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Nowadays, green hydrogen can play a crucial role in a successful clean energy transition, thus reaching net zero emissions in the transport sector. Moreover, hydrogen exploitation in internal combustion engines is favoured by its suitable combustion properties and quasi-zero harmful emissions. High flame speeds enable a lean combustion approach, which provides high efficiency and reduces NOx emissions. However, high air flow rates are required to achieve the load levels typical of heavy-duty applications. In this framework, the present study aims to investigate the required boosting system of a 6-cylinder, 13-liter heavy-duty spark ignition engine through 1D numerical simulation. A comparison among various architectures of the turbocharging system and the size of each component is presented, thus highlighting limitations and potentialities of each architecture and providing important insights for the selection of the best turbocharging system.

The engine acoustic character has always represented the product DNA, owing to its strong correlation with in-cylinder pressure gradient, components design and perceived quality. Best practice for engine acoustic characterization requires the employment of a hemi-anechoic chamber, a significant number of sensors and special acoustic insulation for engine ancillaries and transmission. This process is highly demanding in terms of cost and time due to multiple engine working points to be tested and consequent data post-processing. Since Neural Networks potentially predicting capabilities are apparently un-exploited in this research field, the following paper provides a tool able to acoustically estimate engine performance, processing system inputs (e.g. Injected Fuel, Rail Pressure) thanks to the employment of Multi Layer Perceptron (MLP, a feed forward Network working in stationary points).

Compression ignition engine-based transportation is nowadays looking for cleaner combustion solutions. Among them, ducted fuel injection (DFI) is emerging as a cutting-edge technology due to its potential to drastically curtail engine-out soot emissions. Although the DFI capability to abate soot formation has been demonstrated both in constant-volume and optical engine conditions, its optimization and understanding is still needed for its exploitation on series production engines. For this purpose, computational fluid dynamics (CFD) coupled with low-cost turbulence models, like RANS, can be a powerful tool, especially in the industrial context. However, it is often challenging to obtain reliable RANS-based CFD simulations, especially due to the high dependence of the various state-of-the-art turbulence models on the case study.

Ammonia is a new type of carbon-free fuel with low cost, clean and safe. The research and application of zero-carbon fuel internal combustion engines has become the mainstream of future development. However, there still exist problems should be solved in the application of ammonia fuel. Due to the lower flame laminar speed and higher ignition temperature, ammonia may have unstable combustion phenomena. In this work, the characteristics of ammonia combustion have been investigated, based on controllable thermal activated atmosphere burner. The ignition delay has been used to analyze the ammonia combustion characteristics. With the increase in co-flow temperature, the ignition delay of ammonia/air has an obvious decline. In order to investigate the emission characteristics of ammonia, CHEMKIN is used to validate the different chemical reaction mechanisms and analyse the ammonia emissions.

Nowadays numerical simulations play a major role in the development of future sustainable powertrain thanks to their capability of investigating a wide spectrum of innovative technologies with times and costs significantly lower than a campaign of experimental tests. In such a framework, this paper aims to assess the predictive capabilities of an 1D-CFD engine model developed to support the design and the calibration of the innovative highly efficient spark ignition engine of the PHOENICE (PHev towards zerO EmissioNs & ultimate ICE efficiency) EU H2020 project. As a matter of fact, the availability of a reliable simulation platform is crucial to achieve the project target of 47% peak indicating efficiency, by synergistically exploiting the combination of innovative in-cylinder charge motion, Miller cycle with high compression ratio, lean mixture with cooled Exhaust Gas Recirculation (EGR) and electrified turbocharger.

The role of numerical simulations in the development of innovative and sustainable powertrains is constantly growing thanks to their capabilities to significantly reduce the calibration efforts and to point out potential synergies among different technologies. In such a framework, this paper describes the development of a fully physical 1D-CFD engine model to support the calibration of the highly efficient spark ignition engine of the PHOENICE (PHev towards zerO EmissioNs & ultimate ICE efficiency) EU H2020 project. The availability of a reliable simulation platform is essential to effectively exploit the combination of the several features introduced to achieve the project target of 47% peak gross indicated efficiency, such as SwumbleTM in-cylinder charge motion, Miller cycle combined with high Compression Ratio (CR), lean mixture exploiting cooled low pressure Exhaust Gas Recirculation (EGR) and electrified turbocharging.

The continuous tightening of CO2 emission targets along with the introduction of Real Driving Emissions (RDE) tests make Water Injection (WI) one of the most promising solutions to improve efficiency, enhance performance and reduce emissions of turbocharged high-performance Spark Ignition engines. This technology, by reducing local in-cylinder mixture temperature, enables higher compression ratios, optimal spark timing and stoichiometric combustion over the entire engine operating range. This research activity, therefore, aims to assess the benefits in terms of CO2 emission reduction of a Port Water Injection (PWI) system integrated in a Downsized Turbocharged Direct Injection Spark Ignition (T-DISI) Engine. In this regard, a 1D-CFD model of the engine capable to predict the impact of the water content on both the combustion process and the knock likelihood was firstly developed.

