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The major challenge of the post-processing of soot aggregates in transmission electron microscope (TEM) images is the detection of soot primary particles that have no clear boundaries, vary in size within the fractal aggregates, and often overlap with each other. In this study, we propose an automated detection code for primary particles implementing the Canny Edge Detection (CED) and Circular Hough Transform (CHT) on pre-processed TEM images for particle edge enhancement using unsharp filtering as well as image inversion and self-subtraction. The particle detection code is tested for soot TEM images obtained at various ambient and injection conditions, and from five different combustion facilities including three constant-volume combustion chambers and two diesel engines.

The 4th Workshop of the Engine Combustion Network (ECN) was held September 5-6, 2015 in Kyoto, Japan. This manuscript presents a summary of the progress in experiments and modeling among ECN contributors leading to a better understanding of soot formation under the ECN “Spray A” configuration and some parametric variants. Relevant published and unpublished work from prior ECN workshops is reviewed. Experiments measuring soot particle size and morphology, soot volume fraction (fv), and transient soot mass have been conducted at various international institutions providing target data for improvements to computational models. Multiple modeling contributions using both the Reynolds Averaged Navier-Stokes (RANS) Equations approach and the Large-Eddy Simulation (LES) approach have been submitted. Among these, various chemical mechanisms, soot models, and turbulence-chemistry interaction (TCI) methodologies have been considered.

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.

Modeling plume interaction and collapse for direct-injection gasoline sprays is important because of its impact on fuel-air mixing and engine performance. Nevertheless, the aerodynamic interaction between plumes and the complicated two-phase coupling of the evaporating spray has shown to be notoriously difficult to predict. With the availability of high-speed (100 kHz) Particle Image Velocimetry (PIV) experimental data, we compare velocity field predictions between plumes to observe the full temporal evolution leading up to plume merging and complete spray collapse. The target “Spray G” operating conditions of the Engine Combustion Network (ECN) is the focus of the work, including parametric variations in ambient gas temperature. We apply both LES and RANS spray models in different CFD platforms, outlining features of the spray that are most critical to model in order to predict the correct aerodynamics and fuel-air mixing.

Large-eddy Simulations (LES) have been carried out to investigate spray variability and its effect on cycle-to-cycle flow variability in a direct-injection spark-ignition (DISI) engine under non-reacting conditions. Initial simulations were performed of an injector in a constant volume spray chamber to validate the simulation spray set-up. Comparisons showed good agreement in global spray measures such as the penetration. Local mixing data and shot-to-shot variability were also compared using Rayleigh-scattering images and probability contours. The simulations were found to reasonably match the local mixing data and shot-to-shot variability using a random-seed perturbation methodology. After validation, the same spray set-up with only minor changes was used to simulate the same injector in an optically accessible DISI engine. Particle Image Velocimetry (PIV) measurements were used to quantify the flow velocity in a horizontal plane intersecting the spark plug gap.