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dc.contributor.authorBisetti, Fabrizio
dc.contributor.authorKnio, Omar
dc.date.accessioned2017-06-01T10:20:43Z
dc.date.available2017-06-01T10:20:43Z
dc.date.issued2014-01-06
dc.identifier.urihttp://hdl.handle.net/10754/624015
dc.description.abstractWe develop a Bayesian framework for the optimal experimental design of the shock tube experiments which are being carried out at the KAUST Clean Combustion Research Center. The unknown parameters are the pre-exponential parameters and the activation energies in the reaction rate expressions. The control parameters are the initial mixture composition and the temperature. The approach is based on first building a polynomial based surrogate model for the observables relevant to the shock tube experiments. Based on these surrogates, a novel MAP based approach is used to estimate the expected information gain in the proposed experiments, and to select the best experimental set-ups yielding the optimal expected information gains. The validity of the approach is tested using synthetic data generated by sampling the PC surrogate. We finally outline a methodology for validation using actual laboratory experiments, and extending experimental design methodology to the cases where the control parameters are noisy.
dc.relation.urlhttp://mediasite.kaust.edu.sa/Mediasite/Play/118410a3bebb4a44997ba8ea6134743e1d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52
dc.titleOptimal Design of Shock Tube Experiments for Parameter Inference
dc.typePresentation
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentClean Combustion Research Center
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentMechanical Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentReactive Flow Modeling Laboratory (RFML)
dc.conference.dateJanuary 6-10, 2014
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)
dc.conference.locationKAUST
kaust.personBisetti, Fabrizio
kaust.personKnio, Omar
refterms.dateFOA2018-06-13T16:23:06Z


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