port models is hardly limited to combustion. Kinetic models of adsorption, desorption, and reactions among surface species are fundamental to all aspects of electrochemical energy conversion. For example, characterizing the electrochemical oxidation of carbon monoxide and hydrogen on anode surfaces is critical to the design of fuel-flexible solid-oxide fuel cells. To address these challenges, Aerospace Computational Design Laboratory researchers are developing systematic approaches to chemical kinetic modeling that fuse multiple sources of information, and that, crucially, take advantage of indirect data such as ignition delays and flame speeds for combustion kinetics, or impedance spectroscopy for reaction rates and transport phenomena in fuel cells. These methods cast model construction and refinement as problems of statistical inference, and thus provide data-driven assessments of the uncertainty in the models themselves. Realizing these methods involves computational challenges. Exploring model predictions over a range of parameter values may require thousands of repeated simulations, a computationally prohibitive undertaking for large-scale systems. Therefore, model reduction and output approximations are essential to the inference process. Simulation costs aside, simply exploring a high-dimensional parameter or model space with complicated correlation structure can present many difficulties. Our work encompasses dimensionality reduction and efficient sampling methods that make such sampling possible. For example, we have used dimensionality reduction and surrogate modeling to accelerate the Uncertainty is inevitable when learning from limited, noisy, and indirect data. Here, measurements of pressure and saturation are used to learn properties of porous media through which groundwater flows. Plausible realizations of the permeability field, at coarse and fine scales, are shown in each row. (S. Mckenna, Y. Marzouk, J. Ray, and B. van Bloemen Waanders image.)
inference of kinetic parameters and transport properties in chemically reacting flow by 2–3 orders of magnitude over conventional approaches. A related effort aims to make data more informative, via optimal experimental design. Given limited experimental
32
AEROASTRO 2009-2010