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Methanol has recently emerged as a promising fuel for internal combustion engines due to its multiple carbon-neutral production routes and advantageous properties when combusting. Methanol is intrinsically more suitable for spark-ignition (SI) operation thanks to its high octane number, but its potential in heavy-duty applications also encourages engine manufacturers in this field to retrofit their existing compression-ignition products into methanol/diesel dual-fuel (DF) operation. For both SI operation and DF operation, injecting methanol into the engine’s intake path at low pressure is a relatively simple and robust method to introduce methanol into the cylinders. However, the much higher heat of vaporization (HoV) of methanol compared to conventional SI fuels like gasoline can be a double-edged sword.

In the upcoming decade sustainable powertrain technologies will seek for market entrance in the transport sector. One promising solution is the utilization of dual-fuel engines using renewable methanol ignited by a pilot diesel fuel. This approach allows the displacement of a significant portion of fossil diesel, thereby reducing greenhouse gas emissions. Additionally, this technology is, next to newbuilds, suited for retrofitting existing engines, while maintaining high efficiencies and lowering engine-out emissions. Various researchers have experimentally tested the effects of replacing diesel by methanol and have reported different boundaries for substituting diesel by methanol, including misfire, partial burn, knock and pre-ignition. However, little research has been conducted to explore ways to extend these substitution limits.

The use of renewable fuels such as hydrogen and methanol in marine engines is a promising way to reduce greenhouse gas emissions from maritime transport. Hydrogen and methanol can be used as the main fuel in dual-fuel engines. However, the co-combustion of hydrogen-diesel and methanol-diesel needs to be carefully studied. In the present work, the ignition delay (ID) and laminar burning velocity (LBV) for pilot-ignited dual fuel engine operation with hydrogen or methanol are studied. A constant volume batch reactor numerical setup is used in the open source Cantera code to calculate the effect of the premixed fuel on the ID of the pilot fuel. Also, Cantera is used to simulate a freely-propagating, adiabatic, 1-D flame to estimate the laminar flame speed of either hydrogen or methanol and how it is affected by the presence of pilot fuel. First, suitable chemical kinetic schemes are selected based on experimental data collected from the literature.

To mitigate climate change, it is essential that sustainable technologies emerge in the transport industry. One viable solution is the use of methanol or hydrogen combined with internal combustion engines (ICEs). The dual-fuel technology in particular, in which a diesel pilot ignites port fuel injected methanol or hydrogen, is of great interest to transition from diesel engines to ICEs using purely these fuels. This approach allows for a significant portion of fossil diesel to be replaced with sustainable methanol or hydrogen, while maintaining high efficiencies and the possibility to run solely on diesel if required. Additionally, lower engine-out pollutant emissions (NOx, soot) are produced. Although multiple experimental research results are available, numerical literature on both fuels in dual-fuel mode is scarce. Therefore, this study aims to develop a multi-zone dual-fuel combustion model for engine simulations.

Non-Road Mobile Machinery (NRMM) incorporates a wide variety of machines not intended for the transport of passengers or goods on the road. This includes small gardening equipment, construction, mining, agricultural, and forestry machinery up to locomotives and inland waterway vessels, mostly using an internal combustion engine. NRMM was often overlooked and neglected in the past when considering pollutant and greenhouse gas emissions. Due to their high diversity, they are hard to categorize, resulting in a lack of available data. As emissions from road transport are being tackled by regulations, the emissions of NRMM become an increasing part of total transport emissions. An alternative to fossil fuels will be required for the energy supply of NRMM to fully commit to the CO2 reduction goals, and to fulfil the future requirements of legislators and public opinion.

In the search for sustainable transportation fuels that are not in competition with food production, considerable efforts are made in the development of so-called second-generation (2G) biofuels. This paper looks into the results of a novel 2G biofuel production technique that is based on a catalytic process that operates at low temperature and that converts woody biomass feedstock into a stable light naphtha. The process development is integrated in the Belgian federal government funded Ad-Libio project and the process outcome is mainly consisting of hydrocarbons containing five to six carbon atoms. Their composition can be altered, resulting in a large amount of different possible fuel blends. The ultimate goal is to produce a drop-in fuel that can be fully interchanged with the gasoline fuels in use today. This is a challenge, since the Ad-Libio fuel components differ significantly from gasoline fuel components.

Despite the increasing number of electrified vehicles the transportation system still largely depends on the use of fossil fuels. One way to more rapidly reduce the dependency on fossil fuels in transport is to replace them with biofuels. Evaluating the potential of different biofuels in different applications requires knowledge of their physicochemical properties. In chemistry, message passing neural networks (MPNNs) correlating the atoms and bonds of a molecule to properties have shown promising results in predicting the properties of individual chemical components. In this article a machine learning approach, developed from the message passing neural network called Chemprop, is evaluated for the prediction of multiple properties of organic molecules (containing carbon, nitrogen, oxygen and hydrogen). A novel approach using transfer learning based on estimated property values from theoretical estimation methods is applied.

