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In the present work, an experimental and numerical analysis of high pressure Diesel spray evolution is carried out in terms of spray momentum flux time history and instantaneous injection rate. The final goal of spray momentum and of injection rate analyses is the evaluation of the nozzle outlet flow characteristics and of the nozzle internal geometry possible influences on cavitation phenomena, which are of primary importance for the spray evolution. Further, the evaluation of the flow characteristics at the nozzle exit is fundamental in order to obtain reliable boundary conditions for injection process 3D simulation. In this paper, spray momentum data obtained in ambient temperature, high counter-pressure conditions at the Perugia University Spray Laboratory are presented and compared with the results of 3D simulations of the momentum rig itself.

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.

The present work is the continuation of the research activity developed by the same authors in last years about the use of recent technologies (Artificial Neural Networks) for the set up of “software redundancy” modules to be implemented On Board for the use in Diagnostic Systems. In the present work, a system based on Artificial Neural Networks models for automotive engines Fault Diagnosis and Isolation purposes is set-up and analysed. Four sensors/actuators (throttle valve, rotational speed, torque and intake manifold pressure) are considered, and the respective acquired data are used to train and test four ANN modules correlating the different quantities. An FDI scheme is presented which generates fault codes sequences by suitably treating the primary residuals, obtained by comparing experimental data with the calculated ones by the ANN modules. The robust fault isolation capabilities of the proposed FDI system are presented and discussed.

Radio Frequency Corona ignition systems represent an interesting solution among innovative ignition strategies for their ability to stabilize the combustion and to extend the engine operating range. The corona discharge, generated by a strong electric field at a frequency of about 1 MHz, produces the ignition of the air-fuel mixture in multiple spots, characterized by a large volume when compared to a conventional spark, increasing the early flame growth speed. The transient plasma generated by the discharge, by means of thermal, kinetic and transport effects, allows a robust initialization of the combustion even in critical conditions, such as using diluted or lean mixtures. In this work the effects of Corona ignition have been analyzed on a single cylinder optical engine fueled with gasoline, comparing the results with those of a traditional single spark ignition.

In the last decades, worldwide automotive regulations induced the industry to dramatically increase the application of electronics in the control of the engine and of the pollutant emissions reduction systems. Besides the need of engine control, suitable fault diagnosis tools had also to be developed, in order to fulfil OBD-II and E-OBD requirements. At present, one of the problems in the development of Diesel engines is represented by the achievement of an ever more sharp control on the systems used for the pollutant emission reduction. In particular, as far as NOx gas is concerned, EGR systems are mature and widely used, but an ever higher efficiency in terms of emissions abatement, requires to determine as better as possible the actual oxygen content in the charge at the engine intake manifold, also in dynamic conditions, i.e. in transient engine operation.

The present work concerns the analysis of a concept for a new variable valve actuation system for internal combustion engines, denoted HVC (Hydraulic Valve Control system). The system is an electro-hydraulic device which aims at minimizing the power consumption required for the valve actuation. Unlike lost motion devices, where the excess pumped oil is wasted in order to control the lift profile, the HVC system uses a reduced quantity of energy to ensure the actual lift profile. For that reason interesting potentialities to increase the global fuel conversion efficiency of the engine are expected, in addition to the benefits deriving from the control flexibility. The HVC system has been modeled by means of an hydraulic simulation tool, useful for the dynamic analysis of mechanical and hydraulic systems. In this work the main elements of the device will be described and their relevant modeling parameters will be discussed.

Lean operation of spark ignition engines is a promising strategy for increasing thermal efficiency and minimize emissions. Variability on the other hand is one of the main shortcomings in these conditions. In this context, the present study looks at the interaction between the spark produced by a J-type plug and the surrounding fluid flow. A combined experimental and numerical approach was implemented so as to provide insight into the phenomena related to the ignition process. A sweep of cross-flow velocity of air was performed on a dedicated test rig that allowed accurate control of the volumetric flow and pressure. This last parameter was varied from ambient to 10 bar, so as to investigate conditions closer to real-world engine applications. Optical diagnostics were applied for better characterization of the arc in different operating conditions. The spatial and temporal evolution of the arc was visualized with high-speed camera to estimate the length, width and stretching.

In this paper, a further step along a research line concerning the set up of a Fault Diagnosis system for OBD-II purpose is presented. The suitability of Artificial Neural Networks for the use as engine simulation modules in the framework of a software redundancy approach has been analyzed. Experimental tests were performed, by acquiring four main engine operational parameters. Using this knowledge base, the performance of a wide variety of different Net Types was analyzed and discussed. Peculiar aspects of the possible industrial applications of this methodology are also deeply examined.

It has been proved that Radio Frequency Corona, among other innovative ignition systems, is able to stabilize combustion and to extend the engine operating range in lean conditions, with respect to conventional spark igniters. This paper reports on a sensitivity analysis on the combustion behavior for different values of Corona electric control parameters (supply voltage and discharge duration). Combustion analysis has been carried out on a single cylinder PFI gasoline-fueled optical engine, by means of both indicating measurements and imaging. A high-speed camera has been used to record the natural luminosity of premixed flames and the obtained images have been synchronized with corresponding indicating acquisition data. Imaging tools allowed to observe and measure the early flame development, providing information which are not obtainable by a pressure-based indicating system.

In this paper the evaluation of the suitability of the Artificial Neural Networks for setting up simulation modules for “analytical redundancy” was further carried out. The performance of the ANN modules was enhanced, by taking into account the engine dynamics for the simulation of fast engine transients and obtaining satisfactory results. Working toward actual on board application in Fault Diagnosis systems, some ANN modules were implemented in an on-line system which acquires signals from an engine mounted on a test bench and compares in real time the experimental values with the estimated ones. In this way, it was possible to perform long duration tests of ANN's behaviour, substantially confirming the results of the conventional off-line analysis.