An Approach for Formulating Surrogates for Gasoline with Application toward a Reduced Surrogate Mechanism for CFD Engine Modeling

The numerical study of engine combustion requires the coupling of advanced computational fluid dynamics and accurate chemical kinetic models. This task becomes extremely challenging for real fuels. Gasoline is a mixture of hundreds of different hydrocarbons. Detailed modeling of its chemistry requires huge numbers of species and reactions and exceeds present numerical capabilities. Consequently, simpler surrogate mixtures are adopted to approximate the behavior of the real fuels. Large kinetic models for surrogates are developed to characterize their chemistry, but these models still contain thousands of species and reactions and can usually only be used for simulating simple homogeneous systems. For multidimensional engine applications, they must be reduced. In this work, we propose a methodology for the formulation of a gasoline surrogates based on the intrinsic qualities of the fuel chemical behavior. Using the proposed procedure, a candidate surrogate containing four components has been identified to match a real nonoxygenated gasoline. Starting from this formulation, the LLNL (Lawrence Livermore National Laboratory) detailed kinetic mechanism has been reduced while maintaining its ability to reproduce targets of ignition delay times and flame speeds over a wide range of operating conditions. The reduction was carried by the construction of a preliminary version of a skeletal mechanism using the Computer Assisted Reduction Mechanism (CARM) code under a set of targeted conditions. Further reduction is made with a search algorithm that sequentially tests the importance of each species, leading to a much smaller mechanism. Finally, the resulting reduced mechanism has been validated against the detailed mechanism and available experimental data.