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The turbulent flow field inside the cylinder plays a major role in spark ignition (SI) engines. Multiple phenomena that occur during the high pressure part of the engine cycle, such as early flame kernel development, flame propagation and gas-to-wall heat transfer, are influenced by in-cylinder turbulence. Turbulence inside the cylinder is primarily generated via high shear flows that occur during the intake process, via high velocity injection sprays and by the destruction of macro-scale motions produced by tumbling and/or swirling structures close to top dead center (TDC) . Understanding such complex flow phenomena typically requires detailed 3D-CFD simulations. Such calculations are computationally very expensive and are typically carried out for a limited number of operating conditions. On the other hand, quasi-dimensional simulations, which provide a limited description of the in-cylinder processes, are computationally inexpensive.

The selection and tuning of the Fuel Injection System (FIS) are among the most critical tasks for the automotive diesel engine design engineers. In fact, the injection strongly affects the combustion phenomena through which controlling a wide range of related issues such as pollutant emissions, combustion noise and fuel efficiency becomes feasible. In the scope of the engine design optimization, the simulation is an efficient tool in order to both predict the key performance parameters of the FIS, and to reduce the amount of experiments needed to reach the final product configuration. In this work a complete characterization of a solenoid ballistic injector for a Light-Duty Common Rail system was therefore implemented in a commercially available one-dimensional computational software called GT-SUITE. The main phenomena governing the injector operation were simulated by means of three sub-models (electro-magnetic, hydraulic and mechanical).

The target for future cooling systems is to control the fluid temperatures and flows through a demand oriented control of the engine cooling to minimize energy demand and to achieve comfort, emissions, or service life advantages. The scope of this work is to create a complete engine thermal model (including both cooling and lubrication circuits) able to reproduce engine warm up along the New European Driving Cycle in order to assess the impact of different thermal management concepts on fuel consumption. The engine cylinder structure was modeled through a finite element representation of cylinder liner, piston and head in order to simulate the cylinder heat exchange to coolant or oil flow circuits and to predict heat distribution during transient conditions. Heat exchanges with other components (EGR cooler, turbo cooler, oil cooler) were also taken into account.

A 0D phenomenological turbulence model, based on the K-k and k- ɛ approaches, was coupled with a predictive turbulent combustion model using the commercial code GT-Suite, and its predictive capabilities were assessed for a downsized turbocharged SI engine. Differently from the 3D-CFD approach which is typically utilized to describe the evolution of the in-cylinder flow field, and which has very high computational requirements, the 0D phenomenological approach adopted in this work gives the opportunity to predict the evolution of the in-cylinder charge motion and the subsequent combustion process by means of a turbulent combustion model, with a significantly reduced computational effort, thus paving the way for the simulation of the whole engine operating map.

The predictive capabilities of an innovative multizone combustion model DIPulse, developed by Gamma Technologies, were assessed in this work for a last generation common rail automotive diesel engine. A detailed validation process, based on an extensive experimental data set, was carried out concerning the predicted heat release rate, the in-cylinder pressure trace, as well as NOx and soot emissions for several operating points including both part load and full load points. After a preliminary calibration of the model, the combustion model parameters were then optimized through a Latin Hypercube Design of Experiment (DoE), with the aim of minimizing the RMS error between the predicted and experimental burn rate of several engine operating points, thus achieving a satisfactory agreement between simulation and experimental engine combustion and emissions parameters.

In this paper a novel approach to mimic through numerical simulation Cycle-to-Cycle Variations (CCV) of the combustion process of Spark Ignition (SI) engines is described. The proposed methodology allows to reproduce the variability in combustion which is responsible for knock occurrence and thus to replicate the stochastic behavior of this abnormal combustion phenomenon. On the basis of the analysis of a comprehensive database of experimental data collected on a typical European downsized and turbocharged SI engine, the proposed approach was demonstrated to be capable to replicate in the simulation process the same percentage of knocking cycles experimentally measured in light-knock conditions, after a proper calibration of the Kinetics-Fit (KF), a new phenomenological knock model which was recently developed by Gamma Technologies.

