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Compressed natural gas direct-injection (CNG-DI) engines based on diesel cycle combustion system with pilot ignition have ability to achieve high thermal efficiency and low emissions. Generally, underexpanded jets can be formed when the high pressure natural gas is injected into the combustion chamber. In such conditions, shock wave phenomena are the typical behaviors of the jet, which can significantly influence the downstream flow structure and turbulent mixing. In the present study, the characteristics of high-pressure transient jets were investigated using planar laser-induced fluorescence (PLIF) of acetone as a fuel tracer. The evolution of the pulsed jet shows that there are three typical jet flow patterns (subsonic, moderately underexpanded, and highly underexpanded) during the injection. The full injection process of high-pressure pulsed jets is well described with the help of these shock wave structures.

Dual fuel (DF) combustion technology as a feasible approach controlling engine-out emissions facilitates the concept of fuel flexibility in diesel engines. The abundance of natural gas (90-95% methane) and its relatively low-price and the clean-burning characteristic has attracted the interest of engine manufacturers. Moreover, with the low C/H ratio and very low soot producing tendency of methane combined with high engine efficiency makes it a viable primary fuel for diesel engines. However, the fundamental knowledge on in-cylinder combustion phenomena still remains limited and needs to be studied for further advances in the research on DF technology. The objective of this study is to investigate the ignition delay with the effect of, 1) methane equivalence ratio, 2) intake air temperature and 3) pilot ratio on the diesel-methane DF-combustion. Combustion phenomenon was visualized in a single cylinder heavy-duty diesel engine modified for DF operations with an optical access.

Knocking combustion is a major obstacle towards engine downsizing and boosting—popular techniques towards meeting the increasingly stringent emission standards of SI engines. The commercially available gasoline is a mixture of many chemical compounds like paraffins, isoparaffins, olefins and aromatics⁠. Therefore, the modeling of its combustion process is a difficult task. Additionally, the blends of certain compounds exhibit non-linear behavior in comparison to the pure components in terms of knock resistance. These facts require further analysis from the perspective of combustion chemistry. The present work analyses the effects of blending ethanol to FACE-C gasoline. A range of pressures, temperatures, and equivalence ratios has been considered for this purpose. The open source softwares Cantera version 2.4.0 and OpenSMOKE++ Suite have been used for the simulations.

As CO2 emissions from traffic must be reduced and fossil-based traffic fuels need to phase out, bio-based traffic fuels alone cannot meet the future demand due to their restricted availability. Another way to support fossil phase-out is to include synthetic fuels that are produced from circular carbon sources with renewable energy. Several different fuel types have been proposed, while, methanol only requires little processing from raw materials and could be used directly or as a drop-in fuel for some of the current engine fleet. CO2 emissions arising from fuel production are significantly reduced for synthetic renewable methanol compared to the production of fossil gasoline. Methanol has numerous advantages over the currently used fossil fuels with high RON and flame speed in spark-ignition engines as well as high efficiency and low emissions in combustion ignition engines.

Gasoline blending is known to be complicated, because individual gasoline fractions with different octane numbers, Research Octane Number (RON) or Motor Octane Number (MON) do not always blend linearly. Instead, they may blend non-linearly, in a synergistic or antagonistic manner. Even though RON and MON are regulated properties, linear and non-linear octane blending is not a broadly understood topic. The target in the developing process of a modern SI engine is to have 100% combustion efficiency which would lead to the reduction of hydrocarbon and carbon monoxide emissions. Therefore, the properties of gasoline, especially RON and MON, need to be optimized to ensure proper ignition in the engine and prevent harmful autoignition reactions. There are hundreds of hydrocarbons in gasoline which have different octane numbers (ON). The explanations for these variations are the structural differences in hydrocarbon molecules that influence on their reactivity.

The direct injection (DI) natural gas engine is considered as one of the promising technologies to achieve the continuing goals of the higher efficiency and reduced emissions for internal combustion engines. Shock wave phenomena can easily occur near the nozzle exit when high pressure gaseous fuel is injected directly into the engine cylinder. In the present study, high pressure gas issuing from a prototype gas injector was experimentally studied using planar laser-induced fluorescence (PLIF) technique. Acetone was selected as a fuel tracer. The effects of injection pressures on the flow structure and turbulent mixing were investigated based on a series of high resolution images. The jet macroscopic structures, such as jet penetration, cone angle and jet volume, are analyzed under different injection pressures. Results show that barrel shock waves can significantly influence the jet flow structure and turbulent mixing.

The aim of current study was to compare the global fuel spray characteristics between renewable hydrotreated vegetable oil (HVO) and crude oil-based EN 590 diesel fuel. According to previous studies, the use of HVO enables reductions in carbon monoxide (CO), total hydrocarbon (THC), nitrogen oxide (NOx) and particle matter (PM) emissions without any changes to the engine or its controls. Fuel injection strategies and global fuel spray characteristics affect on engine combustion and exhaust gas emissions. Due to different physical properties of two different fuels, fuel spray characteristics differ. Fuel spray studies were performed with backlight imaging using a pressurized test chamber imitating real engine conditions at the end of compression stroke. However, the measurements were made in non-evaporative conditions. Various injection parameters such as injection pressures and orifice diameter were tested.

