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Ammonia is one of the carbon-free alternatives considered for power generation and transportation sectors. But ammonia’s lower flame speed, higher ignition energy, and higher nitrogen oxides emissions are challenges in practical applications such as internal combustion engines. As a result, modifications in engine design and control and the use of a secondary fuel to initiate combustion such as natural gas are considered for ammonia-fueled engines. The higher-octane number of methane (the main component in natural gas) and ammonia allows for higher compression ratios, which in turn would increase the engine's thermal efficiency. One simple approach to initiate and control combustion for a high-octane fuel at higher compression ratios is to use a spark plug. This study experimentally investigated the operation of a heavy-duty compression ignition engine converted to spark ignition and ammonia-methane blends.

The use of hydrogen in internal combustion engines is an effective approach to significantly support the reduction of CO2 emissions from the transportation sector using technically affordable solutions. The use of direct injection is the most promising approach to fully exploit hydrogen potential as a clean fuel, while preserving targets in terms of power density and emissions. In this frame, the development of an effective combustion system largely relies on the hydrogen-air mixture formation process, so to adequately control the charge stratification to mitigate pre-ignitions and knock and to minimize NOx formation. Hence, improving capabilities of designing a correct gas jet-air interaction is of paramount importance. In this paper the analysis of the evolution of a high-pressure gas jet produced by a single-hole prototype injector operated with different pressure ratios is presented.

Optical motion capture (OMC) is a relatively new experimental tool used in many branches of science and engineering. Despite OMC’s widespread use, literature and practical procedures on the quantification of error and uncertainty in OMC systems for rigid bodies are currently underdeveloped. However, in most studies involving error and uncertainty quantification, the OMC volumes are relatively small (maximum length of 2m in any dimension) and involve expensive experimental apparatuses. Therefore, a cost-effective procedure to quantify the positional errors and uncertainties present in a large volume OMC system is presented. The procedure utilizes the kinematics of a wooden block traveling through air to predict errors and uncertainties in the OMC system by only collecting trajectory data.

Conventional spark-ignition engines are currently incapable of meeting rising customer performance demands while complying with even stringent pollutant-emissions regulations. As a result, innovative ignition systems are being developed to accomplish these targets. Radio-Frequency corona igniters stand out for their ability to accelerate early flame growth speed by exploiting the combined action of kinetic, thermal and transport effects. Furthermore, a volumetric discharge enables the promotion of combustion over a wide area, as opposed to the local ignition of traditional spark. The present work wants to evaluate the advantages of a Streamer-type Radio Frequency corona discharge at about 100 kHz with respect to those of traditional spark igniter.

Emission legislation for light and heavy duty vehicles is requiring a drastic reduction of exhaust pollutants from internal combustion engines (ICE). Achieving a quick heating-up of the catalyst is of paramount importance to cut down cold start emissions and meet current and new regulation requirements. This paper describes the development and the basic characteristics of a novel burner for diesel engines exhaust systems designed for being activated immediately at engine cold start or during vehicle cruise. The burner is comprised of a swirled fuel dosing system, an air system, and an ignition device. The main design characteristics are presented, with a detailed description of the atomization, air-fuel interaction and mixture formation processes. An atomizer prototype has been extensively analyzed and tested in various conditions, to characterize the resulting fuel spray under cold-start and ambient operating conditions.

The goal of mitigating climate change has driven research to the use of carbon-free energy sources. In this regards, green hydrogen appears as one of the best options, however, its storage remains difficult and expensive. Indeed, there is room to consider the use of ammonia (an efficient hydrogen carrier) directly as a fuel for internal combustion engines or gas turbines. Currently, there are very few works in the literature describing liquid ammonia sprays, both from experimental and modeling point of view, and especially dealing with flash-boiling conditions. In this research work, the direct injection ammonia spray is modeled with the Lagrangian particle approach, building up a numerical model within the OpenFOAM framework, for transient analyses using the U-RANS approach.

Computational fluid dynamics is used with the aim to gain further insights of the hydrogen injection process in internal combustion engines. To this end, three-dimensional RANS simulations of hydrogen under-expanded jets under a variety of injection pressures and temperatures and chamber backpressure are performed. A numerical framework that accounts for real-fluid effects is used which includes accurate non-linear mixing rules for thermodynamic and transport properties with multiple species. Jet formation process, transition to turbulent regime, and mixing process are investigated which are key aspects for the design of efficient injection and combustion. Different simulations are discussed to investigate the structures in the near field, such as Mach disk, barrel, and reflected shocks. It is found that for direct injection applications, especially in high back-pressure cases, accounting for real fluid behavior of hydrogen-air mixtures is important for accurate predictions.

