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Low-temperature heat release (LTHR) in spark-ignited internal combustion engines is a critical step toward the occurrence of auto-ignition, which can lead to an undesirable phenomenon known as engine knock. Hence, correct predictions of LTHR are of utmost importance to improve the understanding of knock and enable techniques aimed at controlling it. While LTHR is typically obscured by the deflagration following the spark ignition, extremely late ignition timings can lead to LTHR occurrence prior to the spark, i.e., pre-spark heat release (PSHR). In this research, PSHR in a boosted direct-injection SI engine was numerically investigated using three-dimensional computational fluid dynamics (CFD). A hybrid approach was used, based on the G-equation model for representing the turbulent flame front and the multi-zone well-stirred reactor model for tracking the chemical reactions within the unburnt region.

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.

Understanding and prediction of flash-boiling spray behavior in gasoline direct-injection (GDI) engines remains a challenge. In this study, computational fluid dynamics (CFD) simulations using the homogeneous relaxation model (HRM) for not only internal nozzle flow but also external spray were evaluated using CONVERGE software and compared to experimental data. High-speed extinction imaging experiments were carried out in a real-size axial-hole transparent nozzle installed at the tip of machined GDI injector fueled with n-pentane under various ambient pressure conditions (Pa/Ps = 0.07 - 1.39). The width of the spray during injection was assessed by means of projected liquid volume, but the structure and timing for boil-off of liquid within the sac of the injector were also assessed after the end of injection, including cases with different designed sac volumes.

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.

Diesel spray mixture formation is investigated at target conditions using multiple diagnostics and laboratories. High-speed Particle Image Velocimetry (PIV) is used to measure the velocity field inside and outside the jet simultaneously with a new frame straddling synchronization scheme. The PIV measurements are carried out in the Engine Combustion Network Spray A target conditions, enabling direct comparisons with mixture fraction measurements previously performed in the same conditions, and forming a unique database at diesel conditions. A 1D spray model, based upon mass and momentum exchange between axial control volumes and near-Gaussian velocity and mixture fraction profiles is evaluated against the data.

Combustion issued from an eight-hole, direct-injection spray was experimentally studied in a constant-volume pre-burn combustion vessel using simultaneous high-speed diffused back-illumination extinction imaging (DBIEI) and OH* chemiluminescence. DBIEI has been employed to observe the liquid-phase of the spray and to quantitatively investigate the soot formation and oxidation taking place during combustion. The fuel-air mixture was ignited with a plasma induced by a single-shot Nd:YAG laser, permitting precise control of the ignition location in space and time. OH* chemiluminescence was used to track the high-temperature ignition and flame. The study showed that increasing the delay between the end of injection and ignition drastically reduces soot formation without necessarily compromising combustion efficiency. For long delays between the end of injection and ignition (1.9 ms) soot formation was eliminated in the main downstream charge of the fuel spray.

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.

This paper presents a computational fluid dynamics (CFD) study of the Engine Combustion Network (ECN) Spray G under non-vaporizing condition, focusing on the impacts of fuel properties as well as realistic geometry on the atomization process. The large-eddy-simulation method, coupled with the volume-of-fluid method, is used to model the high-speed turbulent two-phase flow. A moving-needle boundary condition is applied to capture the internal flow boundary condition accurately. The injector geometry was measured with micron-level resolution using x-ray tomographic imaging at the Advanced Photon Source at Argonne National Laboratory, providing detailed machining tolerance and defects from manufacturing and a realistic rough surface. A 2.5-μm fine mesh is used to sufficiently resolve the details of liquid-gas interface and the breakup process.

In this study, three-dimensional large eddy simulations were performed to study the internal nozzle flow of the ECN Spray C diesel injector. Realistic nozzle geometry, full needle motion, and internal flow imaging data obtained from X-ray measurements were employed to initialize and validate the CFD model. The influence of injection pressure and fuel properties were investigated, and the effect of mesh size was discussed. The results agreed well with the experimental data of mass flow rate and correctly captured the flow structures inside the orifice. Simulations showed that the pressure drop near the sharp orifice inlet triggered flow separation, resulting in the ingestion of ambient gas into the orifice via a phenomenon known as hydraulic flip. At higher injection pressure, the pressure drop was more significant as the liquid momentum increased and the stream inertia was less prone to change its direction.

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.

