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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.

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 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.

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.

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.

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.