The aim of this work is the assessment of the predictive capabilities of fast running models, obtained through an appropriate reduction and simplification process from detailed 1D fluid-dynamic models, for a turbocharged s.i. engine under highly transient operating conditions. Simulations results have been compared with experimental data for different types of models, ranging from fully detailed 1D fluid-dynamic models to map-based models, quantifying the degradation of the model accuracy and the reduction in the computational time for different kinds of driving cycles, from moderately transient such as the NEDC to highly dynamic such as the US06.

The effects of using blended renewable diesel fuel (30% vol.), obtained from Rapeseed Methyl Ester (RME) and Hydrotreated Vegetable Oil (HVO), in a Euro 5 small displacement passenger car diesel engine have been evaluated in this paper. The hydraulic behavior of the common rail injection system was verified in terms of injected volume and injection rate with both RME and HVO blends fuelling in comparison with commercial diesel. Further, the spray obtained with RME B30 was analyzed and compared with diesel in terms of global shape and penetration, to investigate the potential differences in the air-fuel mixing process. Then, the impact of a biofuel blend usage on engine performance at full load was first analyzed, adopting the same reference calibration for all the tested fuels.

The fuel injection plays a crucial role in determining the mixture formation process in Gasoline Direct Injection (GDI) engines. Pollutant emissions, and soot emissions in particular, as well as phenomena affecting engine reliability, such as oil dilution and injector coking, are deeply influenced by the injection system features, such as injector geometric characteristics (such as injector type, injector position and targeting within the combustion chamber) and operating characteristics (such as injection pressure, injection phasing, etc.). In this paper, a new CFD methodology is presented, allowing a preliminary assessment of the mixture formation quality in terms of expected soot emissions, oil dilution and injector coking risks for different injection systems (such as for instance multihole or swirl injectors) and different injection strategies, from the early stages of a new engine design.

Hybrid Electric Vehicles (HEVs) represent a powerful technology to save fuel and reduce CO₂ emissions, through the synergic use of a conventional internal combustion engine and one or more electric machines. However their performance strongly depends on the control strategy that shares the power demand among the engine and the electric motors at each time instant, with the objective of minimizing a pre-defined cost function over an entire driving cycle, and satisfying, at the same time, any additional constraints. The aim of this work is therefore the definition of a methodology to develop, through numerical simulation, a sub-optimal hybrid powertrain controller: starting from the problem definition, the ideal performance for a case study hybrid architecture was analyzed through a global optimization algorithm in order to point out information which can be used to define new control laws.

In Motorsports the understanding of the real engine performance within a complete circuit lap is a crucial topic. On the basis of the telemetry data the engineers are able to monitor this performance and try to adapt the engine to the vehicle's and race track's characteristics and driver's needs. However, quite often the telemetry is the sole analysis instrument for the Engine-Vehicle-Driver (EVD) system and it has no prediction capability. The engine optimization for best lap-time or best fuel economy is therefore a topic which is not trivial to solve, without the aid of suitable, reliable and predictive engineering tools. A complete EVD model was therefore built in a GT-SUITE™ environment for a Motorsport racing car (STCC-VW-Scirocco) equipped with a Compressed Natural Gas (CNG) turbocharged S.I. engine and calibrated on the basis of telemetry and test bench data.

The effects of using a B30 blend of ultra-low sulfur diesel and two different Fatty Acid Methyl Esters (FAME) obtained from both Rapeseed Methyl Ester (RME) and Jatropha Methyl Ester (JME) in a Euro 5 small displacement passenger car diesel engine on both full load performance and part load emissions have been evaluated in this paper. In particular the effects on engine torque were firstly analyzed, for both a standard ECU calibration (i.e., without any special tuning for the different fuel characteristics) and for a specifically adjusted ECU calibration obtained by properly increasing the injected fuel quantities to compensate for the lower LHV of the B30: with the latter, the same torque levels measured under diesel operation could be observed with the B30 blend too, with lower smoke levels, thus highlighting the potential for maintaining the same level of performance while achieving substantial emissions benefits.