Despite the legislation targets set by several governments of a full electrification of new light-duty vehicle fleets by 2035, the development of innovative, environmental-friendly Internal Combustion Engines (ICEs) is still crucial to be on track toward the complete decarbonization of on road-mobility of the future. In such a framework, the PHOENICE (PHev towards zerO EmissioNs & ultimate ICE efficiency) project aims at developing a C SUV-class plug-in hybrid (P0/P4) vehicle demonstrator capable to achieve a -10% fuel consumption reduction with respect to current EU6 vehicle while complying with upcoming EU7 pollutant emissions limits. Such ambitious targets will require the optimization of the whole engine system, exploiting the possible synergies among the combustion, the aftertreatment and the exhaust waste heat recovery systems.

Magneto-Rheological (MR) Fluid started to be used for industrial applications in the last 20 years, and, from that moment on, innovative uses have been evaluated for different applications to exploit its characteristic of changing yield stress as a function of the magnetic field applied. Because of the complexity of the behavior of the MR fluid, it is necessary to perform lots of simulations, combining multi-physical software capable of evaluating all the material’s characteristics. The paper proposes a strategy capable of quickly verifying the feasibility of an innovative MR system, considering a sufficient accuracy of the approximation, able to easily verify the principal criticalities of the innovative applications concerning the MR fluid main electromagnetic and fluid-dynamic capabilities.

Fuel injection systems and performance is fundamental to combustion engine performance in terms of power, noise, efficiency, and exhaust emissions. There is a move toward electric vehicles (EVs) to reduce carbon emissions, but this is unlikely to be a rapid transition, in part due to EV batteries: their size, cost, longevity, and charging capabilities as well as the scarcity of materials to produce them. Until these issues are resolved, refining the spark-ignited engine is necessary to address both sustainability and demand for affordable and reliable mobility. Even under policies oriented to smart sustainable mobility, spark-ignited engines remain strategic, because they can be applied to hybridized EVs or can be fueled with gasoline blended with bioethanol or bio-butanol to drastically reduce particulate matter emissions of direct injection engines in addition to lower CO2 emissions.

In the intermediate stage towards zero-emissions, use of methane-hydrogen blends in spark ignition (SI) engines could represent an attractive application. The present work investigated the relevance of empirical base rules for choosing maximum brake torque spark timing settings when using methane-hydrogen blends. A 0D/1D model was used for investigating the optimized ignition for maximizing engine output. Calibration was performed by using in-cylinder pressure data recorded on a methane fueled small size SI engine for two-wheel applications. After adaptations of the model such as valves timing, for rendering it more representative for power generation applications, the investigation was focused on how MBT spark advance was correlated to the 50% mass fraction burned mark (CA50) and peak pressure location. The fact that they were optimized for methane was found to be essential only for high concentrations of hydrogen.

The use of Ducted Fuel Injection (DFI) for attenuating soot formation throughout mixing-controlled diesel combustion has been demonstrated impressively effective both experimentally and numerically. However, the last research studies have highlighted the need for tailored engine calibration and duct geometry optimization for the full exploitation of the technology potential. Nevertheless, the research gap on the response of DFI combustion to the main engine operating parameters has still to be fully covered. Previous research analysis has been focused on numerical soot-targeted duct geometry optimization in constant-volume vessel conditions. Starting from the optimized duct design, the herein study aims to analyze the influence of several engine operating parameters (i.e. rail pressure, air density, oxygen concentration) on DFI combustion, having free spray results as a reference.

In the framework of an increasing demand for a more sustainable mobility, where the fuel consumption reduction is a key driver for the development of innovative internal combustion engines, Turbulent Jet Ignition (TJI) represents one of the most promising solutions to improve the thermal efficiency. However, details concerning turbulent jet assisted combustion are still to be fully captured, and therefore the design and the calibration of efficient TJI systems require the support of reliable simulation tools that can provide additional information not accessible through experiments. To this aim, an experimental investigation combined with a 3D-CFD study was performed to analyze the TJI combustion characteristics in a single-cylinder spark-ignition (SI) engine. Firstly, the model was validated against experiments considering stoichiometric mixture at 3000 rpm, wide open throttle operating conditions.

