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This work investigates the impact of injector temperature on the characteristics of high-pressure n-dodecane sprays under conditions relevant to heavy-duty diesel engines. Sprays are injected from a pair of single-hole diesel injectors belonging to the family of “Spray C” and “Spray D” Engine Combustion Network (ECN) injectors. Low and high injector temperature conditions are achieved by activating or deactivating a cooling jacket. We quantify spray spreading angle and penetration using high-speed shadowgraphy and long-distance microscopy imaging. We evaluate differences in fuel/air mixture formation at key timings through one-dimensional modeling. Injections from a cooled injector penetrate faster than those from a higher temperature injector, especially for an injector already prone to cavitation (Spray C).

For a better understanding of soot formation and oxidation processes in conventional diesel and biodiesel spray flames, the morphology, microstructure and sizes of soot particles directly sampled in spray flames fuelled with US#2 diesel and soy-methyl ester were investigated using transmission electron microscopy (TEM). The soot samples were taken at 50mm from the injector nozzle, which corresponds to the peak soot location in the spray flames. The spray flames were generated in a constant-volume combustion chamber under a diesel-like high pressure and high temperature condition (6.7MPa, 1000K). Direct sampling permits a more direct assessment of soot as it is formed and oxidized in the flame, as opposed to exhaust PM measurements. Density of sampled soot particles, diameter of primary particles, size (gyration radius) and compactness (fractal dimension) of soot aggregates were analyzed and compared. No analysis of the soot micro-structure was made.

Reduced-order models typically assume that the flow through the injector orifice is quasi-steady. The current study investigates to what extent this assumption is true and what factors may induce large-scale variations. Experimental data were collected from a single-hole metal injector with a smoothly converging hole and from a transparent facsimile. Gas, likely indicating cavitation, was observed in the nozzles. Surface roughness was a potential cause for the cavitation. Computations were employed using two engineering-level Computational Fluid Dynamics (CFD) codes that considered the possibility of cavitation. Neither computational model included these small surface features, and so did not predict internal cavitation. At steady state, it was found that initial conditions were of little consequence, even if they included bubbles within the sac. They however did modify the initial rate of injection by a few microseconds.

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.

Optimizing fuel injection spray is essential to comply with stringent future emission regulations for hybrid vehicles and internal combustion engine vehicles, and the spray characteristics and geometry must be robust for various engine operating conditions. This study presents experimental and numerical assessments of spray for lateral-mounted gasoline direct injection (GDI) sprays with different plume arrangements to analyze collapse characteristics, which can significantly deteriorate the geometry and characteristics of fuel sprays. Novel spray characterization methods are applied to analyze complex spray collapse behaviors using LED-based diffusive back-illuminated extinction imaging (DBIEI) and 3D computed tomographic (CT) image reconstruction. High-fidelity computational fluid dynamics (CFD) simulations are performed to analyze the detailed spray characteristics besides experimental characterization.