Engine research community is developing innovative strategies capable of reducing fuel consumption and pollutant emissions while ensuring, at the same time, satisfactory performances. Spark ignition engines operation with highly diluted or lean mixture is demonstrated to be beneficial for engine efficiency and emissions while arduous for combustion initiation and stability. Traditional igniters are unsuitable for such working conditions, therefore, advanced ignition systems have been developed to improve combustion robustness. To overcome the inherent efficiency limit of combustion engines, the usage of renewable fuels is largely studied and employed to offer a carbon neutral transition to a cleaner future. For such a reason, both innovative ignition systems and bio or E-fuels are currently being investigated as alternatives to the previous approaches. Within this context, the present work proposes a synergetic approach which combines the benefits of a biofuel blend, i.e.

Emission legislation for passenger cars is requiring a drastic reduction of exhaust pollutants from internal combustion engines (ICE). In this framework, achieving a quick heating-up of the catalyst is of paramount importance to cut down the cold start emissions and meet future regulation requirements. This paper describes the development and the basic characteristics of a novel burner for gasoline engines exhaust systems designed for being activated immediately at engine cold start. The burner is comprised of a fuel injector, an air system, and an ignition device. The design of the combustion chamber is first presented, with a description of the air-fuel interactions and mixture formation processes. Swirl is used along with a flame-holder concept to anchor the flame at the mixer exit. Spray-swirl and spray-walls interaction are also discussed. Computational Fluid Dynamics (CFD) analyses have been used to investigate these aspects.

Machine learning algorithms are effective tools to reduce the number of engine dynamometer tests during internal combustion engine development and/or optimization. This paper provides a case study of using such a statistical algorithm to characterize the heat transfer from the combustion chamber to the environment during combustion and during the entire engine cycle. The data for building the machine learning model came from a single cylinder compression ignition engine (13.3 compression ratio) that was converted to natural-gas port fuel injection spark-ignition operation. Engine dynamometer tests investigated several spark timings, equivalence ratios, and engine speeds, which were also used as model inputs. While building the model it was found that adding the intake pressure as another model input improved model efficiency.

Ducted Fuel Injection (DFI) is a new technology recently developed with the aim of reducing soot emission formation in diesel compression ignition engines. DFI concept consists of the injection of fuel spray through a small duct located downstream of the injector nozzle leaving a certain gap, the so-called Stand-off distance. Currently, CFD modelers have investigated its performance using classical spray modeling techniques such as the Discrete Drops Method (DDM). However, as discussed in the literature, this type of technique is inappropriate when applied to dense jets as those occurring in diesel sprays, especially in the near-nozzle region (where the duct is placed). Therefore, considering a more appropriate modeling technique for such a problem is mandatory. In this research work, an Eulerian single-fluid diffuse-interface model called Σ-Y and implemented in the OpenFOAM framework is utilized for the simulation of non-reacting conditions.

Close-coupled aftertreatment systems (ATS) for automotive Diesel engines composed of DOC and SCR offer a significant potential in terms of pollutant emission control capability even with the introduction of more aggressive driving cycles and rigorous limits for type-approval tests. This is particularly important for incoming certification standards where the forecast is showing a trade-off towards ultra-low NOx emissions values. As the SCR system NOx conversion capability largely relies on both the UWS mixing device and on NOx sensors used to detect the actual NH3 slip and residual NOx concentration, developing numerical simulation tools for the analysis of the actual flow pattern and species concentration over peculiar sections of the exhaust system is crucial to support the ATS development process.

The combustion process in spark-ignition engines can vary considerably cycle by cycle, which may result in unstable engine operation. The phenomena amplify in natural gas (NG) spark-ignition (SI) engines due to the lower NG laminar flame speed compared to gasoline, and more so under lean burn conditions. The main goal of this study was to investigate the main sources and the characteristics of the cycle-by-cycle variation in heavy-duty compression ignition (CI) engines converted to NG SI operation. The experiments were conducted in a single-cylinder optically-accessible CI engine with a flat bowl-in piston that was converted to NG SI. The engine was operated at medium load under lean operating conditions, using pure methane as a natural gas surrogate. The CI to SI conversion was made through the addition of a low-pressure NG injector in the intake manifold and of a NG spark plug in place of the diesel injector.

