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Accurate modeling of the internal flow and spray characteristics in fuel injectors is a critical aspect of direct injection engine design. However, such high-fidelity computational fluid dynamics (CFD) models are often computationally expensive due to the requirement of resolving fine temporal and spatial scales. This paper addresses the computational bottleneck issue by proposing a machine learning-based emulator framework, which learns efficient surrogate models for spatiotemporal flow distributions relevant for static coupling injection maps, namely total void fraction, velocity, and mass, within a design space of interest. Different design points involving variations of needle lift, fuel viscosity, and level of non-condensable gas in the fuel were explored in this study. An interpretable Bayesian learning strategy was employed to understand the effect of the design parameters on the void fraction fields at the exit of the injector orifice.

A 500-hour test cycle has been used to evaluate the durability of a prototype high pressure common rail injection system operating up to 1800 bar with E10 gasoline. Some aspects of the original diesel based hardware design were optimized in order to accommodate an opposed-piston, two-stroke engine application and also to mitigate the impacts of exposure to gasoline. Overall system performance was maintained throughout testing as fueling rate and rail pressure targets were continuously achieved and no physical damage was observed in the low-pressure components. Injectors showed no deviation in their flow characteristics after exposure to gasoline and high resolution imaging of the nozzle spray holes and pilot valve assemblies did not indicate the presence of cavitation damage. The high pressure pump did not exhibit any performance degradation during gasoline testing and teardown analysis after 500 hours showed no evidence of cavitation erosion.

An accurate prediction of internal nozzle flow in fuel injector offers the potential to improve predictions of spray computational fluid dynamics (CFD) in an engine, providing a coupled internal-external calculation or by defining better rate of injection (ROI) profile and spray angle information for Lagrangian parcel computations. Previous research has addressed experiments and computations in transparent nozzles, but less is known about realistic multi-hole diesel injectors compared to single axial-hole fuel injectors. In this study, the transient injector opening and closing is characterized using a transparent multi-hole diesel injector, and compared to that of a single axial hole nozzle (ECN Spray D shape). A real-size five-hole acrylic transparent nozzle was mounted in a high-pressure, constant-flow chamber. Internal nozzle phenomena such as cavitation and gas exchange were visualized by high-speed long-distance microscopy.

The modeling of fuel jet atomization is key in the characterization of Internal Combustion (IC) engines, and 3D Computational Fluid Dynamics (CFD) is a recognized tool to provide insights for design and control purposes. Multi-hole injectors with counter-bored nozzle are the standard for Gasoline Direct Injection (GDI) applications and the Spray-G injector from the Engine Combustion Network (ECN) is considered the reference for numerical studies, thanks to the availability of extensive experimental data. In this work, the behavior of the Spray-G injector is simulated in a constant volume chamber, ranging from sub-cooled (nominal G) to flashing conditions (G2), validating the models on Diffused Back Illumination and Phase Doppler Anemometry data collected in vaporizing inert conditions.

The occurrence of cavitation inside injectors is generally undesirable since it can cause material erosion and result in deviations from the expected injector performance. Previous numerical work employing an injector geometry measured with x-ray diagnostics and operating with a high-volatility straight-run gasoline (SRG) has shown that: (1) most of the cavitation is generally observed at low needle lifts, (2) needle motion is responsible for asymmetric structures in the internal flow as well as large pressure and velocity gradients that trigger phase transition at the orifice inlets, and (3) cavitation affects the injector discharge coefficient and distribution of injected fuel. To explore the potential for material damage within the injector orifices due to cavitation cloud collapse, the cavitation-induced erosion risk assessment (CIERA) tool has been applied for the first time to the realistic geometry of a heavy-duty injector using the CONVERGE software.

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.

Cavitation and cavitation-induced erosion have been observed in fuel injectors in regions of high acceleration and low pressure. Although these phenomena can have a large influence on the performance and lifetime of injector hardware, questions still remain on how these physics should be accurately and efficiently represented within a computational fluid dynamics model. While several studies have focused on the validation of cavitation predictions within canonical and realistic injector geometries, it is not well documented what influence the numerical and physical parameters selected to represent turbulence and phase change will have on the predictions for cavitation erosion propensity and severity. In this work, a range of numerical and physical parameters are evaluated within the mixture modeling approach in CONVERGE to understand their influence on predictions of cavitation, condensation and erosion.

