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Recent research has shown that gasoline compression ignition (GCI) improves the soot-NOx tradeoff of traditional diesel engines due to the beneficial properties of light distillate fuels. However, system level optimization of a new engine concept is ultimately needed to maximize fuel economy and emissions improvements. Along with air and aftertreatment systems, the fuel system also requires further development to enable GCI. One important design tool for fuel system hardware is 1-D hydraulic modeling. Although accurate tabulations of diesel or equivalent calibration fluid properties are available in 1-D modelling software packages, the same situation does not exist for gasoline-like fuels, especially at conditions encountered in the high-pressure injection equipment needed to support GCI. This study presents a methodology for generating accurate liquid property databases of complex, multi-component light distillate fuels that can be used in high-pressure 1-D hydraulic models.

This research evaluates the capability of data-science models to classify the combustion events in Cooperative Fuel Research Engine (CFR) operated under Homogeneous Charge Compression Ignition (HCCI) conditions. A total of 10,395 experimental data from the CFR engine at the University of Michigan (UM), operated under different input conditions for 15 different fuel blends, were utilized for the study. The combustion events happening under HCCI conditions in the CFR engine are classified into four different modes depending on the combustion phasing and cyclic variability (COVimep). The classes are; no ignition/high COVimep, operable combustion, high MPRR, and early CA50. Two machine learning (ML) models, K-nearest neighbors (KNN) and Support Vector Machines (SVM), are compared for their classification capabilities of combustion events. Seven conditions are used as the input features for the ML models viz.