Nowadays control system development in the automotive industry is evolving rapidly due to several factors. On the one hand legislation tightening is asking for simultaneous emission reduction and efficiency increase, on the other hand the complexity of the powertrain is increasing due to the spreading of electrification. Those factors are pushing for strong design parallelization and frontloading, thus requiring engine calibration to be moved much earlier in the V-Cycle. In this context, this paper shows how, coupling well known physical 1D engine models featuring predictive combustion and emission models with a fully physical aftertreatment system model and longitudinal vehicle model, a powerful virtual test rig can be built. This virtual test rig can be used for powertrain virtual calibration activities with reduced requirement in terms of experimental data.

Diesel engines with their embedded control systems are becoming increasingly complex as the emission regulations tighten, especially concerning NOx pollutants. The combustion and emission formation processes are closely correlated to the intake manifold O2 concentration. Consequently, the performance of the engine controllers can be improved if a model-based or sensor-based estimation of the O2 concentration is available. The paper addresses the modeling of the O2 concentration in a turbocharged diesel engine. Dynamic models, compared to generally employed steady state maps, capture the dynamic effects occurring over transients, when the major deviations from the stationary maps are found. Dynamic models positively affect the control system making it more effective and, exploiting information coming from sensors, they provide a more robust prediction performance. Firstly, a Nonlinear Output Error model (NOE), with simulation focus, fed with four inputs is presented.

As an era of autonomous driving approaches, it is necessary to translate handling comfort - currently a responsibility of human drivers - to a vehicle imbedded algorithm. Therefore, it is imperative to understand the relationship between perceived driving comfort and human driving behaviour. This paper develops a methodology able to generate the information necessary to study how this relationship is expressed in highway overtakes. To achieve this goal, the approach revolved around the implementation of sensor Data Fusion, by processing data from CAN, camera and LIDAR from experimental tests. A myriad of variables was available, requiring individuating the key-information and parameters for recognition, classification and understanding of the manoeuvres. The paper presents the methodology and the role each sensor plays, by expanding on three main steps: Data segregation and parameter selection; Manoeuvre detection and processing; Manoeuvre classification and database generation.

Developing of innovative technologies and materials to meet the requirements of environmental legislation on vehicle emissions has paramount importance for researchers and industries. Therefore, improvement of engine efficiency and fuel saving of modern internal combustion engines (ICEs) is one of the key factors, together with the weight reduction. Thermoplastic composite materials might be one of the alternative materials to be employed to produce engine components to achieve these goals as their properties can be engineered to meet application requirements. Unidirectional carbon fiber reinforced PolyEtherImide (CF/PEI) thermoplastic composite is used to design engine connecting rod and wrist pin, applying commercial engine data and geometries. The current study is focused on some elements of the crank mechanism as the weight reduction of these elements affects not only the curb weight of the engine but the overall structure.

Until 2017 in Europe the Type Approval (TA) procedure for light duty vehicles for the determination of pollutant emissions and fuel consumption was based on the New European Driving Cycle (NEDC), a test cycle performed on a chassis dynamometer. However several studies highlighted significant discrepancies in terms of CO2 emissions between the TA test and the real world, due to the limited representativeness of the test procedure. Therefore, the European authorities decided to introduce a new, up-to date, test procedure capable to closer represent real world driving conditions, called Worldwide Harmonized Light Vehicles Test Procedure (WLTP). This work aims to analyze the effects of the new WLTP on vehicle CO2 emissions through both experimental and simulation investigations on two different Euro 5 vehicles, a petrol and a diesel car, representatives of average European passenger cars.

In this paper, a numerical and experimental assessment of post injection potential for soot emissions mitigation in an off-road diesel engine is presented, with the aim of supporting hardware selection and engine calibration processes. As a case study, a prototype off-road 3.4 liters 4-cylinder diesel engine developed by Kohler Engines was selected. In order to explore the possibility to comply with Stage V emission standards without a dedicated aftertreatment for NOx, the engine was equipped with a low pressure cooled Exhaust Gas Recirculation (EGR), allowing high EGR rates (above 30%) even at high load. To enable the exploitation of such high EGR rates with acceptable soot penalties, a two-stage turbocharger and an extremely high-pressure fuel injection system (up to 3000 bar) were adopted. Moreover, post injections events were also exploited to further mitigate soot emissions with acceptable Brake Specific Fuel Consumption (BSFC) penalties.

The next generation of gasoline turbo-charged engines will have to deal with the continuous tightening of emissions regulations. In fact, to better represent real-world emission figures, WLTP and RDE cycles focus on stricter criteria; spanning higher speeds and loads potentially covering the whole engine operating map. It is common practice at present to use overfueling to avoid catastrophic failure of turbine and aftertreatment systems at very high engine speeds and loads due to excessive temperatures. A past technology, which is presently enjoying a resurgence of interest, is water injection. In particular, for high-specific-power applications, this could be used as replacement strategy for overfueling, potentially enabling full operating range stoichiometric operation with no compromise in terms of maximum performance with respect to today.