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Ammonia is one of the carbon-free alternatives considered for power generation and transportation sectors. But ammonia’s lower flame speed, higher ignition energy, and higher nitrogen oxides emissions are challenges in practical applications such as internal combustion engines. As a result, modifications in engine design and control and the use of a secondary fuel to initiate combustion such as natural gas are considered for ammonia-fueled engines. The higher-octane number of methane (the main component in natural gas) and ammonia allows for higher compression ratios, which in turn would increase the engine's thermal efficiency. One simple approach to initiate and control combustion for a high-octane fuel at higher compression ratios is to use a spark plug. This study experimentally investigated the operation of a heavy-duty compression ignition engine converted to spark ignition and ammonia-methane blends.

Machine learning algorithms are effective tools to reduce the number of engine dynamometer tests during internal combustion engine development and/or optimization. This paper provides a case study of using such a statistical algorithm to characterize the heat transfer from the combustion chamber to the environment during combustion and during the entire engine cycle. The data for building the machine learning model came from a single cylinder compression ignition engine (13.3 compression ratio) that was converted to natural-gas port fuel injection spark-ignition operation. Engine dynamometer tests investigated several spark timings, equivalence ratios, and engine speeds, which were also used as model inputs. While building the model it was found that adding the intake pressure as another model input improved model efficiency.

The combustion process in spark-ignition engines can vary considerably cycle by cycle, which may result in unstable engine operation. The phenomena amplify in natural gas (NG) spark-ignition (SI) engines due to the lower NG laminar flame speed compared to gasoline, and more so under lean burn conditions. The main goal of this study was to investigate the main sources and the characteristics of the cycle-by-cycle variation in heavy-duty compression ignition (CI) engines converted to NG SI operation. The experiments were conducted in a single-cylinder optically-accessible CI engine with a flat bowl-in piston that was converted to NG SI. The engine was operated at medium load under lean operating conditions, using pure methane as a natural gas surrogate. The CI to SI conversion was made through the addition of a low-pressure NG injector in the intake manifold and of a NG spark plug in place of the diesel injector.

Machine learning models were shown to provide faster results but with similar accuracy to multidimensional computational fluid dynamics or in-depth experiments. This study used a support-vector machine (SVM), a set of related supervised learning methods, to predict the dynamic performance (i.e., engine power and torque) of a heavy-duty natural gas spark ignition engine. The single-cylinder four-stroke test engine was fueled with methane. The engine was operated at different spark timings, mixture equivalence ratios, and engine speeds to provide the data for training and testing the proposed SVM. The results indicated that the performance and accuracy of the built regression model were satisfactory, with correlation coefficient quantities all larger than 0.95 and root-mean-square errors close to zero for both training and validation datasets.

Recent development in hydraulic fracking made natural gas (NG) to be a promising alternative gaseous fuel for heavy-duty diesel engines. The existing compression ignition (CI) engine can be retrofitted to NG spark ignition (SI) operation by replacing the diesel injector with a spark plug and fumigating NG into the intake manifold. However, the original diesel piston geometry (flat head and bowl-in-piston chamber) was usually retained to reduce modification cost. The goal of this study was to increase the understanding of the NG lean-burn characteristics in a diesel-like, fast-burn SI combustion chamber. The experimental platform can operate in conventional (i.e., all engine parts are metal) or in optical configuration (i.e., the stock piston and cylinder block are replaced with a see-through piston and an extended cylinder block). The optical data indicated a fast-propagated flame inside the piston bowl.

Natural gas is a promising alternative gaseous fuel due to its availability, economic, and environmental benefits. A solution to increase its use in the heavy-duty transportation sector is to convert existing heavy-duty compression ignition engines to spark-ignition operation by replacing the fuel injector with a spark plug and injecting the natural gas inside the intake manifold. The use of numerical simulations to design and optimize the natural gas combustion in such retrofitted engines can benefit both engine efficiency and emission. However, experimental data of natural gas combustion inside a bowl-in-piston chamber is limited. Consequently, the goal of this study was to provide high-quality experimental data from such a converted engine fueled with methane and operated at steady-state conditions, exploring variations in spark timing, engine speed and equivalence ratio.

Natural gas (NG) is an alternative fuel for spark-ignition engines. In addition to its cleaner combustion, recent breakthroughs in drilling technologies increased its availability and lowered its cost. NG consists of mostly methane, but it also contains heavier hydrocarbons and inert diluents, the levels of which vary substantially with geographical source, time of the year and treatments applied during production or transportation. To investigate the effects of NG composition on engine performance and emissions, a 3D CFD model of a heavy-duty diesel engine retrofitted to NG spark ignition simulated lean-combustion engine operation at low speed and medium load conditions. The work investigated three NG blends with similar lower heating value (i.e., similar energy density) but different Methane Number (MN). The results indicated that a lower MN increased flame propagation speed and thus increased in-cylinder pressure and indicated mean effective pressure.

On- and off-road heavy-duty diesel engines modified to spark-ignition natural gas operation can reduce U.S. dependence on imported oil and enhance national energy security. Engine conversion can be achieved through the addition of a gas injector in the intake manifold and of a spark plug in place of the diesel injector. This paper investigated combustion characteristics and engine performance at several lean-burn operating conditions that changed spark timing, mixture equivalence ratio, and engine speed, using methane as NG surrogate.

