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In this study, the combustion system of a light-duty compression ignition engine running on a market gasoline fuel with Research Octane Number (RON) of 91 was optimized using computational fluid dynamics (CFD) and Machine Learning (ML). This work was focused on optimizing the piston bowl geometry at two compression ratios (CR) (17 and 18:1) and this exercise was carried out at full-load conditions (20 bar indicated mean effective pressure, IMEP). First, a limited manual piston design optimization was performed for CR 17:1, where a couple of pistons were designed and tested. Thereafter, a CFD design of experiments (DoE) optimization was performed where CAESES, a commercial software tool, was used to automatically perturb key bowl design parameters and CONVERGE software was utilized to perform the CFD simulations. At each compression ratio, 128 piston bowl designs were evaluated.

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.

Fuel stratification effects on the combustion and emissions behaviors for partially premixed compression ignition (PPCI) combustion of a high reactivity gasoline (research octane number of 80) was investigated using the third generation Gasoline Direct-Injection Compression Ignition (Gen3 GDCI) multi-cylinder engine. The PPCI combustion mode was achieved through a double injection strategy. The extent of in-cylinder fuel stratification was tailored by varying the start of second fuel injection timing (SOIsecond) while the first fuel injection event was held constant and occurred during the intake stroke. Based on the experimental results, three combustion characteristic zones were identified in terms of the SOIsecond - CA50 (crank angle at 50% cumulative heat release) relationship: (I) no response zone (HCCI-like combustion); (II) negative CA50 slope zone: (early PPCI mode); and (III) positive CA50 slope zone (late PPCI mode).

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

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.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty compression-ignition engine with a gasoline-like fuel that has an anti-knock index (AKI) of 58. The primary goal was to design an optimized combustion system utilizing the high volatility and low sooting tendency of the fuel for improved fuel efficiency with minimal hardware modifications to the engine. The CFD model predictions were first validated against experimental results generated using the stock engine hardware. A comprehensive design of experiments (DoE) study was performed at different operating conditions on a world-leading supercomputer, MIRA at Argonne National Laboratory, to accelerate the development of an optimized fuel-efficiency focused design while maintaining the engine-out NOx and soot emissions levels of the baseline production engine.

Fuels in the gasoline auto-ignition range (Research Octane Number (RON) > 60) have been demonstrated to be effective alternatives to diesel fuel in compression ignition engines. Such fuels allow more time for mixing with oxygen before combustion starts, owing to longer ignition delay. Moreover, by controlling fuel injection timing, it can be ensured that the in-cylinder mixture is “premixed enough” before combustion occurs to prevent soot formation while remaining “sufficiently inhomogeneous” in order to avoid excessive heat release rates. Gasoline compression ignition (GCI) has the potential to offer diesel-like efficiency at a lower cost and can be achieved with fuels such as low-octane straight run gasoline which require significantly less processing in the refinery compared to today’s fuels.

It is well known that in-nozzle flow behavior can significantly influence the near-nozzle spray formation and mixing that in turn affect engine performance and emissions. This in-nozzle flow behavior can, in turn, be significantly influenced by fuel properties. The goal of this study is to characterize the behavior of two different fuels, namely, a straight-run naphtha that has an anti-knock index of 58 (denoted as “Full-Range Naphtha”) and n-dodecane, in a simulated multi-hole common-rail diesel fuel injector. Simulations were carried out using a fully compressible multi-phase flow representation based on the mixture model assumption with the Volume of Fluid method. Our previous studies have shown that the characteristics of internal and near-nozzle flow are strongly related to needle motion in both the along- and off-axis directions.

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.

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.

The importance of radiative heat transfer on the combustion and soot formation characteristics under nominal ECN Spray A conditions has been studied numerically. The liquid n-dodecane fuel is injected with 1500 bar fuel pressure into the constant volume chamber at different ambient conditions. Radiation from both gas-phase as well as soot particles has been included and assumed as gray. Three different solvers for the radiative transfer equation have been employed: the discrete ordinate method, the spherical-harmonics method and the optically thin assumption. The radiation models have been coupled with the transported probability density function method for turbulent reactive flows and soot, where unresolved turbulent fluctuations in temperature and composition are included and therefore capturing turbulence-chemistry-soot-radiation interactions. Results show that the gas-phase (mostly CO2 ad H2O species) has a higher contribution to the net radiation heat transfer compared to soot.

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.

In internal combustion engine computational fluid dynamics (CFD) simulations, uncertainties arise from various sources, such as estimates of model parameters, experimental boundary conditions, estimates of chemical kinetic rates, etc. These uncertainties propagate through the model and may result in discrepancies compared to experimental measurements. The relative importance of the various sources of uncertainty can be quantified by performing a sensitivity analysis. In this work, global sensitivity analysis (GSA) was applied to engine CFD simulations of a low-temperature combustion concept called gasoline compression ignition, to understand the influence of experimental measurement uncertainties from various sources on specific targets of interest-spray penetration, ignition timing, combustion phasing, combustion duration, and emissions. The sensitivity of these targets was evaluated with respect to imposed uncertainties in experimental boundary conditions and fuel properties.

The primary strength of large eddy simulation (LES) is in directly resolving the instantaneous large-scale flow features which can then be used to study critical flame properties such as ignition, extinction, flame propagation and lift-off. However, validation of the LES results with experimental or direct numerical simulation (DNS) datasets requires the determination of statistically-averaged quantities. This is typically done by performing multiple realizations of LES and performing a statistical averaging among this sample. In this study, LES of n-dodecane spray flame is performed using a well-mixed turbulent combustion model along with a dynamic structure subgrid model. A high-resolution mesh is employed with a cell size of 62.5 microns in the entire spray and combustion regions. The computational cost of each calculation was in the order of 3 weeks on 200 processors with a peak cell count of about 22 million at 1 ms.

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.