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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.

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 new real driving emission cycles and the growing adoption of turbocharged GDI engines are directing the automotive technology towards the use of innovative solutions aimed at reducing environmental impact and increasing engine efficiency. Water injection is a solution that has received particular attention in recent years, because it allows to achieve fuel savings while meeting the most stringent emissions regulations. Water is able to reduce the temperature of the gases inside the cylinder, coupled with the beneficial effect of preventing knock occurrences. Moreover, water dilutes combustion, and varies the specific heat ratio of the working fluid; this allows the use of higher compression ratios, with more advanced and optimal spark timing, as well as eliminating the need of fuel enrichment at high load. Computational fluid dynamics simulations are a powerful tool to provide more in-depth details on the thermo-fluid dynamics involved in engine operations with water injection.

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.