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Lean combustion technologies show promise for improving engine efficiency and reducing emissions. Among these technologies, prechamber-assisted combustion (PCC) is established as a reliable option for achieving lean or ultra-lean combustion. In this study, the effect of engine speed on PCC was investigated in a naturally aspirated heavy-duty optical engine: a comparison has been made between analytical performances and optical flame behavior. Bottom view natural flame luminosity (NFL) imaging was used to observe the combustion process. The prechamber was fueled with methane, while the main chamber was fueled with methanol. The engine speed was varied at 1000, 1100, and 1200 revolutions per minute (rpm). The combustion in the prechamber is not affected by changes in engine speed. However, the heat release rate (HRR) in the main chamber changed from two distinct stages with a faster first stage to more gradual and merged stages as the engine speed increased.

To understand key practical aspects of ammonia as a fuel for internal combustion engines, three-dimensional computational fluid dynamics (CFD) simulations were performed using CONVERGETM. A light-duty single-cylinder research engine with a geometrical compression ratio of 11.5 and a conventional pentroof combustion chamber was experimentally operated at stoichiometry. The fumigated ammonia was introduced at the intake plenum. Upon model validation, additional sensitivity analysis was performed. The combustion was modeled using a detailed chemistry solver (SAGE), and the ammonia oxidation was computed from a 38-specie and 262-reaction chemical reaction mechanism. Three different piston shapes were assessed, and it was found that the near-spark flow field associated with the piston design in combination with the tumble motion promotes faster combustion and yields enhanced engine performance.

The global need for de-carbonization and stringent emission regulations are pushing the current engine research toward alternative fuels. Previous studies have shown that the uHC, CO, and CO2 emissions are greatly reduced and brake thermal efficiency increases with an increase in hydrogen concentration in methane-hydrogen blends for the richer mixture compositions. However, the combustion suffers from high NOx emissions. While these trends are well established, there is limited information on a detailed optical study on the effect of air-excess ratio for different methane-hydrogen mixtures. In the present study, experimental investigations of different methane-hydrogen blends between 0 and 100% hydrogen concentration by volume for the air-excess ratio of 1, 1.4, 1.8, and 2.2 were conducted in a heavy-duty optical diesel engine converted to spark-ignition operation. The engine was equipped with a flat-shaped optical piston to allow bottom-view imaging of the combustion chamber.

Typical automotive emission testing systems usually employ Flame Ionization Detection (FID) analyzers to measure unburned fuel species in the exhaust, but the technique is not suitable for engines operating on alcohol fuels. The FID method is not sensitive to measuring unburned alcohol fuels due to the presence of oxygen bonds in the fuel molecule. Other techniques, such as Fourier Transform Infrared (FTIR), can provide accurate unburned fuel measurements with alcohol fuel. However, these techniques are expensive and are less accessible compared to FID analyzers. In this study, the unburned fuel emissions from the engine exhaust were measured simultaneously with FID and FTIR analyzers, with the engine operating on pure alcohols, which are methanol, ethanol, and n-butanol. While most previous work focuses on stoichiometric air-fuel mixtures, a wide range of lean operating conditions between global-λ 1.6 to 2.8 will be tested in this study.

The current work utilizes computational fluid dynamics (CFD) simulations to assess the effects of different piston geometries in an active-type pre-chamber combustion engine fueled with methane. Previous works identified that the interaction of the jets with the main chamber flow and piston wall are key aspects for the local turbulent flame speed and overall burning duration. The combustion process is simulated with the G-equation model for flame propagation combined with the MZ-WSR model to determine the post-flame composition and to predict possible auto-ignition of the reactant mixture. Four setups were considered: two bowl-shaped and one flat piston, and one additional case of the flat piston with jets at wider jet angles to the cylinder axis. The results show that premature jet-wall interaction impacts the main chamber pressure build-up, turbulence, and burn rate.

The pre-chamber combustion can extend the lean limit of internal combustion engines (ICE) and hence increase their overall efficiency. Compared to active pre-chambers equipped with an auxiliary fuel supply system, passive pre-chambers have lower manufacturing costs and require minimal or no design modifications to the conventional spark plug engines. The major challenge of the passive pre-chamber is to extend the lean limit as much as the active pre-chamber. Computational fluid dynamics (CFD) simulations were conducted on a light-duty single-cylinder engine geometry fitted with a passive pre-chamber and using iso-octane as fuel to investigate and optimize the passive pre-chamber fuel enrichment through the pre-chamber nozzles. The non-reacting flow simulations were performed from the intake valve open (IVO) to spark timing.

