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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.

In-service 1994-2003 heavy-duty trucks were acquired by West Virginia University (WVU), equipped with the WVU Mobile Emissions Measurement System (MEMS) to measure on-road NOx, and driven on road routes near Sabraton, West Virginia, and extending up to Washington, PA to obtain real-world oxides of nitrogen (NOx) emissions data on highways and local roads. The MEMS measured 5Hz NOx, and load was obtained from the electronic control unit. Trucks were loaded to about 95% of their gross vehicle weights. Emissions in g/mi and g/bhp-hr were computed over the various road routes. In addition, some of the trucks were tested 1 to 2 years later to determine emission changes that may have occurred for these trucks. Emission results varied significantly over the different road routes due to different speeds, driving patterns, and road grades.

Reactivity controlled compression ignition (RCCI) is a form of dual-fuel combustion that exploits the reactivity difference between two fuels to control combustion phasing. This combustion approach limits the formation of oxides of nitrogen (NOX) and soot while retaining high thermal efficiency. The research presented herein was performed to determine the influences that high reactivity (diesel) fuel properties have on RCCI combustion characteristics, exhaust emissions, fuel efficiency, and the operable load range. A 4-cylinder, 1.9 liter, light-duty compression-ignition (CI) engine was converted to run on diesel fuel (high reactivity fuel) and compressed natural gas (CNG) (low reactivity fuel). The engine was operated at 2100 revolutions per minute (RPM), and at two different loads, 3.6 bar brake mean effective pressure (BMEP) and 6 bar BMEP.

With continuously increasing concern for the emissions from two-stroke engines including regulated hydrocarbon (HC) and oxides of nitrogen (NOx) emissions, non-road engines are implementing proven technologies from the on-road market. For example, four stroke diesel generators now include additional internal exhaust gas recirculation (EGR) via an intake/exhaust valve passage. EGR can offer benefits of reduced HC, NOx, and may even improve combustion stability and fuel efficiency. In addition, there is particular interest in use of natural gas as fuel for home power generation. This paper examines exhaust throttling applied to the Helmholtz resonator of a two-stroke, port injected, natural gas engine. The 34 cc engine was air cooled and operated at wide-open throttle (WOT) conditions at an engine speed of 5400 RPM with fueling adjusted to achieve maximum brake torque. Exhaust throttling served as a method to decrease the effective diameter of the outlet of the convergent cone.

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.

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.

Most transient heavy duty diesel emissions data in the USA have been acquired using the Federal Test Procedure (FTP), a heavy-duty diesel engine transient test schedule described in the US Code of Federal Regulations. The FTP includes both urban and freeway operation and does not provide data separated by driving mode (such as rural, urban, freeway). Recently, a four-mode engine test schedule was created for use in the Advanced Collaborative Emission Study (ACES), and was demonstrated on a 2004 engine equipped with cooled Exhaust Gas Recirculation (EGR). In the present work, the authors examined emissions using these ACES modes (Creep, Cruise, Transient and High-speed Cruise) and the FTP from a Detroit Diesel Corporation (DDC) Series 60 1992 12.7 liter pre-EGR engine. The engine emissions were measured using full exhaust dilution, continuous measurement of gaseous species, and filter-based Particulate Matter (PM) measurement.