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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.

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.

A new camless system (referred to as Hydraulic Valve Control - HVC - system) is in an advanced state of prototyping and development. The present paper aims to support the new incoming activities concerning the possible modifications to the geometrical and mechanical characteristics of the system. The optimization of the new HVC system prototype is done using a multi-objective tool that integrates the hydraulic/mechanical simulator reproducing the physical model, with an optimization software. The latter tool can be used choosing a specific approach among different probabilistic mathematical models; the Genetic Algorithm approach was chosen to achieve the goal of the present study. The paper describes design optimization of the pilot stage of the actuator for given characteristics of the power stage and of the poppet valve.

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.

This work aims at analyzing the fluid dynamic characteristics of a Ducati 4 valves SI engine, for racing motorcycle, during the intake and compression strokes, focusing on the correlation between steady state flow test data (experiments and simulations) and transient CFD simulation results, including the effect of variable valve actuation strategies with independent intake valve actuation. Several steady state flow test data were available in terms of maps of the discharge, tumble and swirl coefficients, at any combination of asymmetric lifts of the two intake valves. From these steady state data it can be argued that asymmetric strategies could enhance engine full load and part load operation characteristics, by exploiting favourable trade off occurring between the opposing needs for high mass flow rate and high charge motion intensity.

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.

The recent introduction of electronic controlled, high pressure injection systems has deeply changed the scenario for light duty, automotive diesel engines. This change is mainly due to the enhanced flexibility in obtaining the desired injection law (time history and injected fuel quantity), while high injection pressures also favour a suitable mixture formation. This results in higher engine performance (efficiency and power) and in better pollutant emissions control. At the same time, in order to reduce the greenhouse gases net production, research is analyzing alternative resources, such as bio-derived fuels. In particular, methyl esters derived by different vegetable oils are characterized by high cetane numbers and very small sulfur content. The present work reports the results of a comparative analysis performed on a modern DI, common-rail, turbocharged engine by using three different bio-derived fuels (rape seed, soybean, waste cooked oil) and conventional fossil diesel fuel.

A two-step simulation methodology was applied for the computation of the injector nozzle internal flow and the spray evolution in diesel engine-like conditions. In the first step, the multiphase cavitating flow inside injector nozzle is calculated by means of unsteady CFD simulation on moving grids from needle opening to closure. A non-homogeneous Eulerian multi-fluid approach - with three phases i.e. liquid, vapor and air - has been applied. Afterward, in the second step, transient data of spatial distributions of velocity, turbulent kinetic energy, dissipation rate, void fraction and many other relevant properties at the nozzle exit were extracted and used for the subsequent Lagrangian spray calculation. A primary break-up model, which makes use of the transferred data, is used to initialize droplet properties within the hole area.

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.

In recent years there has been an increasing worldwide effort to limit polluting emissions from road vehicles. The On Board II Diagnostic (OBD II) regulations adopted by California Air Resources Board (CARB) are among the most restrictive rules. They require on-board devices which monitor emission control systems in order to identify deterioration or malfunction of components. For automotive purpose, the high cost of achieving hardware redundancy can be reduced by substituting software redundancy. This approach requires an engine model definition. In this work the application of the Artificial Neural Networks (ANNs) technology, is analyzed and validated by experiments. First model has been tested under varying load conditions with very encouraging results.

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.

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.