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Particulate Matter (PM) filters are becoming a standard component of Diesel engines exhaust aftertreatment devices to comply with the forthcoming engine emission regulations. However, cost reduction and durability are still critical issues in particular for the integration of the PM-filter with other components of the after-treatment system (e.g. pre-turbo-catalyst, close-coupled-catalyst, PM-filter, SCR). To respect functional (available temperature and gas composition) and space restraints, very complex shapes may result from the design causing tortuous flow patterns and influencing the flow distribution into the PM-filter. Uneven soot distributions in the filter may cause a non-homogeneous development of filter regeneration, leading to failures, for example due to the occurrence of large temperature gradients during the oxidation of soot deposits.

During gasoline direct injection (GDI) in spark ignition engines, droplets may hit piston or liner surfaces and be rebounded or deposit in the liquid phase as wallfilm. This may determine slower secondary atomization and local enrichments of the mixture, hence be the reason of increased unburned hydrocarbons and particulate matter emissions at the exhaust. Complex phenomena indeed characterize the in-cylinder turbulent multi-phase system, where heat transfer involves the gaseous mixture (made of air and gasoline vapor), the liquid phase (droplets not yet evaporated and wallfilm) and the solid walls. A reliable 3D CFD modelling of the in-cylinder processes, therefore, necessarily requires also the correct simulation of the cooling effect due to the subtraction of the latent heat of vaporization of gasoline needed for secondary evaporation in the zone where droplets hit the wall. The related conductive heat transfer within the solid is to be taken into account.

Computational Fluid Dynamics (CFD) surely has played a fundamental role in the design of the bodies and shapes of both commercial and racing vehicles in the last decades. This circumstance was mainly due to the connected substantial improvement in the design timings and to the possibility of producing numerous flow field and surface data that are difficult to obtain from a physical experimental method. Such a local analysis leads to a further understanding of the interactions of components with the overall aerodynamics. The development of wing performances, with respect to racing vehicles, has to deal with very short times but also with a very detailed description of the physics occurring. Starting from these constraints, a coupling procedure was developed by the joining of a CFD code with a Finite Element Method (FEM) structural code to better evaluate the aerodynamic performance of the wing deformed under the fluid dynamic loads.

The analysis of a spray behavior is confined to study the fluid dynamic parameters such as axial and radial velocity of the droplets, size distribution of the droplets, and geometrical aspect as the penetration length. In this paper, the spray is considered like a dynamic system and consequently it can be described by a number of parameters that characterize its dynamic behavior. The parameter chosen to describe the dynamic behavior is the external cone angle. This parameter has been detected by using an experimental injection chamber, a multi-hole (8 holes) injector for GDI applications and recorded by a high-speed C-Mos camera. The images have been elaborated by a fuzzy logic and neural network algorithm and are processed by using a chaos deterministic theory. This procedure carries out a map distribution of the working point of the spray and determines the stable (signature of the spray) and instable behavior.

The international community is making significant efforts to face climate changes related to substantial greenhouse gas (GHG) emissions. Among all the sectors, transport is responsible for almost a quarter of global GHG emissions, 72% of which is imputable to road vehicles. It’s also expected that, without significant measures, these emissions may grow at a faster rate than other sectors. Furthermore, rising fuel costs and availability concerns have made the electrification of road transportation an attractive option to reduce oil dependency. However, this solution produces an electricity demand increase, causing significant overload conditions that could affect the reliability of the distribution sector.

The increasing diffusion of gasoline direct injection (GDI) engines requires a more detailed and reliable description of the phenomena occurring during the fuel injection process. As well known the thermal and fluid-dynamic conditions present in the combustion chamber greatly influence the air-fuel mixture process deriving from GDI injectors. GDI fuel sprays typically evolve in wide range of ambient pressure and temperatures depending on the engine load. In some particular injection conditions, when in-cylinder pressure is relatively low, flash evaporation might occur significantly affecting the fuel-air mixing process. In some other particular injection conditions spray impingement on the piston wall might occur, causing high unburned hydrocarbons and soot emissions, so currently representing one of the main drawbacks of GDI engines.

The numerical reconstruction of the liquid jet generated by a multi-hole injector, operating in flash-boiling conditions, has been developed by means of a Eulerian- Lagrangian CFD code and validated thanks to experimental data collected with schlieren and Mie scattering imaging techniques. The model has been tested with different injection parameters in order to recreate various possible engine thermodynamic conditions. The work carried out is framed in the growing interest present around the gasoline direct-injection systems (GDI). Such technology has been recognized as an effective way to achieve better engine performance and reduced pollutant emissions. High-pressure injectors operating in flashing conditions are demonstrating many advantages in the applications for GDI engines providing a better fuel atomization, a better mixing with the air, a consequent more efficient combustion and, finally, reduced tailpipe emissions.