Lean combustion is one of the most applied methods to increase engine efficiency and maintain a good trade-off with engine emissions. The pre-chamber combustion (PCC) is one of the most promising combustion concepts to extend the lean operating limits of the engine. The Narrow throat pre-chamber has shown better lean limit extension compared to other ignition sources. The pre-chamber jets and the main-chamber combustion were studied in a Heavy-Duty optical engine using methane fuel. The tested conditions covered global excess air ratios (λ), between 1.9 to 2.3. The combustion process was recorded using three collection systems: (a) Natural Flame Luminosity (NFL) with a temporal resolution of 0.1 CAD; (b) OH* Chemiluminescence, and (c) CH* Chemiluminescence with a temporal resolution of 0.2 CAD for both. The propagating velocity of the reacting jets was studied using Combustion Image Velocimetry (CIV) based on bottom view images of the main chamber.

Investigating combustion characteristics of oxygenated gasoline and gasoline blended ethanol is a subject of recent interest. The non-linearity in the interaction of fuel components in the oxygenated gasoline can be studied by developing chemical kinetics of relevant surrogate of fewer components. This work proposes a new reduced four-component (isooctane, heptane, toluene, and ethanol) oxygenated gasoline surrogate mechanism consisting of 67 species and 325 reactions, applicable for dynamic CFD applications in engine combustion and sprays. The model introduces the addition of eight C1-C3 species into the previous model (Li et al; 2019) followed by extensive tuning of reaction rate constants of C7 - C8 chemistry. The current mechanism delivers excellent prediction capabilities in comprehensive combustion applications with an improved performance in lean conditions.

To elucidate the complex characteristics of pre-chamber combustion engines, the interaction of the hot gas jets initiated by an active narrow throated pre-chamber with lean premixed CH4/air in a heavy-duty engine was studied computationally. A twelve-hole KAUST proprietary pre-chamber geometry was investigated using CONVERGE software. The KAUST pre-chamber has an upper conical part with the spark plug, and fuel injector, followed by a straight narrow region called the throat and nozzles connecting the chambers. The simulations were run for an entire cycle, starting at the previous cycle's exhaust valve opening (EVO). The SAGE combustion model was used with the chemistry modeled using a reduced methane oxidation mechanism based on GRI Mech 3.0, which was validated against in-house OH chemiluminescence data from the optical engine experiments.

Pre-chamber combustion (PCC) has re-emerged in recent last years as a potential solution to help to decarbonize the transport sector with its improved engine efficiency as well as providing lower emissions. Research into the combustion process inside the pre-chamber is still a challenge due to the high pressure and temperatures, the geometrical restrictions, and the short combustion durations. Some fundamental studies in constant volume combustion chambers (CVCC) at low and medium working pressures have shown the complexity of the process and the influence of high pressures on the turbulence levels. In this study, the pre-chamber combustion process was investigated by combustion visualization in an optically-accessible pre-chamber under engine relevant conditions and linked with the jet emergence inside the main chamber. The pre-chamber geometry has a narrow-throat. The total nozzle area is distributed in two six-hole rows of nozzle holes.

Flame lift-off length (FLOL), ignition delay time (IDT), liquid length (LL), and Soot are essential parameters defining spray combustion characteristics. They help understand the combustion dynamics and validate the spray and combustion models for numerical simulations. However, obtaining extensive data from experiments is costlier and time-consuming. Machine learning (ML) models have advanced to the point where they could create efficient models that could be used as surrogates for experiments. In this study, five different ML algorithms have been trained using the experimental dataset available through the engine combustion network (ECN) community. A novel genetic algorithm-based hyperparameter optimization code has been used to optimize the models to improve prediction accuracy. The model performances were compared, and the better model was chosen as an experimental surrogate to predict FLOL, IDT, LL, and Soot.

The high-pressure isobaric combustion has been proposed as the most suitable combustion mode for the double compre4ssion expansion engine (DCEE) concept. Previous experimental and simulation studies have demonstrated an improved efficiency compared to the conventional diesel combustion (CDC) engine. In the current study, isobaric combustion was achieved using a single injector with multiple injections. Since this concept involves complex phenomena such as spray to spray interactions, the computational models were extensively validated against the optical engine experiment data, to ensure high-fidelity simulations. The considered optical diagnostic techniques are Mie-scattering, fuel tracer planar laser-induced fluorescence (PLIF), and natural flame luminosity imaging. Overall, a good agreement between the numerical and experimental results was obtained.

The effects of intake pressure (Pin), start of injection (SOI), injection pressure (Pinj), injection split ratio (Qsplit), internal and external exhaust gas recirculation rates were varied to optimize several key parameters at a partially pre-mixed combustion low load/low speed condition using CFD. These include indicated specific fuel consumption (ISFC), combustion phasing (CA50), maximum rate of pressure rise (MRPR), maximum cylinder pressure (Pmax), indicated specific NOx (sNOx), indicated specific hydrocarbons (sHC) and Filter Smoke Number (FSN) emissions. Low-load point (6 bar indicated mean effective pressure (IMEP), 1500 revolutions per minute (RPM)) was selected where Pin varied between 1.25 and 1.5 bar, SOI between -100 and -10 crank angle degree (CAD) after top dead center (aTDC), Pinj between 100 and 200 bar, split ratio between 0 and 0.5, EGR between 0 and 45% and internal EGR measured by rebreathing valve height was varied between 0 and 4.5 mm.