Natural gas has been considered as one promising alternative fuel for internal combustion (IC) engines to meet strict engine emission regulations and reduce the dependence on petroleum oil. Although compressed natural gas (CNG) intake manifold injection has been successfully applied into spark ignition (SI) engines in the past decade, natural gas direct injection compression ignition (DICI) engine with new injection system is being pursued to improve engine performance. Gas jet behaves significantly different from liquid fuels, so the better understanding of the effects of gas jet on fuel distribution and mixing process is essential for combustion and emission optimization. The present work is aimed to gain further insight into the characteristics of low pressure gas jet. An experimental gas jet investigation has been successfully conducted using tracer-based planar laser-induced fluorescence (PLIF) technique. For safety reason, nitrogen (N₂) was instead of CNG in this study.

Hydrogen (H2), a potential carbon-neutral fuel, has attracted considerable attention in the automotive industry for transition toward zero-emission. Since the H2 jet dynamics play a significant role in the fuel/air mixing process of direct injection spark ignition (DISI) engines, the current study focuses on experimental and numerical investigation of a low-pressure H2 jet to assess its mixing behavior. In the experimental campaign, high-speed z-type schlieren imaging is applied in a constant volume chamber and H2 jet characteristics (penetration and cross-sectional area) are calculated by MATLAB and Python-based image post-processing. In addition, the Unsteady Reynolds-Averaged Navier-Stokes (URANS) approach is used in the commercial software Star-CCM+ for numerical simulations.

Over the past few years, the growing concerns about global warming and efforts to reduce engine-out emissions have made the dual-fuel (DF) engines more popular in marine and power industries. The use of natural gas as an alternative fuel in DF engines has both the environmental and economic advantages over the conventional diesel combustion. However, the misfire phenomenon at lean conditions limits the operating range of DF combustion and causes emissions of unburned hydrocarbon (UHC) and unburned methane (methane-slip) in the environment. The greenhouse effect of methane is considered 28 times greater than CO2 over a 100-year perspective, which raises concerns for the governments and marine engine manufacturers. In efforts to reduce the UHC and methane-slip from DF engines, this study discusses ethane enrichment of diesel-methane DF combustion in a full-metal single-cylinder research engine under lean condition (λGFB = ~2.0) while keeping the total-fuel energy rather constant.

The present-day transport sector needs sustainable energy solutions. Substitution of fossil-fuels with fuels produced from biomass is one of the most relevant solutions for the sector. Nevertheless, bringing biofuels into the market is associated with many challenges that policymakers, feedstock suppliers, fuel producers, and engine manufacturers need to overcome. The main objective of this research is an investigation of the impact of alternative fuel properties on light vehicle engine performance and greenhouse gases (GHG). The purpose of the present study is to provide decision-makers with tools that will accelerate the implementation of biofuels into the market. As a result, two models were developed, that represent the impact of fuel properties on engine performance in a uniform and reliable way but also with very high accuracy (coefficients of determination over 0.95) and from the end-user point of view.

This study presents a novel crank mechanism which enables easy and fast compression ratio adjustment. The novel crank mechanism and piston travel are explained and highlighted. The basic idea is that eccentric gear is installed on a crankshaft web. Eccentric gear is fitted to the big end of the connection rod and eccentricity is controlled by rotating the control gear a discrete amount. Thus the position of eccentricity is varied and controls an effective stroke length. The compression ratio is adjusted to best fit current load demand, either optimizing fuel efficiency or engine power and torque. Adjustments are individual to each cylinder. The system is capable of adjusting from min to max within 10 milliseconds [ms]. Emphasis is on reduction of CO2 emissions and reducing fuel consumption, especially at part load condition. The governing mechanical equations are presented.

Pre-ignition in a boosted spark-ignition engine can be triggered by several mechanisms, including oil-fuel droplets, deposits, overheated engine components and gas-phase autoignition of the fuel-air mixture. A high pre-ignition resistance of the fuel used mitigates the risk of engine damage, since pre-ignition can evolve into super-knock. This paper presents the pre-ignition propensities of 11 RON 89-100+ gasoline fuel blends in a single-cylinder research engine. Albeit the addition of two high-octane components (methanol and reformate) to a toluene primary reference fuel improved the pre-ignition resistance, one high-RON fuel experienced runaway pre-ignition at relatively low boost pressure levels. A comparison of RON 96 blends showed that the fuel composition can affect pre-ignition resistance at constant RON.

Distillation properties of gasoline are regulated to ensure the safe and efficient operation of SI-engines. Blending various gasoline components affects the distillation values in a non-linear fashion, making the prediction of these properties challenging. Furthermore, the rise of renewable components necessitates the development of new property prediction methods. In this work, a variety of Machine Learning models were created to predict the distillation points of gasoline blends based on the blending recipe. As input data, real industrial data from a refinery was used together with data from blends created for R&D purposes. The predicted properties were the evaporated volume at the 70 and 100 °C distillation points (E70 and E100). Altogether four different machine learning models were trained, cross-validated and tested using seven different pre-processing methods. It was found that Support Vector Regression (SVR) was the most effective at predicting the distillation points.