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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.

This study aims at building an efficient and robust radial basis function (RBF) artificial neural network (ANN), to reconstruct the in-cylinder pressure of a diesel engine starting from the signal of a low-cost accelerometer placed on the engine block. The accelerometer is a perfect non-intrusive replacement for expensive probes and is prospectively suitable for production vehicles. The RBF network is trained using measurements from different engine operating conditions. Training data are composed of time series from the accelerometer and corresponding measured in-cylinder pressure signals. The RBF network is then validated using data not included in training and the results show good correspondence between measured and reconstructed pressure signal. Various network parameters are used to optimize the network quality.

In order to meet the requirements in the stringent emission regulations, more and more research work has been focused on homogeneous charge compression ignition (HCCI) and partially premixed combustion (PPC) or partially premixed compression ignition (PCCI) as they have the potential to produce low NOx and soot emissions without adverse effects on engine efficiency. The mixture formation and charge stratification influence the combustion behavior and emissions for PPC/PCCI, significantly. An ultra-high speed burst-mode laser is used to capture the mixture formation process from the start of injection until several CADs after the start of combustion in a single cycle. To the authors’ best knowledge, this is the first time that such a high temporal resolution, i.e. 0.2 CAD, PLIF could be accomplished for imaging of the in-cylinder mixing process. The capability of resolving single cycles allows for the influence of cycle-to-cycle variations to be eliminated.