Search Results

Investigating combustion characteristics of oxygenated gasoline and gasoline blended ethanol is a subject of recent interest. The non-linearity in the interaction of fuel components in the oxygenated gasoline can be studied by developing chemical kinetics of relevant surrogate of fewer components. This work proposes a new reduced four-component (isooctane, heptane, toluene, and ethanol) oxygenated gasoline surrogate mechanism consisting of 67 species and 325 reactions, applicable for dynamic CFD applications in engine combustion and sprays. The model introduces the addition of eight C1-C3 species into the previous model (Li et al; 2019) followed by extensive tuning of reaction rate constants of C7 - C8 chemistry. The current mechanism delivers excellent prediction capabilities in comprehensive combustion applications with an improved performance in lean conditions.

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.

Toluene primary reference fuel (TPRF) (mixture of toluene, iso-octane and heptane) is a suitable surrogate to represent a wide spectrum of real fuels with varying octane sensitivity. Investigating different surrogates in engine simulations is a prerequisite to identify the best matching mixture. However, running 3D engine simulations using detailed models is currently impossible and reduction of detailed models is essential. This work presents an AramcoMech reduced kinetic model developed at King Abdullah University of Science and Technology (KAUST) for simulating complex TPRF surrogate blends. A semi-decoupling approach was used together with species and reaction lumping to obtain a reduced kinetic model. The model was widely validated against experimental data including shock tube ignition delay times and premixed laminar flame speeds. Finally, the model was utilized to simulate the combustion of a low reactivity gasoline fuel under partially premixed combustion conditions.