The paper reports an innovative method of analysis based on an advanced statistical techniques applied to images captured by a high-speed camera that allows highlighting phenomena and anomalies hardly detectable by conventional optical diagnostic techniques. The images, previously elaborated by neural network tools in order for clearly identifying the contours, have been analyzed in their time evolution as pseudo-chaotic variables that may have internal periodic components. In addition to the Fourier analysis, tools as Lyapunov and Hurst exponents and average Kω permitted to detect the chaos level of the signals. The use of this technique has permitted to distinguish periodic oscillations from chaotic variations and to detect those parameters that actually determine the spray behavior.

Gasoline Direct Injection (GDI) is a leading technology for Spark Ignition (SI) engines: control of the injection process is a key to design the engine properly. The aim of this paper is a numerical investigation of the gasoline injection and the resulting development of plumes from an 8-hole Spray G injector into a quiescent chamber. A LES approach has been used to represent with high accuracy the mixing process between the injected fuel and the surrounding mixture. A Lagrangian approach is employed to model the liquid spray. The fuel, considered as a surrogate of gasoline, is the iso-octane which is injected into the high-pressure vessel filled with nitrogen. The numerical results have been compared against experimental data realized in the optical chamber. To reveal the geometry of plumes two different imaging techniques have been used in a quasi-simultaneous mode: Mie-scattering for the liquid phase and schlieren for the gaseous one.

Direct injection of gaseous fuels usually involves the presence of under-expanded jets. Understanding the physics of such process is imperative for developing Direct Injection (DI) internal combustion engines fueled, for example, by methane or hydrogen. An experimental-numerical characterization of the under-expanded jets issued from an innovative multi-hole injector, designed for application in heavy-duty engines, is carried out. The experimental characterization of the jet evolution was recorded by means of schlieren imaging technique and, then, a numerical simulation procedure was validated, allowing a comprehensive injection process analysis. A high-order and density-based solver, capable of reproducing the most relevant features of the under-expanded jets, was developed within OpenFOAM framework. Initially the effects of the upstream-to-downstream pressure ratio, namely Net Pressure Ratios (NPR), on the spray morphology were investigated.

Ultra-high injection pressures, as well as flash-boiling occurrence, are among the most important research fields recently explored for improving Gasoline Direct Injection (GDI) engine performance. Both of them play a key role in the enhancement of the air/fuel mixing process, in the reduction of tailpipe pollutant emissions, as well as in the investigation of new combustion concepts. Injector manufacturers are even more producing devices with ultra-high injection pressures capable of working with flashing sprays. Flash-boiling of fuel sprays occurs when a super-heated fuel is discharged into an environment whose pressure is lower than the saturation pressure of the fuel and can dramatically alter spray formation due to complex two-phase flow effects and rapid droplet vaporization. In GDI engines, typically, it occurs during the injection process when high fuel temperatures make its saturation pressures higher than the in-cylinder one.

Diesel Particulate Filters (DPFs) are well assessed exhaust aftertreatment devices currently equipping almost every modern diesel engine to comply with the most stringent emission standards. However, an accurate estimation of soot content (loading) is critical to managing the regeneration of DPFs in order to attain optimal behavior of the whole engine-after-treatment assembly, and minimize fuel consumption. Real-time models can be used to address challenges posed by advanced control systems, such as the integration of the DPF with the engine or other critical aftertreatment components or to develop model-based OBD sensors. One of the major hurdles in such applications is the accurate estimation of engine Particulate Matter (PM) emissions as a function of time. Such data would be required as input data for any kind of accurate models. The most accurate way consists of employing soot sensors to gather the real transient soot emissions signal, which will serve as an input to the model.

Today, Direct-Injection systems are widely used on Spark-Ignition engines in combination with turbo-charging to reduce the fuel-consumption and the knock risks. In particular, the spread of Gasoline Direct Injection (GDI) systems is mainly related to the use of new generations of multi-hole, high-pressure injectors whose characteristics are quite different with respect to the hollow-cone, low-pressure injectors adopted in the last decade. This paper presents the results of an experimental campaign conducted on the spray produced by a GDI six-holes injector into a constant volume vessel with optical access. The vessel was filled with air at atmospheric pressure. Different operating conditions were considered for an injection pressure ranging from 3 to 20 MPa. For each operating condition, spray images were acquired by a CCD camera and then post processed to evaluate the spray penetration and cone angles.

A non-evaporating transient high pressure diesel spray operating under different ambient conditions was studied. Tip penetration and Sauter Mean Diameter (SMD) were measured using simultaneously the high speed photography and the laser light extinction techniques. Comparisons between experiments and computations were performed. The spray simulations were carried out by KIVA code version two with and without breakup submodel. The effect of the grid spacing on the numerical results was also evaluated. The KIVA simulations consistently underestimate the jet penetration. The computations of SMD are in disagreement with the experiments. These inaccuracies are probably caused by the inadeguate atomization model and, in part, by the limitation of the experimental techniques.

