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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.

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.

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.

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.

In this work a new design of a liquid fuelled combustion engine is proposed for small and light weight unmanned air vehicles (<10kg and 15-200N thrust). Ethanol and gasoline were selected as the potential fuels while pressurized air and hydrogen peroxide were used as the oxidizer. The engine combines features of both a common rocket and turbojet engine. The main features of the engine are the restart ability during flight, low cost, easy manufacturability, light weight, long operation time and high durability. The main difficulties that come along with this engine are the need for proper engine cooling (long term operation) and start-up ability at atmospheric conditions. The low temperatures and injection pressures are not favorable for the fuel atomization and ignition. The paper focuses on the design on low pressure injectors and a start-up mechanism for micro UAV's without the use of a large amount of additional fueling circuits or components.

Diesel spray experimentation at controlled high-temperature and high-pressure conditions is intended to provide a more fundamental understanding of diesel combustion than can be achieved in engine experiments. This level of understanding is needed to develop the high-fidelity multi-scale CFD models that will be used to optimize future engine designs. Several spray chamber facilities capable of high-temperature, high-pressure conditions typical of engine combustion have been developed, but because of the uniqueness of each facility, there are uncertainties about their operation. The Engine Combustion Network (ECN) is a worldwide group of institutions using combustion vessels, whose aim is to advance the state of spray and combustion knowledge at engine-relevant conditions. A key activity is the use of spray chamber facilities operated at specific target conditions in order to leverage research capabilities and advanced diagnostics of all ECN participants.