Currently, conventional spark-ignition engines are unfit to satisfy the growing customer requirements on efficiency while complying with the legislations on pollutant emissions. New ignition systems are being developed to extend the engine stable operating range towards increasing lean conditions. Among these, the Radio-Frequency corona igniters represent an interesting solution for the capability to promote the combustion in a much wider region than the one involved by the traditional spark channel. Moreover, the flame kernel development is enhanced by means of the production of non-thermal plasma, where low-temperature active radicals are ignition promoters. However, at low pressure and at high voltage the low temperature plasma benefits can be lost due to occurrences of spark-like events. Recently, RF barrier discharge igniters (BDI) have been investigated for the ability to prevent the arc formation thanks to a strong-breakdown resistance.

Nowadays internal combustion engines can operate under lean combustion conditions to maximize efficiency, as long as combustion stability is guaranteed. The robustness of combustion initiation is one of the main issues of actual spark-ignition engines, especially at high level of excess-air or dilution. The enhancement of the in-cylinder global motion and local turbulence is an effective way to increase the flame velocity. During the ignition process, the excessive charge motion can hinder the spark discharge and eventually cause a misfire. In this perspective, the interaction between the igniter and the flow field is a fundamental aspect which still needs to be explored in more detail to understand how the combustion originates and develops. In this work, a combined experimental and numerical study is carried out to investigate the flow field around the spark gap, and its effect on the spark discharge evolution.

The amount of the thermal energy released in a gas mixture is crucial to characterize the igniter capability to start a robust ignition in internal combustion engines, especially in challenging operating conditions as high EGR dilution or very lean mixture. Nevertheless, the thermal energy measurement can be performed only in controlled environments, such as constant volume vessels, while it is not feasible in metal engines. This work proposes to find a correlation between the released thermal energy and the luminosity generated by the same discharge event in an optical vessel. This correlation implies that energy information could be indirectly obtained through feasible optical measurements in optical engines, and even in metal engines via low-cost diagnostic tools such as borescopes. The experimental campaign is carried out with a non-equilibrium plasma igniter, a dielectric-barrier discharge igniter (BDI).

Machine learning models were shown to provide faster results but with similar accuracy to multidimensional computational fluid dynamics or in-depth experiments. This study used a support-vector machine (SVM), a set of related supervised learning methods, to predict the dynamic performance (i.e., engine power and torque) of a heavy-duty natural gas spark ignition engine. The single-cylinder four-stroke test engine was fueled with methane. The engine was operated at different spark timings, mixture equivalence ratios, and engine speeds to provide the data for training and testing the proposed SVM. The results indicated that the performance and accuracy of the built regression model were satisfactory, with correlation coefficient quantities all larger than 0.95 and root-mean-square errors close to zero for both training and validation datasets.

The measurement of solid particles down to 10 nm is being incorporated into global technical regulations (GTR). This study explores the measurement of solid particles below 23 nm by using both current and proposed particle number (PN) systems having different volatile particle remover (VPR) methodologies and condensation particle counter (CPC) cutoff diameters. The measurements were conducted in dynamometer test cells using ten diesel and eight natural gas (NG) engines that were going under development for a variety of global emission standards. The PN systems measured solid PN from more than 700 test cycles. The results from the preliminary campaign showed a 10-280% increase in PN emissions with the inclusion of particles below 23 nm.

Euro VI regulations in Europe and its adaptors recently extended the regulation to include Particle Number (PN) for in-use conformity testing. However, the in-use PN Portable Emissions Measurement System (PEMS) is still evolving and has higher measurement uncertainty when compared against laboratory-grade PN systems. The PN systems for laboratory require a condensation particle counter (CPC). Thus, in this study, a CPC-based Horiba PN-PEMS was selected for performance evaluation against the laboratory-grade PN systems. This study was divided into four phases. The first two phases’ measurements were conducted from the Constant Volume Sampler (CVS) tunnel where the brake-specific particle number (BSPN) levels of 1010-12 and 1013 (#/bhp-h) were measured from the engines equipped with diesel particulate filter (DPF) and without DPF, respectively. In comparison against PN systems, PN-PEMS, on average, reported 14% lower BSPN from 82 various tests for the BSPN levels of 1010-11.

Artificial Intelligence is becoming very important and useful in several scientific fields. Machine learning methods, such as neural networks and decision trees, are often proposed in applications for internal combustion engines as virtual sensors, faults diagnosis systems and engine performance optimization. The high pressure of the intake air coupled with the demand of lean conditions, in order to reduce emissions, have often close relationship with the knock events. Fuels autoignition characteristics and flame front speed have a significant impact on knock phenomenon, producing high internal cylinder pressures and engine faults. The limitations in using pressure sensors in the racing field and the challenge to reduce the costs of commercial cars, push the replacement of a hardware redundancy with a software redundancy.