Michael Burke | Dissecting Chemical Processes for Fuel Combustion

Reducing oil use, protecting the environment, and cutting costs are all significant advantages of the newest generation of high-efficiency, low-emissions engines. In turn, these factors influence the buying power of many audiences including municipalities, companies, and consumers. But well before these engines reach their intended market, engineers often rely on advanced computer simulations, a fundamental tool of the trade, to test and improve an engine’s performance, fuel efficiency, and environmental discharges.

Michael Burke
Michael Burke
Assistant Professor of Mechanical Engineering
—Photo by Jeffrey Schifman

That’s where Michael Burke comes in, with a multi-disciplinary approach combining his expertise in engineering and chemistry.

“I focus on data-driven approaches to creating the chemical models used in advanced engine simulations—approaches that incorporate both experimental and theoretical data to produce highly accurate predictions with quantified uncertainties,” says Michael Burke, assistant professor of mechanical engineering and a member of Columbia’s Data Science Institute.

A major component of these computer simulations is the chemical model that foretells how the particular fuel will react, how quickly it will react, and what it will discharge into the air. As part of the process, Burke studies the oxidation chemistry related to the combustion of nearly all practical fuels—from conventional gasoline to alternative bio-derived fuels—in a variety of engines such as spark-ignition engines that power passenger vehicles and gas turbine engines that power airplanes and generate electricity.

Burke’s approach is to analyze everything from small-scale electronic behavior that controls molecular reactivity to large-scale, turbulent, reactive phenomena that directs engine performance. His primary research interests are in mixed-experimental-and-computational investigations of advanced combustion and energy systems that utilize multi-scale modeling, automation, and data sciences.

“The advantage of constructing models in this data-driven, multi-scale manner is that the models produced have a strong fundamental physical basis, allowing for reliable extrapolation from the limited conditions where data is available to very different and often extreme conditions that might be encountered in advanced engines,” Burke notes.

Burke’s love of science started as an undergraduate at Penn State where he became hooked on examining thermal fluids, often involving generation of heat from chemical reactions and the transfer of heat and other quantities through a flowing medium. “Afterward, I gravitated toward advanced combustion and energy systems as a way to apply my thermal fluids knowledge to solve problems that can improve fuel efficiencies and reduce greenhouse gas and other emissions,” he says regarding his decision to pursue a PhD in combustion science at Princeton in mechanical engineering.

Based on his realizations that better combustion models required better fundamental chemistry, he worked in Argonne National Laboratory’s chemistry division as a director’s postdoctoral fellow before joining Columbia in July 2014. At the Engineering School, Burke teaches Introduction to Combustion. “What excites me most about teaching is the chance to help students further develop their ability to think,” he says. “I like to illustrate to the students how some concepts they learn about in class are not yet fully understood by anyone. Some of the major challenges in the state-of-the-art science and technology would benefit from fresh perspectives and insights that students could provide.”

BS, Penn State, 2005; PhD, Princeton University, 2011

—by Janet Haney

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