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Future internal combustion engines demand higher efficiency but progression towards this is limited by the phenomenon called knock. A possible solution for reaching high efficiency is Octane-on-Demand (OoD), which allows to customize the antiknock quality of a fuel through blending of high-octane fuel with a low octane fuel. Previous studies on Octane-on-Demand highlighted efficiency benefits depending on the combination of low octane fuel with high octane booster. The author recently published works with ethanol and methanol as high-octane fuels. The results of this work showed that the composition and octane number of the low octane fuel is significant for the blending octane number of both ethanol and methanol. This work focuses on toluene as the high octane fuel (RON 120). Aromatics offers anti-knock quality and with high octane number than alcohols, this work will address if toluene can provide higher octane enhancement.

Spray combustion processes of palm oil biodiesel (PO) and conventional diesel fuels were simulated using the CONVERGE CFD code. Thermochemical and reaction kinetic data (115 species and 460 reactions) by Luo et al. (2012) and Lu et al. (2009) (68 species and 283 reactions) were implemented in the CONVERGE CFD to simulate the spray and combustion processes of the two fuels. Tetradecane (C14H30) and n- heptane (C7H16) were used as surrogates for diesel. For the palm biodiesel, the mixture of methyl decanoate (C11H20O2), methyl-9-decenoate (C11H19O2) and n-heptane was used as surrogate. The palm biodiesel surrogates were combined in proportions based on the previous GC-MS results for the five major biodiesel components namely methyl palmitate, methyl stearate, methyl oleate, methyl linoleate and methyl linolenate.

Refinements were made to a post-processing technique, termed the Thermal Stratification Analysis (TSA), that couples the mass fraction burned data to ignition timing predictions from the autoignition integral to calculate an apparent temperature distribution from an experimental HCCI data point. Specifically, the analysis is expanded to include all of the mass in the cylinder by fitting the unburned mass with an exponential function, characteristic of the wall-affected region. The analysis-derived temperature distributions are then validated in two ways. First, the output data from CFD simulations are processed with the Thermal Stratification Analysis and the calculated temperature distributions are compared to the known CFD distributions.

Designing advanced combustion engines requires a better understanding of the physical and chemical processes occurring during spray combustion. In this study, the ignition characteristics of conventional diesel and palm biodiesel fuels were simulated using the two-stage Lagrangian (TSL) simulation, a zero dimensional (0-D) modeling technique. For the diesel fuel surrogate, a detailed chemical kinetic model for n-heptane from LLNL (Lawrence Livermore National Laboratory), with 550 chemical species and 2450 elementary reactions was utilized. For the palm biodiesel, detailed mechanism (4800 species and 2450 elementary reactions) for the 5 basic biodiesel components; methyl palmitate, methyl stearate, methyl oleate, methyl linoleate and methyl linolenate was used. Also, simulations were performed using a reduced mechanism (115 species and 460 reactions) for surrogates of palm oil biodiesel comprising mixtures of methyl decanoate, methyl decenoate and n-heptane.

A phenomenological description, or “conceptual model,” of how direct-injection (DI) diesel combustion occurs has been derived from laser-sheet imaging and other recent optical data. To provide background, the most relevant of the recent imaging data of the author and co-workers are presented and discussed, as are the relationships between the various imaging measurements. Where appropriate, other supporting data from the literature is also discussed. Then, this combined information is summarized in a series of idealized schematics that depict the combustion process for a typical, modern-diesel-engine condition. The schematics incorporate virtually all of the information provided by our recent imaging data including: liquid- and vapor-fuel zones, fuel/air mixing, autoignition, reaction zones, and soot distributions.

The effect of ethanol blended with three FACE (Fuels for Advanced Combustion Engines) gasolines, I, J and A corresponding to RON 70.3, 71.8 and 83.5, respectively, were compared to PRF70 and PRF84 with the same ethanol concentrations, these being 2%, 5%, 10%, 15% and 20% by volume. A Cooperative Fuel Research (CFR) engine was used to understand the blending effect of ethanol with FACE gasolines and PRFs in spark-ignited and homogeneous charge compression ignited mode. Blending octane numbers (BON) were obtained for both the modes. All the fuels were also tested in an ignition quality tester to obtain Blending Derived Cetane numbers (BDCN). It is shown that fuel composition and octane number are important characteristics of all the base fuels that have a significant impact on octane increase with ethanol. The dependency of octane number for the base fuel on the blending octane number depended on the combustion mode operated.

The homogeneous charge, compression-ignition (HCCI) combustion process has the potential to significantly reduce NOx and particulate emissions, while achieving high thermal efficiency and the capability of operating with a wide variety of fuels. This makes the HCCI engine an attractive technology that can ostensibly provide diesel-like fuel efficiency and very low emissions, which may allow emissions compliance to occur without relying on lean aftertreatment systems.

The accurate prediction of engine performance maps can guide data-driven optimization of engine technologies to control fuel use and associated emissions. However, engine operational maps are scarcely reported in literature and often have missing data. Assessment of missing-data resilient algorithms in the context of engine data prediction could enable better processing of real-world driving cycles, where missing data is a more pervasive phenomenon. The goal of this study is, therefore, to determine the most effective technique to deal with missing data and employ it in prediction of engine performance characteristics. We assess the performance of two machine learning approaches, namely Artificial Neural Networks (ANNs) and the extreme tree boosting algorithm (XGBoost), in handling missing data.