Internal combustion engines with optical access (a.k.a. optical engines) provide additional information in the quest for understanding the fundamental in-cylinder combustion phenomena. However, most optical engines have flat bowl-in-piston combustion chamber to optimize the visualization process, which is different, for example, from the traditional re-entrant bowl in compression ignition engines. A conventional heavy-duty direct-injection compression ignition engine was converted to spark ignition operation by replacing the fuel injector with a spark plug in both optical and metal setups to investigate the effect of the bowl geometry on flame propagation. Experimental data from steady-state lean-burn conditions was used to develop and validate a 3D CFD model of the engine. Numerical simulation results show that flame propagation in the radial direction was similar for both combustion chambers despite their different geometries.

Internal combustion diesel engines with optical access (a.k.a. optical engines) increase the fundamental understanding of combustion phenomena. However, optical access requirements result in most optical engines having a different in-cylinder geometry compared with the conventional diesel engine, such as a flat bowl-in-piston combustion chamber. This study investigated the effect of the bowl geometry on the flow motion and emissions inside a conventional heavy-duty direct-injection diesel engine that can operate in both metal and optical-access configurations. This engine was converted to natural-gas spark-ignition operation by replacing the fuel injector with a spark plug and adding a low-pressure gas injector in the intake manifold for fuel delivery, then operated at steady-state lean-burn conditions. A 3D CFD model based on the experimental data predicted that the different bowl geometry did not significantly affect in-cylinder emissions distribution.

3D CFD spark-ignition IC engine simulations are extremely complex for the regular user. Truly-predictive CFD simulations for the turbulent flame combustion that solve fully coupled transport/chemistry equations may require large computational capabilities unavailable to regular CFD users. A solution is to use a simpler phenomenological model such as the G-equation that decouples transport/chemistry result. Such simulation can still provide acceptable and faster results at the expense of predictive capabilities. While the G-equation is well understood within the experienced modeling community, the goal of this paper is to document some of them for a novice or less experienced CFD user who may not be aware that phenomenological models of turbulent flame combustion usually require heavy tuning and calibration from the user to mimic experimental observations.

Low-carbon fuels such as natural gas (NG) have the potential to lower the demand of petroleum-based fuels, reduce engine-out emissions, and increase IC engine thermal efficiency. One of the most rapid and efficient use of NG in the transportation sector would be as a direct replacement of the diesel fuel in compression ignition (CI) engines without any major engine modifications to the combustion chamber such as new pistons and/or engine head. An issue is the large variation in NG composition with the location and age of the gas well across U.S., which would affect engine operation, as well as the technology integration with emissions after treatment systems. This study used a conventional CI engine modified for spark ignition (SI) NG operation to investigate the effects of methane and a C1-C4 alkane blend on main combustion parameters like in-cylinder pressure, apparent heat release rate, IMEP, etc.

Leaner lifted-flame combustion (LLFC) is a mixing-controlled combustion strategy for compression-ignition (CI) engines that does not produce soot because the equivalence ratio at the lift-off length is less than or equal to approximately two. In addition to completely preventing soot formation, LLFC can simultaneously control emissions of nitrogen oxides because it is tolerant to the use of exhaust-gas recirculation for lowering in-cylinder temperatures. Experiments were conducted in a heavy-duty CI engine that has been modified to provide optical access to the combustion chamber, to study whether LLFC is facilitated by an oxygenated fuel blend (T50) comprising a 1:1 mixture by volume of tri-propylene glycol mono-methyl ether with an ultra-low-sulfur #2 diesel emissions-certification fuel (CFA). Results from the T50 experiments are compared against baseline results using the CFA fuel without the oxygenate.

Although soot-formation processes in diesel engines have been well characterized during the mixing-controlled burn, little is known about the distribution of soot throughout the combustion chamber after the end of appreciable heat release during the expansion and exhaust strokes. Hence, the laser-induced incandescence (LII) diagnostic was developed to visualize the distribution of soot within an optically accessible single-cylinder direct-injection diesel engine during this period. The developed LII diagnostic is semi-quantitative; i.e., if certain conditions (listed in the Appendix) are true, it accurately captures spatial and temporal trends in the in-cylinder soot field. The diagnostic features a vertically oriented and vertically propagating laser sheet that can be translated across the combustion chamber, where “vertical” refers to a direction parallel to the axis of the cylinder bore.

Natural luminosity (NL) and chemiluminescence (CL) imaging diagnostics are employed to investigate fuel-property effects on mixing-controlled combustion, using select research fuels-a #2 ultra-low sulfur emissions-certification diesel fuel (CF) and four of the Fuels for Advanced Combustion Engines (FACE) diesel fuels (F1, F2, F6, and F8)-that varied in cetane number (CN), distillation characteristics, and aromatic content. The experiments were performed in a single-cylinder heavy-duty optical compression-ignition (CI) engine at two injection pressures, three dilution levels, and constant start-of-combustion timing. If the experimental results are analyzed only in the context of the FACE fuel design parameters, CN had the largest effect on emissions and efficiency.

The effects of cetane number, aromatics content and 90% distillation temperature (T90) on HCCI combustion were investigated using a fuel matrix designed by the Fuels for Advanced Combustion Engines (FACE) Working Group of the Coordinating Research Council (CRC). The experiments were conducted in a single-cylinder, variable compression ratio, Cooperative Fuel Research (CFR) engine. The fuels were atomized and partially vaporized in the intake manifold. The engine was operated at a relative air/fuel ratio of 1.2, 60% exhaust gas recirculation (EGR) and 900 rpm. The compression ratio was varied over the range of 9:1 to 15:1 to optimize the combustion phasing for each fuel, keeping other operating parameters constant. The results show that cetane number and T90 distillation temperature significantly affected the combustion phasing. Cetane number was clearly found to have the strongest effect.