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The scattering properties (influenced by morphology) and refractive index (dependent on microstructure) of engine-emitted soot influences its effect on climate, as well as how we interpret optical measurements of aerosols. The morphology and microstructure of soot from two different engines were studied. The soot samples were collected from a 1.9L Volkswagen TDI engine for two different fuel types (ULSD and B20) and six speed/load combinations., as well as from a Cummins ISX heavy-duty engine using the Westport pilot-ignited high-pressure direct-injection (HPDI) natural-gas fuelling system for three different speed/load combinations. The transmission electron microscopy (TEM) was employed to investigate the soot morphology, emphasizing the fractal properties. Image processing was used to extract the geometrical properties of the thirty-five randomly chosen aggregates from each sample.

Soot emissions from direct-injection engines are sensitive to the fuel-air mixing process, and may vary between combustion cycles due to turbulence and injector variability. Conventional exhaust emissions measurements cannot resolve inter- or intra-cycle variations in particle emissions, which can be important during transient engine operations where a few cycles can disproportionately affect the total exhaust soot. The Fast Exhaust Nephelometer (FEN) is introduced here to use light scattering to measure particulate matter concentration and size near the exhaust port of an engine with a time resolution of better than one millisecond. The FEN operates at atmospheric pressure, sampling near the engine exhaust port and uses a laser diode to illuminate a small measurement volume. The scattered light is focused on two amplified photodiodes.

Alternative fuel injection systems and advanced in-cylinder diagnostics are two important tools for engine development; however, the rapid and simultaneous achievement of these goals is often limited by the space available in the cylinder head. Here, a research-oriented cylinder head is developed for use on a single cylinder 2-litre engine, and permits three simultaneous in-cylinder combustion diagnostic tools (cylinder pressure measurement, infrared absorption, and 2-color pyrometry). In addition, a modular injector mounting system enables the use of a variety of direct fuel injectors for both gaseous and liquid fuels. The purpose of this research-oriented cylinder head is to improve the connection between thermodynamic and optical engine studies for a wide variety of combustion strategies by facilitating the application of multiple in-cylinder diagnostics.

The effects of impinging airflow on the near nozzle characteristics of an inwardly opening, high pressure-swirl atomiser are investigated in an optically-accessed, steady-state flow rig designed to emulate the intake flow of a typical, side-injected, 4-valve gasoline direct-injection combustion system. The results indicate that the impinging airflow has a relatively minor effect on the initial break-up of the fuel spray. However, the secondary break-up of the spray, i.e. the break-up of liquid ligaments, the spatial distribution of droplets within the spray and the location of the spray within the cylinder are significantly affected by the impinging air.

Measurements of ignition characteristics and emissions were made in a shock tube facility operating at engine-relevant conditions. Methane and methane/ethane fuels were injected down the centerline of the shock tube using an electronically controlled prototype gaseous fuel injector developed by Westport Innovations. Air was preheated and compressed using a reflected shock technique that produced run times of 4-5 ms. Particulate matter (PM) emissions were found to be highly intermittent. In only 6 out of 97 experiments was PM detected above background levels. In all of these 6 sooting experiments ignition kernels were located relatively close to the injector tip and ignition occurred prior to the end of fuel injection. Using the large orifice injector tip with pure methane fuel, PM was detected in 4 out of 28 experiments; using the small orifice with pure methane fuel, no PM was detected in any of 50 experiments.

This paper presents a sensitivity-based input selection algorithm and a layered modeling approach for improving Gaussian Process Regression (GPR) modeling with hyperparameter optimization for engine model development with data sets of 120 training points or less. The models presented here are developed for a Pilot-Ignited Direct-Injected Natural Gas (PIDING) engine. A previously developed GPR modeling method with hyperparameter optimization produced some models with normalized root mean square error (nRMSE) over 0.2. The input selection method reduced the overall error by 0.6% to 18.85% while the layered modeling method improved the error for carbon monoxide (CO) by 52.6%, particulate matter (PM) by 32.5%, and nitrogen oxides (NOX) by 29.8%. These results demonstrate the importance of selecting only the most relevant inputs for machine learning models.

Heavy-duty diesel trucking is responsible for 25%-30% of the road transportation CO2 emissions in North America. Retrofitting class-8 trucks with a complementary hydrogen fuelling system makes it possible to co-combust hydrogen and diesel in the existing internal combustion engine (ICE), thus minimizing the costs associated with switching to non-ICE platforms and reducing the barrier for the implementation of low-carbon gaseous fuels such as hydrogen. This retrofitting approach is evaluated based on the exhaust emissions of a converted truck with several thousand kilometres of road data. The heavy-duty truck used here was retrofitted with an air-intake hydrogen injection system, onboard hydrogen storage tanks, and a proprietary hydrogen controller enabling it to operate in hydrogen-diesel co-combustion (HDC) mode.

This paper examines the combustion and emissions produced using a prototype fuel injector nozzle for pilot-ignited direct-injection natural gas engines. In the new geometry, 7 individual equally-spaced gas injection holes were replaced by 7 pairs of closely-aligned holes (“paired-hole nozzle”). The paired-hole nozzle was intended to reduce particulate formation by increasing air entrainment due to jet interaction. Tests were performed on a single-cylinder research engine at different speeds and loads, and over a range of fuel injection and air handling conditions. Emissions were compared to those resulting from a reference injector with equally spaced holes (“single-hole nozzle”). Contrary to expectations, the CO and PM emissions were 3 to 10 times higher when using the paired-hole nozzles. Despite the large differences in emissions, the relative change in emissions in response to parametric changes was remarkably similar for single-hole and paired-hole nozzles.

In direct-injection engines, combustion and emission formation is strongly affected by injection quality. Injection quality is related to mass-flow rate shape, momentum rate shape, stability of pulses as well as mechanical and hydraulic delays associated with fuel injection. Finding these injector characteristics aids the interpretation of engine experiments and design of new injection strategies. The goal of this study is to investigate the rate of momentum for the single and post injections for high-pressure direct-injection natural gas injectors. The momentum measurement method has been used before to study momentum rate of injection for single and split injections for diesel sprays. In this paper, a method of momentum measurement for gas injections is developed in order to present transient momentum rate shape during injection timing. In this method, a gas jet impinges perpendicularly on a pressure transducer surface.

The emissions and efficiency of modern internal combustion engines need to be improved to reduce their environmental impact. Many strategies to address this (e.g., alternative fuels, exhaust gas aftertreatment, novel injection systems, etc.) require engine calibrations to be modified, involving extensive experimental data collection. A new approach to modeling and data collection is proposed to expedite the development of these new technologies and to reduce their upfront cost. This work evaluates a Gaussian Process Regression, Artificial Neural Network and Bayesian Optimization based strategy for the efficient development of machine learning models, intended for engine optimization and calibration. The objective of this method is to minimize the size of the required experimental data set and reduce the associated data collection cost for engine modeling.