New Bayesian inference method using two steps of Markov chain Monte Carlo and its application to shock tube experiment data of Furan oxidation

Handle URI:
http://hdl.handle.net/10754/624825
Title:
New Bayesian inference method using two steps of Markov chain Monte Carlo and its application to shock tube experiment data of Furan oxidation
Authors:
Kim, Daesang; El Gharamti, Iman; Bisetti, Fabrizio ( 0000-0001-5162-7805 ) ; Farooq, Aamir ( 0000-0001-5296-2197 ) ; Knio, Omar
Abstract:
A new Bayesian inference method has been developed and applied to Furan shock tube experimental data for efficient statistical inferences of the Arrhenius parameters of two OH radical consumption reactions. The collected experimental data, which consist of time series signals of OH radical concentrations of 14 shock tube experiments, may require several days for MCMC computations even with the support of a fast surrogate of the combustion simulation model, while the new method reduces it to several hours by splitting the process into two steps of MCMC: the first inference of rate constants and the second inference of the Arrhenius parameters. Each step has low dimensional parameter spaces and the second step does not need the executions of the combustion simulation. Furthermore, the new approach has more flexibility in choosing the ranges of the inference parameters, and the higher speed and flexibility enable the more accurate inferences and the analyses of the propagation of errors in the measured temperatures and the alignment of the experimental time to the inference results.
KAUST Department:
Computer, Electrical and Mathematical Sciences & Engineering (CEMSE)
Conference/Event name:
Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)
Issue Date:
6-Jan-2016
Type:
Poster
Appears in Collections:
Posters; Conference on Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)

Full metadata record

DC FieldValue Language
dc.contributor.authorKim, Daesangen
dc.contributor.authorEl Gharamti, Imanen
dc.contributor.authorBisetti, Fabrizioen
dc.contributor.authorFarooq, Aamiren
dc.contributor.authorKnio, Omaren
dc.date.accessioned2017-06-08T06:32:28Z-
dc.date.available2017-06-08T06:32:28Z-
dc.date.issued2016-01-06-
dc.identifier.urihttp://hdl.handle.net/10754/624825-
dc.description.abstractA new Bayesian inference method has been developed and applied to Furan shock tube experimental data for efficient statistical inferences of the Arrhenius parameters of two OH radical consumption reactions. The collected experimental data, which consist of time series signals of OH radical concentrations of 14 shock tube experiments, may require several days for MCMC computations even with the support of a fast surrogate of the combustion simulation model, while the new method reduces it to several hours by splitting the process into two steps of MCMC: the first inference of rate constants and the second inference of the Arrhenius parameters. Each step has low dimensional parameter spaces and the second step does not need the executions of the combustion simulation. Furthermore, the new approach has more flexibility in choosing the ranges of the inference parameters, and the higher speed and flexibility enable the more accurate inferences and the analyses of the propagation of errors in the measured temperatures and the alignment of the experimental time to the inference results.en
dc.subjectRDFDen
dc.titleNew Bayesian inference method using two steps of Markov chain Monte Carlo and its application to shock tube experiment data of Furan oxidationen
dc.typePosteren
dc.contributor.departmentComputer, Electrical and Mathematical Sciences & Engineering (CEMSE)en
dc.conference.dateJanuary 5-10, 2016en
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)en
dc.conference.locationKAUSTen
kaust.authorKim, Daesangen
kaust.authorEl Gharamti, Imanen
kaust.authorBisetti, Fabrizioen
kaust.authorFarooq, Aamiren
kaust.authorKnio, Omaren
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