Handle URI:
http://hdl.handle.net/10754/624015
Title:
Optimal Design of Shock Tube Experiments for Parameter Inference
Authors:
Bisetti, Fabrizio ( 0000-0001-5162-7805 ) ; Knio, Omar
Abstract:
We 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.
KAUST Department:
Physical Sciences and Engineering (PSE) Division
Conference/Event name:
Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)
Issue Date:
6-Jan-2014
Type:
Presentation
Additional Links:
http://mediasite.kaust.edu.sa/Mediasite/Play/118410a3bebb4a44997ba8ea6134743e1d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52
Appears in Collections:
Presentations; Conference on Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)

Full metadata record

DC FieldValue Language
dc.contributor.authorBisetti, Fabrizioen
dc.contributor.authorKnio, Omaren
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.en
dc.relation.urlhttp://mediasite.kaust.edu.sa/Mediasite/Play/118410a3bebb4a44997ba8ea6134743e1d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52en
dc.titleOptimal Design of Shock Tube Experiments for Parameter Inferenceen
dc.typePresentationen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.conference.dateJanuary 6-10, 2014en
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)en
dc.conference.locationKAUSTen
kaust.authorBisetti, Fabrizioen
kaust.authorKnio, Omaren
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