Cyclic variability in internal combustion engines (ICEs) arises from multiple concurrent sources, many of which remain to be fully understood and controlled. This variability can, in turn, affect the behavior of the engine resulting in undesirable deviations from the expected operating conditions and performance. Shot-to-shot variation during the fuel injection process is strongly suspected of being a source of cyclic variability. This study focuses on the shot-to-shot variability of injector needle motion and its influence on the internal nozzle flow behavior using diesel fuel. High-speed x-ray imaging techniques have been used to extract high-resolution injector geometry images of the sac, orifices, and needle tip that allowed the true dynamics of the needle motion to emerge. These measurements showed high repeatability in the needle lift profile across multiple injection events, while the needle radial displacement was characterized by a much higher degree of randomness.

In order to improve understanding of the primary atomization process for diesel-like sprays, a collaborative experimental and computational study was focused on the near-nozzle spray structure for the Engine Combustion Network (ECN) Spray D single-hole injector. These results were presented at the 5th Workshop of the ECN in Detroit, Michigan. Application of x-ray diagnostics to the Spray D standard cold condition enabled quantification of distributions of mass, phase interfacial area, and droplet size in the near-nozzle region from 0.1 to 14 mm from the nozzle exit. Using these data, several modeling frameworks, from Lagrangian-Eulerian to Eulerian-Eulerian and from Reynolds-Averaged Navier-Stokes (RANS) to Direct Numerical Simulation (DNS), were assessed in their ability to capture and explain experimentally observed spray details. Due to its computational efficiency, the Lagrangian-Eulerian approach was able to provide spray predictions across a broad range of conditions.

The sparking behavior in an internal combustion engine affects the fuel efficiency, engine-out emissions, and general drivability of a vehicle. As emissions regulations become progressively stringent, combustion strategies, including exhaust gas recirculation (EGR), lean-burn, and turbocharging are receiving increasing attention as models of higher efficiency advanced combustion engines with reduced emissions levels. Because these new strategies affect the working environment of the spark plug, ongoing research strives to understand the influence of external factors on the spark ignition process. Due to the short time and length scales involved and the harsh environment, experimental quantification of the deposited energy from the sparking event is difficult to obtain. In this paper, we present the results of x-ray radiography measurements of spark ignition plasma generated by a conventional spark plug.

The atomization and initial spray formation processes in direct injection engines are not well understood due to the experimental and computational challenges associated with resolving these processes. Although different physical mechanisms, such as aerodynamic-induced instabilities and nozzle-generated turbulence and cavitation, have been proposed in the literature to describe these processes, direct validation of the theoretical basis of these models under engine-relevant conditions has not been possible to date. Recent developments in droplet sizing measurement techniques offer a new opportunity to evaluate droplet size distributions formed in the central and peripheral regions of the spray. There is therefore a need to understand how these measurements might be utilized to validate unobservable physics in the near nozzle-region.

Understanding the short-lived structure of the plasma that forms between the electrodes of a spark plug is crucial to the development of improved ignition models for SI engines. However, measuring the amount of energy deposited in the gas directly and non-intrusively is difficult, due to the short time scales and small length scales involved. The breakdown of the spark gap occurs at nanosecond time scales, followed by an arc phase lasting a few microseconds. Finally, a glow discharge phase occurs over several milliseconds. It is during the arc and glow discharge phases that most of the heat transfer from the plasma to the electrodes and combustion gases occurs. Light emission can be used to measure an average temperature, but micron spatial resolution is required to make localized measurements.

Using natural gas in an internal combustion engine (ICE) is emerging as a promising way to improve thermal efficiency and reduce exhaust emissions. In the development of such engine platforms, computational fluid dynamics (CFD) plays a fundamental role in the optimization of geometries and operating parameters. One of the most relevant issues in the simulation of direct injection (DI) gaseous processes is the accurate prediction of the gas jet evolution. The simulation of the injection process for a gaseous fuel does not require complex modeling, nevertheless properly describing high-pressure gas jets remains a challenging task. At the exit of the nozzle, the injected gas is under-expanded, the flow becomes supersonic and shocks occur due to compressibility effects. These phenomena lead to challenging computational requirements resulting from high grid resolution and low computational time-steps.

The salient features of modern gasoline direct injection include cavitation, flash boiling, and plume/plume interaction, depending on the operating conditions. These complex phenomena make the prediction of the spray behavior particularly difficult. The present investigation combines mass-based experimental diagnostics with an advanced, in-house modeling capability in order to provide a multi-faceted study of the Engine Combustion Network’s Spray G injector. First, x-ray tomography is used to distinguish the actual injector geometry from the nominal geometry used in past works. The actual geometry is used as the basis of multidimensional CFD simulations which are compared to x-ray radiography measurements for validation under cold conditions. The influence of nozzle diameter and corner radius are of particular interest. Next, the model is used to simulate flash-boiling conditions, in order to understand how the cold flow behavior corresponds to flashing performance.

