The Burke Lab
As the energy demands in the United States and elsewhere in the world continue to grow, energy security, energy economy, and climate change become increasingly more pressing societal issues. Roughly 85% of our current energy for residential, commercial, industrial and transportation purposes in the United States is derived from combustion of fossil fuels; roughly 60% of the total energy is wasted. As a result, substantial (and highly warranted) attention has recently been devoted to 1) increasing the efficiency of combustion devices to increase the amount of usable energy per fuel consumed and 2) using new fuels from more sustainable, bio-derived sources. Furthermore, as we learn more about the detrimental effects of burning fossils fuels for the environment, human health, and climate change, the ability to mitigate pollutants from combustion becomes increasingly more important.
The reaction networks responsible for fuel oxidation in combustion devices pose a number of unique scientific challenges that arise from the richly complex and inherently multi-scale nature of these processes. Inside the piston engines that power vehicles on the road, inside the gas turbine engines that power airplanes and the electrical grid, and, more visibly, inside the candles that illuminate the dinner table, the conversion of fuel and atmospheric oxygen to products and heat does not take place in a single step. Rather, the conversion process frequently proceeds through thousands of intermediate chemical species, which undergo tens of thousands of elementary reactions, all of which occur at rates that are strongly dependent on the local temperature, pressure, and concentrations of other chemical species. Needless to say, enormous amounts of data are required to understand and characterize these processes; and interpreting those data require interdisciplinary, data-driven solutions that embrace the inherent multi-scale nature of these processes.
Our group focuses on addressing those scientific challenges through a combination of novel mixed-experimental-and-computational strategies that employ high levels of automation, data analytics methods, and multi-scale/multi-physics models as well as fundamental investigations of previously unexplored (and often previously undiscovered) phenomena.
Models, codes, and data developed in our work with our collaborators are used by others for a variety of applications ranging from understanding fundamental combustion phenomena to computing emissions from high-efficiency, low-temperature engine concepts to predicting the fate of compounds in the troposphere.