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.
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.
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).