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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.

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.

Third generation advanced high strength steels (AHSS) have been developed combining high strength and formability, allowing for lightweighting of vehicle structural components. These AHSS components are exposed to paint baking operations ranging in time and temperature to cure the applied paint. The paint baking treatment, combined with straining induced from part forming, may lead to increased in-service component performance due to a strengthening mechanism known as bake hardening. This study aims to quantify the bake hardening behavior of select AHSS grades. Materials investigated were press hardenable steels (PHS) 1500 and 2000; transformation induced plasticity (TRIP) aided bainitic ferrite (TBF) 1000 and 1200; and dual phase (DP) 1000. The number designations of these grades refer to minimum as-received ultimate tensile strengths in MPa. Paint baking was simulated using industrially relevant times and temperatures from 15 to 60 min and 120 to 200 °C, respectively.

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.

This work establishes the relationship between core hardness, case hardness, and case depth on susceptibility to hydrogen embrittlement of case hardened steel fasteners. Such fasteners have a high surface hardness in order to create their own threads in a mating hole, and are commonly used to attach bracketry and sheet metal in automotive applications. While case hardened fasteners have been studied previously, there are currently no processing guidelines supported by quantitative data for fastener standards. Through sustained load embrittlement testing techniques, the susceptibility of case hardened steel tapping screws to internal and environmental hydrogen embrittlement is examined. Further characterization of the fastener samples through microhardness testing, microstructure review, and fracture surface examination allows the investigation of susceptibility thresholds. It is shown that core hardness is the primary consideration for susceptibility.

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.

Homogeneous Charge Compression Ignition (HCCI) engines can achieve both a high thermal efficiency and near-zero emissions of NOx and soot. However, their maximum attainable load is limited by the occurrence of a ringing combustion. At high loads, the fast combustion rate gives rise to pressure oscillations in the combustion chamber accompanied by a ringing or knocking sound. In this work, it is investigated how these pressure oscillations affect the in-cylinder heat transfer and what the best approach is to model the heat transfer during ringing combustion. The heat transfer is measured with a thermopile heat flux sensor inside a CFR engine converted to HCCI operation. A variation of the mass fuel rate at different compression ratios is performed to measure the heat transfer during three different operating conditions: no, light and severe ringing. The occurrence of ringing increases both the peak heat flux and the total heat loss.

To optimize internal combustion engines (ICEs), a good understanding of engine operation is essential. The heat transfer from the working gases to the combustion chamber walls plays an important role, not only for the performance, but also for the emissions of the engine. Besides, thermal management of ICEs is becoming more and more important as an additional tool for optimizing efficiency and emission aftertreatment. In contrast little is known about the convective heat transfer inside the combustion chamber due to the complexity of the working processes. Heat transfer measurements inside the combustion chamber pose a challenge in instrumentation due to the harsh environment. Additionally, the heat loss in a spark ignition (SI) engine shows a high temporal and spatial variation. This poses certain requirements on the heat flux sensor. In this paper we examine the heat transfer in a production SI ICE through the use of Thin Film Gauge (TFG) heat flux sensors.

The strain-induced diffusionless shear transformation of retained austenite to martensite during straining of transformation induced plasticity (TRIP) assisted steels increases strain hardening and delays necking and fracture leading to exceptional ductility and strength, which are attractive for automotive applications. A novel technique that provides the retained austenite volume fraction variation with strain with improved precision is presented. Digital images of the gauge section of tensile specimens were first recorded up to selected plastic strains with a stereo digital image correlation (DIC) system. The austenite volume fraction was measured by synchrotron X-ray diffraction from small squares cut from the gage section. Strain fields in the squares were then computed by localizing the strain measurement to the corresponding region of a given square during DIC post-processing of the images recorded during tensile testing.

