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Article: Uncertainty quantification and robust predictive system analysis for high temperature kinetics of HCN/O2/Ar mixture

TitleUncertainty quantification and robust predictive system analysis for high temperature kinetics of HCN/O<inf>2</inf>/Ar mixture
Authors
KeywordsModeling error
Reaction rate
Bayesian approach
Stochastic system
Robust predictive analysis
Arrhenius form
Stochastic model
Deterministic model
Experimental error
Uncertainty quantification
Issue Date2016
Citation
Chemical Physics, 2016, v. 475, p. 136-152 How to Cite?
Abstract© 2016 Elsevier B.V. In this paper, a stochastic system based Bayesian approach is applied to quantify the uncertainties involved in the modeling of the HCN/O2/Ar mixture kinetics proposed by Thielen and Roth (1987). This enables more robust predictions of quantities of interest such as rate coefficients of HCN + Ar → H + CN + Ar and O2 + CN → NCO + O by using a stochastic Arrhenius form calibrated against their experimental data. This Bayesian approach requires the evaluation of multidimensional integrals, which cannot be done analytically. Here a recently developed stochastic simulation algorithm, which allows for efficient sampling in the high-dimensional parameter space, is used. We quantify the uncertainties in the modeling of the HCN/O2/Ar mixture kinetics and in turn the two rate coefficients and the other relevant rate coefficients. The uncertainty in the error including both the experimental measurement error and physical modeling error is also quantified. The effect of the number of uncertain parameters on the uncertainties is investigated.
Persistent Identifierhttp://hdl.handle.net/10722/296129
ISSN
2023 Impact Factor: 2.0
2023 SCImago Journal Rankings: 0.439
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorCheung, Sai Hung-
dc.contributor.authorMiki, Kenji-
dc.contributor.authorPrudencio, Ernesto-
dc.contributor.authorSimmons, Chris-
dc.date.accessioned2021-02-11T04:52:54Z-
dc.date.available2021-02-11T04:52:54Z-
dc.date.issued2016-
dc.identifier.citationChemical Physics, 2016, v. 475, p. 136-152-
dc.identifier.issn0301-0104-
dc.identifier.urihttp://hdl.handle.net/10722/296129-
dc.description.abstract© 2016 Elsevier B.V. In this paper, a stochastic system based Bayesian approach is applied to quantify the uncertainties involved in the modeling of the HCN/O2/Ar mixture kinetics proposed by Thielen and Roth (1987). This enables more robust predictions of quantities of interest such as rate coefficients of HCN + Ar → H + CN + Ar and O2 + CN → NCO + O by using a stochastic Arrhenius form calibrated against their experimental data. This Bayesian approach requires the evaluation of multidimensional integrals, which cannot be done analytically. Here a recently developed stochastic simulation algorithm, which allows for efficient sampling in the high-dimensional parameter space, is used. We quantify the uncertainties in the modeling of the HCN/O2/Ar mixture kinetics and in turn the two rate coefficients and the other relevant rate coefficients. The uncertainty in the error including both the experimental measurement error and physical modeling error is also quantified. The effect of the number of uncertain parameters on the uncertainties is investigated.-
dc.languageeng-
dc.relation.ispartofChemical Physics-
dc.subjectModeling error-
dc.subjectReaction rate-
dc.subjectBayesian approach-
dc.subjectStochastic system-
dc.subjectRobust predictive analysis-
dc.subjectArrhenius form-
dc.subjectStochastic model-
dc.subjectDeterministic model-
dc.subjectExperimental error-
dc.subjectUncertainty quantification-
dc.titleUncertainty quantification and robust predictive system analysis for high temperature kinetics of HCN/O<inf>2</inf>/Ar mixture-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.chemphys.2016.05.026-
dc.identifier.scopuseid_2-s2.0-84979587283-
dc.identifier.volume475-
dc.identifier.spage136-
dc.identifier.epage152-
dc.identifier.isiWOS:000381588400018-
dc.identifier.issnl0301-0104-

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