An increasing need to lower greenhouse gas emissions, and so move away from fossil fuels like diesel and gasoline, has greatly increased the interest for methanol. Methanol can be produced from renewable sources and eliminate soot emissions from combustion engines [1]. Since compression ignition (CI) engines are used for the majority of commercial applications, research is intensifying into the use of methanol, as a replacement for diesel fuel, in CI engines. This includes work on dual-fuel set-ups, different fuel blends with methanol, ignition enhancers mixed with methanol, and partially premixed combustion (PPC) strategies with methanol. However, methanol is difficult to ignite, using compression alone, at low load conditions. The problem comes from methanol’s high octane number, low lower heating value and high heat of vaporization, which add up to a lot of heat being needed from the start to combust methanol [2].

In order to reduce the carbon footprint of the Internal Combustion Engine (ICE), biofuels have been in use for a number of years. One of the problems with first-generation (1G) biofuels however is their competition with food production. In search of second-generation (2G) biofuels, that are not in competition with food agriculture, a novel biorefinery process has been developed to produce biofuel from woody biomass sources. This novel technique, part of the Belgian federal government funded Ad-Libio project, uses a catalytic process that operates at low temperature and is able to convert 2G feedstock into a stable light naphtha. The bulk of the yield consists out of hydrocarbons containing five to six carbon atoms, along with a fraction of oxygenates and aromatics. The oxygen content and the aromaticity of the hydrocarbons can be varied, both of which have a significant influence on the fuel’s combustion and emission characteristics when used in Internal Combustion Engines.

To speed up the development of the next-generation combustion engines with renewable fuels, the importance of reliable and robust simulations cannot be overemphasized. Compared to gasoline, methanol is a promising fuel for spark-ignited engines due to its higher research octane number to resist auto-ignition, higher flame speed for faster combustion and higher heat of vaporization for intake charge cooling. These advantageous properties all contribute to higher thermal efficiency and lower knock tendency, and they need to be well-captured in the simulation environment in order to generate accurate predictions. In this paper, the sub-models which estimate the burning velocities and ignition delay of methanol are revisited. These building blocks are implemented and integrated in a quasi-dimensional simulation environment to predict the combustion behavior, which are subsequently validated against test data measured on both light-duty and heavy-duty engines.

The large difference in fuel properties between methanol and gasoline demand the development of a dedicated spark ignition (SI) engine in order to exploit methanol’s properties for maximum thermal efficiency, rather than using the flex-fuel engines of today. In order to develop such an engine, proven technologies on a high efficiency gasoline engine are a good reference point to start with. The engine setup used in this work was a 1.6l turbocharged direct injection engine equipped with variable valve timing (VVT) and a low pressure EGR loop. A central composite design (CCD) was used to quantify the influence of five control parameters on the brake thermal efficiency (BTE) and main energy losses when running the engine on methanol at full load and a fixed engine speed of 1700 rpm. The set of control parameters consisted of the intake valve opening timing, exhaust valve opening timing, opening of the waste gate, opening of the EGR valve and opening of the backpressure valve.

Methanol is a promising fuel for future spark-ignition engines. Its properties enable increased engine efficiency. Moreover, the ease with which methanol can be reformed, using waste exhaust heat, potentially offers a pathway to even higher efficiencies. The primary objective of this study was to build and validate a model for a methanol fueled direct-injection spark-ignition engine with on-board fuel reforming for future investigation and optimization. The second objective was to understand the combustion characteristics, energy losses and engine efficiency. The base engine model was developed and calibrated before adding a reformed-exhaust gas recirculation system (R-EGR). A newly developed laminar burning velocity correlation with universal dilution term was implemented into the model to predict the laminar burning velocity with the presence of hydrogen in the reforming products.

In this work, the possibility to perform a cold-flow simulation as a way to improve the accuracy of the starting conditions for a combustion simulation is examined. Specifically, a dual-fuel marine engine running on methanol/diesel and natural gas/diesel fueling conditions is investigated. Dual-fuel engines can provide a short-term solution to cope with the more stringent emission legislations in the maritime sector. Both natural gas and methanol appear to be interesting alternative fuels that can be used as main fuel in these dual-fuel engines. Nevertheless, it is observed that combustion problems occur at part load using these alternative fuels. Therefore, different methods to increase the combustion efficiency at part load are investigated. Numerical simulations prove to be very suitable hereto, as they are an efficient way to study the effect of different parameters on the combustion characteristics.