Fracture split forged steel connecting rods are utilized in many new high performance automotive engines to increase durability. Higher strength levels are needed as the power density increases. Fracture splitting without plastic deformation is necessary for manufacturability. Metallurgical design is a key for achieving the required performance levels. Several medium carbon steels containing 0.07 wt pct P, 0.06 wt pct S and various amounts of Mn, Si, V, and N were produced by vacuum induction melting laboratory heats and hot working the cast ingots into plates. The plates were cooled at varying rates to simulate typical cooling methods after forging. Microstructures were generally ferrite and pearlite as evaluated by light optical and scanning electron microscopy. Mechanical properties were determined by standard tensile tests, high strain rate notched tensile tests, and Charpy V-notch impact tests to assess “splittability”.

Quenching and partitioning (Q&P) is a novel heat treatment to produce third generation advanced high-strength steels (AHSS). The influence of carbon on mechanical properties of Q&P treated CMnSi-steels was studied using 0.3C-1.5Mn-1.5Si and 0.4C-1.5Mn-1.5Si alloys. Full austenitization followed by two-step Q&P treatments were conducted using varying partitioning times and a fixed partitioning temperature of 400 °C. The results were compared to literature data for 0.2C-1.6Mn-1.6Si, 0.2-3Mn-1.6Si and 0.3-3Mn-1.6Si Q&P treated steels. The comparison showed that increasing the carbon content from 0.2 to 0.4 wt pct increased the ultimate tensile strength by 140 MPa per 0.1 wt pct C up to 1611 MPa without significantly decreasing ductility for the partitioning conditions used. Increased alloy carbon content did not substantially increase the retained austenite fractions. The best combinations of ultimate tensile strength and total elongation were obtained using short partitioning times.

In the development of internal combustion engines, measurements of the heat transfer to the cylinder walls play an important role. These measurements are necessary to provide data for building a model of the heat transfer, which can be used to further develop simulation tools for engine optimization. This research will focus on the Thin Film Gauge (TFG) heat flux sensor. This sensor consists of a platinum RTD (Resistance Temperature Detector) on an insulating Macor® (ceramic) substrate. The sensor has a high frequency response (up to 100 kHz) and is small and robust. These properties make the TFG sensor adequate for measurements in the combustion chamber of an internal combustion engine. To use this sensor, its thermal properties - namely the temperature sensitivity coefficient and the thermal product - must be correctly calibrated. First, different calibration setups with a different temperature range are used to calibrate the temperature sensitivity coefficient of the TFG sensor.

Homogeneous charge compression ignition (HCCI) engines are a promising alternative to traditional spark- and compression-ignition engines, due to their high thermal efficiency and near-zero emissions of NOx and soot. Simulation software is an essential tool in the development and optimization of these engines. The heat transfer submodel used in simulation software has a large influence on the accuracy of the simulation results, due to its significant effect on the combustion. In this work several empirical heat transfer models are assessed on their ability to accurately predict the heat flux in a CFR engine during HCCI operation. Models are investigated that are developed for traditional spark- and compression-ignition engines such as those from Annand [1], Woschni [2] and Hohenberg [3] and also models developed for HCCI engines such as those from Chang et al. [4] and Hensel et al. [5].

The work-hardening of three common automotive low carbon steel sheets is described in terms of two different types of microstructure based models. The first model employed is the classical Kocks-Mecking model. On the other hand, the second model incorporates the effect of the density evolution of non-redundant dislocations, also known as Geometrically Necessary Dislocations (GND), into the Kocks-Mecking hardening law. Each of both models was coupled to a Taylor-type crystal plasticity framework in order to follow the dislocation density evolution over all slip systems. One of the microstructural parameters employed on the calibration of the second model is the density of GND, parameter which was experimentally acquired from crystal orientation maps by EBSD (Electron Backscatter Diffraction) on the tensile samples.

In this paper, a two-dimensional microstructure-based finite element modeling method is adopted to investigate the effects of material parameters of the constituent phases on the macroscopic tensile behavior of Q&P steel and to perform a computational material design approach for performance improvement. For this purpose, a model Q&P steel is first produced and various experiments are then performed to characterize the model steel. Actual microstructure-based model is generated based on the information from EBSD, SEM and nano-indentation test, and the material properties for the constituent phases in the model are determined based on the initial constituent properties from HEXRD test and the subsequent calibration of model predictions to tensile test results. The influence of various material parameters of the constituents on the macroscopic behavior is then investigated.

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.