This paper reports an experimental and numerical investigation of the spray structure development for pure gasoline fuel and two different ethanol-gasoline blends (10% and 85% ethanol). A numerical methodology has been developed to improve the prediction of the pure and blends fuel spray. The fuel sprays have been simulated by means of a 3D-CFD code, adopting a multi-component approach for the fuel simulations. The vaporization behavior of the real fuel has been improved testing blends of 7 hydrocarbons and a reduced multi-component model has been defined in order to reduce the computational cost of the CFD simulations. Particular care has been also dedicated to the modeling of the atomization and secondary breakup processes occurring to the GDI sprays. The multi-hole jets have been simulated by means of a new atomization approach combined with the Kelvin-Helmholtz/Rayleigh-Taylor hybrid model.

Compressed Natural Gas (CNG) is regarded as a promising fuel for spark-ignited (SI) internal combustion engines (ICE) to improve engine thermal efficiency and reduce both carbon dioxide and pollutant emissions. Significant advantages of CNG are higher-octane number, higher hydrogen to carbon ratio, and lower energy-specific CO2 emissions compared with gasoline. More, it can be produced in renewable ways, and is more widespread and cheaper than conventional liquid fossil fuels. In this regard, the direct injection of CNG engines can be considered a promising technology for highly efficient and low-emission future engines. This work reports an experimental and numerical characterization of high-pressure methane jets from a multi-hole injector for direct injection engines.

A numerical methodology to simulate the high pressure spray evolution and the fuel-air mixing in diesel engines is presented. Attention is focused on the employed atomization model, a modified version of the Huh and Gosman, on the definition of a turbulence length scale limiter and of an adaptive local mesh refinement technique to minimize the result grid dependency. All the discussed models were implemented into Lib-ICE, which is a set of libraries and solvers, specifically tailored for engine simulations, which runs under the open-source CFD technology OpenFOAM®. To provide a comprehensive assessment of the proposed methodology, the validation procedure consisted into simulating, with a unique and coherent setup of all models, two different sets of experiments: a non-evaporating diesel fuel spray in a constant-volume vessel with optical access and an evaporating non-reacting diesel fuel spray in an optical engine.

During an injection process, a fluid undergoes a sudden pressure drop across the nozzle. If the pressure downstream the injector is below the saturation value of the fluid, superheated conditions are reached and thermodynamic instabilities realized. In internal combustion engines, flashing conditions greatly influence atomization and vaporization processes of a fuel as well as the mixture formation and combustion. This paper reports imaging behavior of a fuel under both flash boiling and non-flash boiling conditions. A GDI injector, eight-hole, 15.0 cc/s @ 10 MPa static flow, injected a single-component fluid (iso-octane), generating the spray. Experiments were carried out in an optically-accessible constant-volume quiescent vessel by Mie-scattering technique. A C-Mos high-speed camera was used to acquire cycle-resolved images of the spray evolving in the chamber filled with N2 which pressure ranged between 0.05 and 0.3 MPa.

Hydrogen technologies have been widely recognized as effective means to reduce Greenhouse Gases emissions, a crucial issue to target a Carbon-free world aimed by the European Green Deal. Within the road transport sector, electric vehicles with a hybrid powertrain, including battery packs and hydrogen Fuel Cells (FCs), are gaining importance owing to their adaptability to a wide variety of applications, high driving mileages and short refueling times. The control strategy is crucial to achieve a proper management of the energy flows, to maximize energy efficiency and maximize components durability and state of health. This work is focused on the design of an integrated Energy Management Strategy (EMS), whose aim is to minimize the hydrogen consumption, by operating the FC mainly in the high efficiency region while the battery pack works according to a charge sustaining mode. The proposed EMS is composed of a control algorithm and a supervisor.

Hydrogen technologies are among the main candidates to reduce carbon emissions in the automotive transport sector. Among the innovative solutions, Electric Vehicles (EVs) featuring hybrid powertrains, combining battery packs and hydrogen Fuel Cell (FC) stacks, are gaining prominence in our pursuit of sustainability objectives. Nonetheless, realizing the full potential of these hybrid vehicles hinges on the implementation of efficient Energy Management Strategies (EMS). In this study, we present an integrated EMS approach to achieve extended driving ranges and reduced energy consumption. This is achieved primarily by operating the FC within its high-efficiency range, while ensuring that the battery packs operate in a charge-sustaining mode. The EMS is crafted through an adaptive algorithm that takes into account various driving conditions to establish the most suitable sub-optimal control strategy.