Reducing emissions from internal combustion engines while improving fuel economy is a challenging task, requiring new strategies such as lean burn engines.In lean burn engines, the air fuel ratio is above stoichiometric, meaning that there is excess oxygen available relative to the mass of fuel. This approach offers lower combustion temperatures, and reduced formation of NOx, a major pollutant. One specific approach to lean combustion is Homogeneous Charge Compression Ignition (HCCI).Understanding the effects of thermal and mixture stratification is required to avoid preignition of the fuel, more commonly known as “knock,” and optimize HCCI engines for use.Simulation of HCCI, and all other combustion engine configurations, requires capturing multiple complex physics, including fuel vaporization, turbulent mixing and heat transfer, and complex chemical kinetics, across a range of length and time scales. Modeling of the chemical kinetics is particularly challenging, since thousands of chemical species may be present during the combustion process. In many cases, surrogate fuels, which simplify the chemistry and physical behavior of real fuels to allow simulation, are used to study combustion. The current work takes a two step approach using CONVERGE, a commercially available computational Fluid Dynamics (CFD) code, along with a customized user defined function (UDF) that take advantages of Titan’s Graphical Processor Units (GPUs) to solve chemical kinetics.In the first phase of the work, detailed kinetics simulations will be used to evaluate publicly available surrogate components, which are mostly focused on matching ignition of homogeneous mixtures. In the second step, surrogate components will be validated against vaporization measurements to best model the fuel mixing process. These results will be used to capture the in cylinder mixture stratification and combustion processes with sufficient accuracy to resolve the underlying physics leading to HCCI engine knock reduction.
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