Optimal Bayesian Experimental Design for Combustion Kinetics

Handle URI:
http://hdl.handle.net/10754/599086
Title:
Optimal Bayesian Experimental Design for Combustion Kinetics
Authors:
Huan, Xun; Marzouk, Youssef
Abstract:
Experimental diagnostics play an essential role in the development and refinement of chemical kinetic models, whether for the combustion of common complex hydrocarbons or of emerging alternative fuels. Questions of experimental design—e.g., which variables or species to interrogate, at what resolution and under what conditions—are extremely important in this context, particularly when experimental resources are limited. This paper attempts to answer such questions in a rigorous and systematic way. We propose a Bayesian framework for optimal experimental design with nonlinear simulation-based models. While the framework is broadly applicable, we use it to infer rate parameters in a combustion system with detailed kinetics. The framework introduces a utility function that reflects the expected information gain from a particular experiment. Straightforward evaluation (and maximization) of this utility function requires Monte Carlo sampling, which is infeasible with computationally intensive models. Instead, we construct a polynomial surrogate for the dependence of experimental observables on model parameters and design conditions, with the help of dimension-adaptive sparse quadrature. Results demonstrate the efficiency and accuracy of the surrogate, as well as the considerable effectiveness of the experimental design framework in choosing informative experimental conditions.
Citation:
Huan X, Marzouk Y (2011) Optimal Bayesian Experimental Design for Combustion Kinetics. 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. Available: http://dx.doi.org/10.2514/6.2011-513.
Publisher:
American Institute of Aeronautics and Astronautics (AIAA)
Journal:
49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition
Issue Date:
4-Jan-2011
DOI:
10.2514/6.2011-513
Type:
Conference Paper
Sponsors:
The authors would like to acknowledge support from the KAUST Global Research Partnership and fromthe US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorHuan, Xunen
dc.contributor.authorMarzouk, Youssefen
dc.date.accessioned2016-02-25T13:52:36Zen
dc.date.available2016-02-25T13:52:36Zen
dc.date.issued2011-01-04en
dc.identifier.citationHuan X, Marzouk Y (2011) Optimal Bayesian Experimental Design for Combustion Kinetics. 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. Available: http://dx.doi.org/10.2514/6.2011-513.en
dc.identifier.doi10.2514/6.2011-513en
dc.identifier.urihttp://hdl.handle.net/10754/599086en
dc.description.abstractExperimental diagnostics play an essential role in the development and refinement of chemical kinetic models, whether for the combustion of common complex hydrocarbons or of emerging alternative fuels. Questions of experimental design—e.g., which variables or species to interrogate, at what resolution and under what conditions—are extremely important in this context, particularly when experimental resources are limited. This paper attempts to answer such questions in a rigorous and systematic way. We propose a Bayesian framework for optimal experimental design with nonlinear simulation-based models. While the framework is broadly applicable, we use it to infer rate parameters in a combustion system with detailed kinetics. The framework introduces a utility function that reflects the expected information gain from a particular experiment. Straightforward evaluation (and maximization) of this utility function requires Monte Carlo sampling, which is infeasible with computationally intensive models. Instead, we construct a polynomial surrogate for the dependence of experimental observables on model parameters and design conditions, with the help of dimension-adaptive sparse quadrature. Results demonstrate the efficiency and accuracy of the surrogate, as well as the considerable effectiveness of the experimental design framework in choosing informative experimental conditions.en
dc.description.sponsorshipThe authors would like to acknowledge support from the KAUST Global Research Partnership and fromthe US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR).en
dc.publisherAmerican Institute of Aeronautics and Astronautics (AIAA)en
dc.titleOptimal Bayesian Experimental Design for Combustion Kineticsen
dc.typeConference Paperen
dc.identifier.journal49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Expositionen
dc.contributor.institutionMassachusetts Institute of Technologyen
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