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    Optimal Design of Shock Tube Experiments for Parameter Inference

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    Type
    Presentation
    Authors
    Bisetti, Fabrizio cc
    Knio, Omar cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Clean Combustion Research Center
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Mechanical Engineering Program
    Physical Science and Engineering (PSE) Division
    Reactive Flow Modeling Laboratory (RFML)
    Date
    2014-01-06
    Permanent link to this record
    http://hdl.handle.net/10754/624015
    
    Metadata
    Show full item record
    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.
    Conference/Event name
    Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)
    Additional Links
    http://mediasite.kaust.edu.sa/Mediasite/Play/118410a3bebb4a44997ba8ea6134743e1d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52
    Collections
    Applied Mathematics and Computational Science Program; Physical Science and Engineering (PSE) Division; Mechanical Engineering Program; Clean Combustion Research Center; Presentations; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division; Conference on Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)

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