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dc.contributor.authorKim, Daesang
dc.contributor.authorEl Gharamti, Iman
dc.contributor.authorBisetti, Fabrizio
dc.contributor.authorFarooq, Aamir
dc.contributor.authorKnio, Omar
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.
dc.subjectRDFD
dc.titleNew Bayesian inference method using two steps of Markov chain Monte Carlo and its application to shock tube experiment data of Furan oxidation
dc.typePoster
dc.contributor.departmentClean Combustion Research Center
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Division
dc.contributor.departmentMechanical Engineering Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences & Engineering (CEMSE)
dc.conference.dateJanuary 5-10, 2016
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)
dc.conference.locationKAUST
kaust.personKim, Daesang
kaust.personEl Gharamti, Iman
kaust.personBisetti, Fabrizio
kaust.personFarooq, Aamir
kaust.personKnio, Omar
refterms.dateFOA2018-06-13T10:45:07Z


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