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    Optimal Design and Model Validation for Combustion Experiments in a Shock Tube

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    Type
    Poster
    Authors
    Long, Quan cc
    Kim, Daesang
    Tempone, Raul cc
    Bisetti, Fabrizio cc
    Farooq, Aamir cc
    Knio, Omar cc
    Prudhomme, Serge
    KAUST Department
    Applied Mathematics and Computational Science Program
    Chemical Kinetics & Laser Sensors Laboratory
    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)
    Stochastic Numerics Research Group
    Date
    2014-01-06
    Permanent link to this record
    http://hdl.handle.net/10754/623996
    
    Metadata
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    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 Center. The unknown parameters are the pre-exponential parameters and the activation energies in the reaction rate functions. The control parameters are the initial hydrogen concentration and the temperature. First, we build a polynomial based surrogate model for the observable related to the reactions in the shock tube. Second, we use a novel MAP based approach to estimate the expected information gain in the proposed experiments and select the best experimental set-ups corresponding to the optimal expected information gains. Third, we use the synthetic data to carry out virtual validation of our methodology.
    Conference/Event name
    Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)
    Collections
    Posters; Applied Mathematics and Computational Science Program; Physical Science and Engineering (PSE) Division; Mechanical Engineering Program; Clean Combustion Research Center; 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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