The present work describes the numerical modeling of medium-speed marine engines, operating in a fumigated dual-fuel mode, i.e. with the second fuel injected in the ports. This engine technology allows reducing engine-out emissions while maintaining the engine efficiency and can be fairly easily retrofitted from current diesel engines. The main premixed fuel that is added can be a low-carbon one and can additionally be of a renewable nature, thereby reducing or even completely removing the global warming impact. To fully optimize the operational parameters of such a large marine engine, computational fluid dynamics can be very helpful. Accurately describing the combustion process in such an engine is key, as the prediction of the heat release and the pollutant formation is crucial. Auto-ignition of the diesel fuel needs to be captured, followed by the combustion and flame propagation of the premixed fuel.

Lean operation is a promising approach to increase the engine efficiency. One of the main challenges for lean-burn technology is the combustion instability. Using a high laminar burning velocity fuel such as methanol might solve that problem. The potential of lean-burn limit extension with methanol was investigated through a comparison with conventional gasoline. In this work, a direct injection turbocharged SI engine was operated at wide open throttle (WOT), with the load controlled by a lean-burn strategy. The amount of fuel was decreased (or lambda increased) until the combustion became unstable. For methanol, the lambda limit was about 1.5, higher than the lambda limit for gasoline which was only about 1.2. The brake thermal efficiency for methanol increased as lambda increased and reached its peak at ~41% in a lambda range of 1.2-1.4. Then, the efficiency decreased as lambda increased.

Low Temperature Combustion is a technology that enables achieving both a higher efficiency and simultaneously lower emissions of NOx and particulate matter. It is a noun for combustion regimes that operate with a lean air-fuel mixture and where the combustion occurs at a low temperature, such as Homogeneous Charge Compression Ignition and Partially Premixed Combustion. In this work a new model is proposed to predict the instantaneous heat flux in engines with Low Temperature Combustion. In-cylinder heat flux measurements were used to construct this model. The new model addresses two shortcomings of the existing heat transfer models already present during motored operation: the phasing of the instantaneous heat flux and the overprediction of the heat flux during the expansion phase. This was achieved by implementing the in-cylinder turbulence in the heat transfer model. The heat transfer during the combustion was taken into account by using the turbulence generated in the burned zone.

Methanol is gaining traction in some regions, e.g. for road transportation in China and for marine transportation in Europe. In this research, the possibility for achieving higher power output and higher efficiency with methanol, compared to gasoline, is investigated and the influence of several engine settings, such as valve timing and intake boost control, is studied. At wide open throttle (WOT), engine speed of 1650 rpm, the brake mean effective pressure (BMEP) of the methanol-fueled engine is higher than on gasoline, by around 1.8 bar. The maximum BMEP is further increased when positive valve overlap and higher intake boost pressure are applied. Thanks to a lower residual gas fraction, and a richer in-cylinder mixture with positive valve overlap period, the engine BMEP improves by a further 2.6 bar. Because of higher volumetric efficiency with a boosted intake air, the engine BMEP enhances with 4.7 bar.

The present work details a study of the heat flux through the walls of an internal combustion engine. The determination of this heat flux is an important aspect in engine optimization, as it influences the power, efficiency and the emissions of the engine. Therefore, a set of simulation tools in the OpenFOAM® software has been developed, that allows the calculation of the heat transfer through engine walls for ICEs. Normal practice in these types of engine simulations is to apply a wall function model to calculate the heat flux, rather than resolving the complete thermo-viscous boundary layer, and perform simulations of the closed engine cycle. When dealing with a complex engine, this methodology will reduce the overall computational cost. It however increases the need to rely on assumptions on both the initial flow field and the behavior in the near-wall region.

Engine optimization requires a good understanding of the in-cylinder heat transfer since it affects the power output, engine efficiency and emissions of the engine. However little is known about the convective heat transfer inside the combustion chamber due to its complexity. To aid the understanding of the heat transfer phenomena in a Spark Ignition (SI) engine, accurate measurements of the local instantaneous heat flux are wanted. An improved understanding will lead to better heat transfer modelling, which will improve the accuracy of current simulation software. In this research, prototype thin film gauge (TFG) heat flux sensors are used to capture the transient in-cylinder heat flux within a Cooperative Fuel Research (CFR) engine. A two-zone temperature model is linked with the heat flux data. This allows the distinction between the convection coefficient in the unburned and burned zone.

Methanol fueled spark ignition (SI) engines have the potential for very high efficiency using an advanced heat recovery system for fuel reforming. In order to allow simulation of such an engine system, several sub-models are needed. This paper reports the development of two laminar burning velocity correlations, corresponding to two reforming concepts, one in which the reformer uses water from an extra tank to produce hydrogen rich gas (syngas) and another that employs the water vapor in the exhaust gas recirculation (EGR) stream to produce reformed-EGR (R-EGR). This work uses a one-dimensional (1D) flame simulation tool with a comprehensive chemical kinetic mechanism to predict the laminar burning velocities of methanol/syngas blends and correlate it. The syngas is a mixture of H2/CO/CO2 with a CO selectivity of 6.5% to simulate the methanol steam reforming products over a Cu-Mn/Al catalyst.