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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.

The influence of early induction stroke direct injection on late-cycle flows was investigated for a lean-burn, high-tumble, gasoline engine. The engine features side-mounted injection and was operated at a moderate load (8.5 bar brake mean effective pressure) and engine speed (2000 revolutions per minute) condition representative of a significant portion of the duty cycle for a hybridized powertrain system. Thermodynamic engine tests were used to evaluate cam phasing, injection schedule, and ignition timing such that an optimal balance of acceptable fuel economy, combustion stability, and engine-out nitrogen oxide (NOx) emissions was achieved. A single cylinder of the 4-cylinder thermodynamic engine was outfitted with an endoscope that enabled direct imaging of the spark discharge and early flame development.

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.

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.

With the global adoption of diesel common rail systems and the wide variation in composition of local commercial fuels, modern fuel injection systems must be robust against diverse fuel properties. To bridge the knowledge gap on the effects of compositional variation for real commercial fuels on spray combustion characteristics, the present work quantifies ignition and soot formation/oxidation in three unique, international diesel blends. Schlieren imaging, excited-state hydroxyl radical (OH*) chemiluminescence imaging and diffused back-illumination extinction imaging were employed to quantify vapor penetration, ignition, and soot formation and oxidation for high-pressure sprays in a constant-volume, pre-burn chamber. The three fuels were procured from Finland, Japan and Brazil and have cetane numbers of 64.1, 56.1 and 45.4, respectively.

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).

Within the Engine Combustion Network (ECN) spray combustion research frame, simultaneous line-of-sight laser extinction measurements and laser-induced incandescence (LII) imaging were performed to derive the soot volume fraction (fv). Experiments are conducted at engine-relevant high-temperature and high-pressure conditions in a constant-volume pre-combustion type vessel. The target condition, called "Spray A," uses well-defined ambient (900 K, 60 bar, 22.8 kg/m₃, 15% oxygen) and injector conditions (common rail, 1500 bar, KS1.5/86 nozzle, 0.090 mm orifice diameter, n-dodecane, 363 K). Extinction measurements are used to calibrate LII images for quantitative soot distribution measurements at cross sections intersecting the spray axis. LII images are taken after the start of injection where quasi-stationary combustion is already established.