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Computational models widely employed for predicting the dispersion of fuel sprays in combustion engines suffer from well-known drawbacks associated with the utilization of case-dependent empirical phase-change models, describing the conversion of liquid into vapour during fuel injection. The present work couples the compressible Navier-Stokes and energy conservation equations with a thermodynamic closure approximation covering pressures from 25 to 2000bar and temperatures that expand from compressed liquid, vapor-liquid equilibrium to trans/supercritical mixing, and thus, cover the whole range of P-T values that diesel fuel undergoes during its injection into combustion engines. The model assumes mechanical and thermal equilibrium between the liquid, vapour and surrounding air phases and thus, it avoids utilization of case-dependent empirical phase-change models for predicting in-nozzle cavitation and vaporization of fuels.

Rail pressures of modern diesel fuel injection systems have increased significantly over recent years, greatly improving atomisation of the main fuel injection event and air utilisation of the combustion process. Continued improvement in controlling the process of introducing fuel into the cylinder has led to focussing on fluid phenomena related to transient response. High-speed microscopy has been employed to visualise the detailed fluid dynamics around the near nozzle region of an automotive diesel fuel injector, during the opening, closing and post injection events. Complementary computational fluid dynamic (CFD) simulations have been undertaken to elucidate the interaction of the liquid and gas phases during these highly transient events, including an assessment of close-coupled injections.

The fuel spray process is of utmost importance to internal combustion engine design as it dominates engine performance and emissions characteristics. While designers rely on computational fluid dynamics (CFD) modeling for understanding of the air-fuel mixing process, there are recognized shortcomings in current CFD spray predictions, particularly under super-critical or flash-boiling conditions. In contrast, time-resolved optical spray experiments have now produced datasets for the three-dimensional liquid distribution for a wide range of operating conditions and fuels. By utilizing such a large amount of detailed experimental data, the machine learning (ML) techniques have opened new pathways for the prediction of fuel sprays under various engine-like conditions.