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This paper describes a correlation study on fuel spray pattern recognition of multi-hole injectors for gasoline direct injection (GDi) engines. Spray pattern is characterized by patternation length, which represents the distance of maximum droplet concentration from the axis of the injector. Five fuel injectors with different numbers and sizes of nozzle holes were considered in this study. Experimental data and CFD modeling results were used separately to develop regression models for spray patternation. These regressions predicted the influence of a number of injector operating and design parameters, including injection system operating pressure, valve lift, injector hole length-to-diameter ratio (L/d) and the orientation of the injector hole. The regression correlations provided a good fit with both experimental and CFD spray simulation results. Thus CFD offers a good complement to experimental validation during development efforts to meet a desired injector spray pattern.

The CFD code KIVA has been applied to the simulation of the transient air-assisted fuel injection(AAFI) process, in which air and fuel at moderate pressures are mixed in an interior chamber of the injector before passing through a pintle valve into air at near ambient pressure in a cylinder. On passage through the pintle valve fuel is atomised. Because of the small dimensions of the flow passages within the injector, a very fine computational grid structure is used to accurately resolve the flow behaviour. Adopting an axisymmetric grid structure enables symmetry to be exploited. The computational results are validated with experimental data for fuel jet penetration and spread with time, obtained using Schlieren visualisation. The simulation of air blast atomisation in an engine cannot utilise the fine grid structure above because of the large computational resources required.