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Low-temperature combustion technology may save energy and cut emissions.
To flip an old saying, where there's fire, there's smoke. Perhaps no one knows that better than Jacqueline Chen, a mechanical engineer at Sandia National Laboratories who employs one of the world's fastest supercomputers to model combustion. With Sandia colleague mechanical engineer Chun Sang Yoo and computational scientist Ramanan Sankaran of Oak Ridge National Laboratory, Chen used the Cray XT4 Jaguar supercomputer at Oak Ridge to generate 120 terabytes (trillion bytes) of data about flames similar to those occurring during ignition and stabilization of diesel-engine jets. The data equaled more than five times the printed contents of the U.S. Library of Congress. In their simulation, the researchers burned one of the simplest hydrocarbon fuels—ethylene molecules. Whereas diesel fuels are more complex, ethylene is a commonly used fuel in laboratory experiments and a fuel fragment resulting from high-temperature initiation reactions for autoignition of n-heptane, a diesel surrogate. Engineers are using Chen's data library to develop predictive models that will be used in the future to optimize designs for diesel engines and industrial boilers with reduced emissions and increased efficiency. Since diesel fuel powers most semi trucks, delivery vehicles, buses, trains, boats and farm, construction and military vehicles and equipment in the United States, development of advanced diesel technology is a leading near-term option by which the country could reduce fuel consumption and greenhouse gas emissions. "If low-temperature compression ignition systems employing dilute fuel mixtures make their way into next-generation autos, fuel efficiency could increase by as much as 25 to 50 percent," Chen says. The new technology would also make it possible to meet future low-emission vehicle standards with almost undetectable emissions of nitrogen oxide, a major contributor to smog, she adds. Chen, Yoo and Sankaran created the first three-dimensional simulation that fully resolves flame features, such as chemical composition, temperature profile and flow characteristics. The model shows feature detail on all size scales—the biggest, the smallest and everything in between—of a turbulent fuel jet igniting in a hot coflowing airstream.
The researchers modeled in unprecedented detail what happens in the so-called "lifted flames" relevant to industrial boilers and diesel engines. Unlike spark-plug ignition systems in automobiles powered by gasoline in which the fuel and oxidizer (air) are premixed, diesel-injection systems have the diesel fuel entering the engine full of hot air via jet nozzles. Turbulence mixes the fuel and air. Pistons subject the air/fuel mixture to pressure, and the mixture heats further, spurring a chemical pathway that sharply increases the concentration of a highly reactive chemical, hydroperoxyl radical, Chen says. The hydroperoxyl radical produces heat that spurs the production of other radicals that eventually leads to thermal runaway. At about 2,330°F, the fuel/air mixture autoignites, or bursts into flame, as a result of the rapid, heat-producing oxidation of its own constituents, regardless of heat from external sources. This process creates a lifted flame. The temperature peaks in excess of 3,140°F. "Autoignition is helpful because it stabilizes the flame," Chen says. "A hot, vitiated coflow supports its existence." Before this work by Chen and her colleagues, scientists had modeled only large eddies, or turbulent curlicues, in a burning fuel. They had not simulated the full range of scales down to the smallest eddies, which dictate the viscosity of the system and dissipate heat. Equipped with greater computational power, researchers can now resolve the nitty-gritty of the small eddies responsible for flame extinction and reignition in canonical flows with a moderate Reynolds number, which indicates the range of scales in a system. The combustion research at ORNL is supported by the Department of Energy through the agency's Innovative and Novel Computational Impact on Theory and Experiment program. The program seeks to address scientific "grand challenges" by granting large allocations of supercomputing time to approximately 25 peer-reviewed projects each year, including proprietary projects in partnership with private industry. The program is unique in the ability to provide scientists with the computational tools required to address problems too large and complex for most research institutions.—Dawn Levy
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