimal experimental design conditions. Furthermore, inversion of the legacy data indicates the validity and robustness of our designs. This article is protected by copyright. All rights reserved.en
dc.language.isoenen
dc.publisherWiley-Blackwellen
dc.relation.urlhttp://doi.wiley.com/10.1002/nme.5211en
dc.rightsThis is the peer reviewed version of the following article: Bisetti, F., Kim, D., Knio, O., Long, Q., and Tempone, R. (2016) Optimal Bayesian experimental design for priors of compact support with application to shock-tube experiments for combustion kinetics. Int. J. Numer. Meth. Engng, which has been published in final form at http://doi.wiley.com/10.1002/nme.5211. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.en
dc.titleOptimal Bayesian experimental design for priors of compact support with application to shock-tube experiments for combustion kineticsen
dc.typeArticleen
dc.contributor.departmentCenter for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ)en
dc.contributor.departmentClean Combustion Research Centeren
dc.identifier.journalInternational Journal for Numerical Methods in Engineeringen
dc.eprint.versionPost-printen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorBisetti, Fabrizioen
kaust.authorBisetti, Fabrizioen
kaust.authorKim, Daesangen
kaust.authorKim, Daesangen
kaust.authorKnio, Omaren
kaust.authorLong, Quanen
kaust.authorTempone, Raulen
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