Combustion cycle-to-cycle variation (CCV) of Spark-Ignition (SI) engines can be influenced by the cyclic variations in charge motion, trapped mass and mixture composition inside the cylinder. A high CCV leads to misfire or knock, limiting the engine’s operating regime. To understand the mechanism of the effect of flow field and mixture compositions on CCV, the present numerical work was performed in a single cylinder Direct Injection Spark-Ignition (DISI) engine. A large eddy simulation (LES) approach coupled with the G-equation combustion model was developed to capture the CCV by accurately resolving the turbulent flow field spatially and temporally. Further, the ignition process was modeled by sourcing energy during the breakdown and arc phases with a line-shape ignition model which could move with the local flow. Detailed chemistry was solved both inside and outside the flame front. A compact 48-species 152-reactions primary reference fuel (PRF) reduced mechanism was used.

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).

It is challenging to develop highly efficient and clean engines while meeting user expectations in terms of performance, comfort, and drivability. One of the critical aspects in this regard is combustion noise control. Combustion noise accounts for about 40 percent of the overall engine noise in typical turbocharged diesel engines. The experimental investigation of noise generation is difficult due to its inherent complexity and measurement limitations. Therefore, it is important to develop efficient numerical strategies in order to gain a better understanding of the combustion noise mechanisms. In this work, a novel methodology was developed, combining computational fluid dynamics (CFD) modeling and genetic algorithm (GA) technique to optimize the combustion system hardware design of a high-speed direct injection (HSDI) diesel engine, with respect to various emissions and performance targets including combustion noise.

Knock is a major bottleneck to achieving higher thermal efficiency in spark ignition (SI) engines. The overall tendency to knock is highly dependent on fuel anti-knock quality as well as engine operating conditions. It is, therefore, critical to gain a better understanding of fuel-engine interactions in order to develop robust knock mitigation strategies. In the present work, a numerical model based on three-dimensional (3-D) computational fluid dynamics (CFD) was developed to capture knock in a Cooperative Fuel Research (CFR) engine. For combustion modeling, a hybrid approach incorporating the G-equation model to track turbulent flame propagation, and a homogeneous reactor multi-zone model to predict end-gas auto-ignition ahead of the flame front and post-flame oxidation in the burned zone, was employed.

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.

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.

The necessity to study spray-wall interaction in internal combustion engines is driven by the evidence that fuel sprays impinge on chamber and piston surfaces resulting in the formation of wall films. This, in turn, may influence the air-fuel mixing and increase the hydrocarbon and particulate matter emissions. This work reports an experimental and numerical study on spray-wall impingement and liquid film formation in a constant volume combustion vessel. Diesel and n-heptane were selected as test fuels and injected from a side-mounted single-hole diesel injector at injection pressures of 120, 150, and 180 MPa on a flat transparent window. Ambient and plate temperatures were set at 423 K, the fuel temperature at 363 K, and the ambient densities at 14.8, 22.8, and 30 kg/m3. Simultaneous Mie scattering and schlieren imaging were carried out in the experiment to perform a visual tracking of the spray-wall interaction process from different perspectives.

Numerical modeling of fuel injection in internal combustion engines in a Lagrangian framework requires the use of a spray-wall interaction sub-model to correctly assess the effects associated with spray impingement. The spray impingement dynamics may influence the air-fuel mixing and result in increased hydrocarbon and particulate matter emissions. One component of a spray-wall interaction model is the splashed mass fraction, i.e. the amount of mass that is ejected upon impingement. Many existing models are based on relatively large droplets (mm size), while diesel and gasoline sprays are expected to be of micron size before splashing under high pressure conditions. It is challenging to experimentally distinguish pre- from post-impinged spray droplets, leading to difficulty in model validation.

Actual combustion strategies in internal combustion engines rely on fast and accurate injection systems to be successful. One of the injector designs that has shown good performance over the past years is the direct-acting piezoelectric. This system allows precise control of the injector needle position and hence the injected mass flow rate. Therefore, understanding how nozzle flow characteristics change as function of needle dynamics helps to choose the best lift law in terms of delivered fuel for a determined combustion strategy. Computational fluid dynamics is a useful tool for this task. In this work, nozzle flow of a prototype direct-acting piezoelectric has been simulated by using CONVERGE. Unsteady Reynolds-Averaged Navier-Stokes approach is used to take into account the turbulence. Results are compared with experiments in terms of mass flow rate. The nozzle geometry and needle lift profiles were obtained by means of X-rays in previous works.

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.