The combustion system of a heavy-duty diesel engine operated in a gasoline compression ignition mode was optimized using a CFD-based response surface methodology and a machine learning genetic algorithm. One common dataset obtained from a CFD design of experiment campaign was used to construct response surfaces and train machine learning models. 128 designs were included in the campaign and were evaluated across three engine load conditions using the CONVERGE CFD solver. The design variables included piston bowl geometry, injector specifications, and swirl ratio, and the objective variables were fuel consumption, criteria emissions, and mechanical design constraints. In this study, the two approaches were extensively investigated and applied to a common dataset. The response surface-based approach utilized a combination of three modeling techniques to construct response surfaces to enhance the performance of predictions.

Gasoline compression ignition (GCI) pertains to high efficiency lean burn compression ignition with gasoline fuels, where ignition is controlled by mixture’s auto-ignition chemistry as well as local mixture strength. The presented GCI combustion strategy is based on a multi-mode combustion strategy at various operating conditions. This study presents a part of work on the development of an optimum combustion strategy at medium loading condition for commercial gasoline fuel with research octane number (RON) = 91. The single cylinder engine with a compression ratio (CR) = 16 features a centrally mounted multi-hole injector with a spark plug at a distance from the injector under shallow pent-roof combustion chamber design. The design of combustion chamber and piston was previously optimized based on CFD numerical analysis.

In this study, the authors investigated the effect of fuel properties on the combustion characteristics by employing methane, methanol, ethanol, and primary reference fuels (PRFs) as the main chamber fuel while using methane for the pre-chamber. Global excess air ratios (λ) from 1.6 to lean limit were tested, while 13% of total fuel energy supplied to the engine was delivered via the pre-chamber. The gaseous methane was injected into the pre-chamber at the gas exchange top-dead-center (TDC). Port fuel injection was tested with both open and closed inlet valves. The pre-chamber assembly was designed to fit into the diesel injector pocket of the base engine, which resulted in a narrow throat diameter of 3.3 mm. The combustion stability limit was set at 5% of the coefficient of variation of gross IMEP, and the knock intensity limit was set at 10 bar. GT-Power software was used to estimate the composition of pre-chamber species and was used in heat release analysis of the two chambers.

Pre-chamber combustion (PCC) is a promising engine combustion concept capable of extending the lean limit at part load. The engine experiments in the literature showed that the PCC could achieve higher engine thermal efficiency and much lower NOx emission than the spark-ignition engine. Improved understanding of the detailed flow and combustion physics of PCC is important for optimizing the PCC combustion. In this study, we investigated the gas exchange and flame jet from a narrow throat pre-chamber (PC) by only fueling the PC with methane in an optical engine. Simultaneous negative acetone planar laser-induced fluorescence (PLIF) imaging and OH* chemiluminescence imaging were applied to visualize the PC jet and flame jet from the PC, respectively. Results indicate a delay of the PC gas exchange relative to the built-up of the pressure difference (△ P) between PC and the main chamber (MC). This should be due to the gas inertia inside the PC and the resistance of the PC nozzle.

Pre-chamber combustion (PCC) allows an extension on the lean limit of an internal combustion engine (ICE). This combustion mode provides lower NOx emissions and shorter combustion durations that lead to a higher indicated efficiency. In the present work, a narrow throat pre-chamber was tested, which has a unique nozzle area distribution in two rows of six nozzle holes each. Tests were carried out in a modified heavy-duty engine for optical visualization. Methane was used as fuel for both the pre-chamber and the main chamber. Seven operating points were tested, including passive pre-chamber mode as a limit condition, to study the effect of pre- and main-chamber fuel addition on the pre-chamber jets and the main chamber combustion via chemiluminescence imaging. A typical cycle of one of the tested conditions is explained through the captured images. Observations of the typical cycle reveal a predominant presence of only six jets (from the lower row), with well-defined jet structures.

The regulatory requirements to reduce both greenhouse gases and exhaust gas pollutants from heavy duty engines are driving new perspectives on the interaction between fuels and engines. Fuels that reliefs the burden on engine manufacturers to reach these goals are of particular interest. A low carbon fuel with a higher volatility and heating value than diesel is one such fuel that reduces engine-out emissions and carbon footprint from the entire hydrocarbon lifecycle (well-to-wheel) and improves fuel efficiency, which is a main enabler for gasoline compression ignition (GCI) technology. The present study investigated the potential of GCI technology by evaluating the performance of a low carbon high efficiency, high reactivity gasoline fuel in Doosan’s 6L medium duty diesel engine.

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.