Quantitative measurements of direct injection fuel spray density and mixing are difficult to achieve using optical diagnostics, due to the substantial scattering of light and high optical density of the droplet field. For multi-hole sprays, the problem is even more challenging, as it is difficult to isolate a single spray plume along a single line of sight. Time resolved x-ray radiography diagnostics developed at Argonne's Advanced Photon Source have been used for some time to study diesel fuel sprays, as x-rays have high penetrating power in sprays and scatter only weakly. Traditionally, radiography measurements have been conducted along any single line of sight, and have been applied to single-hole and group-hole nozzles with few plumes. In this new work, we extend the technique to multi-hole gasoline direct injection sprays.

Recent advances in x-ray spray diagnostics at Argonne National Laboratory's Advanced Photon Source have made absorption measurements of individual spray events possible. A focused x-ray beam (5×6 μm) enables collection of data along a single line of sight in the flow field and these measurements have allowed the calculation of quantitative, shot-to-shot statistics for the projected mass of fuel sprays. Raster scanning though the spray generates a two-dimensional field of data, which is a path integrated representation of a three-dimensional flow. In a previous work, we investigated the shot-to-shot variation over 32 events by visualizing the ensemble standard deviations throughout a two dimensional mapping of the spray. In the current work, provide further analysis of the time to steady-state and steady-state spatial location of the fluctuating field via the transverse integrated fluctuations (TIF).

Spray processes, such as primary breakup, play an important role for subsequent combustion processes and emissions formation. Accurate modeling of these spray physics is therefore key to ensure faithful representation of both the global and local characteristics of the spray. However, the governing physical mechanisms underlying primary breakup in fuel sprays are still not known. Several theories have been proposed and incorporated into different engineering models for the primary breakup of fuel sprays, with the most widely employed models following an approach based on aerodynamically-induced breakup, or more recently, based on liquid turbulence-induced breakup. However, a complete validation of these breakup models and theories is lacking since no existing measurements have yielded the joint liquid mass and drop size distribution needed to fully define the spray, especially in the near-nozzle region.

Recent advancements in x-ray radiography diagnostics for direct injection sprays at Argonne's Advanced Photon Source have allowed absorption measurements of individual spray events, in addition to ensemble-averaged measurements. These measurements offer insight into the shot-to-shot variation of these sprays in the near-nozzle, spray formation region. Three single hole diesel injectors are studied across various injection and ambient pressures, spanning 14 different conditions. We calculated two dimensional maps of the standard deviation in line of sight mass distribution between individual spray events. These illuminated the spatial and temporal extent of variability between spray events. Regions of large fluctuations were observed to move downstream during the initial spray period and reached a steady state location after this initial transient.

This paper focuses on detailed numerical simulations of direct injection diesel and gasoline sprays from production grade, multi-hole injectors. In a dual-fuel engine the direct injection of both the fuels can facilitate appropriate mixture preparation prior to ignition and combustion. Diesel and gasoline sprays were simulated using high-fidelity Large Eddy Simulations (LES) with the dynamic structure sub-grid scale model. Numerical predictions of liquid penetration, fuel density distribution as well as transverse integrated mass (TIM) at different axial locations versus time were compared against x-ray radiography data obtained from Argonne National Laboratory. A necessary, but often overlooked, criterion of grid-convergence is ensured by using Adaptive Mesh Refinement (AMR) for both diesel and gasoline. Nine different realizations were performed and the effects of random seeds on spray behavior were investigated.

It is common practice to validate diesel spray models against experimental diesel-spray images based on elastic light scattering, but the metric used to define the liquid boundary in a modeled spray can be physically inconsistent with the liquid boundary detected by light scattering measurements. In particular, spray models typically define liquid penetration based on a liquid mass threshold, while light scattering signal intensities are based on droplet size and volume fraction. These metrics have different response characteristics to changes in ambient conditions and fuel properties. Thus, when spray models are “tuned” or calibrated to match these types of measurements, the predictive capabilities of these models can be compromised. In this work, we compare two different liquid length metrics of an evaporating, non-reacting n-dodecane spray under diesel-like conditions using KIVA-3V.