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Detailed characteristics of morphology, nanostructures, and sizes were analyzed for particulate matter (PM) emissions from low-temperature combustion (LTC) modes of a single-cylinder, light-duty diesel engine. The LTC engines have been widely studied in an effort to achieve high combustion efficiency and low exhaust emissions. Recent reports indicate that the number of nucleation mode particles increased in a broad engine operating range, which implies a negative impact on future PM emissions regulations in terms of the nanoparticle number. However, the size measurement of solid carbon particles by commercial instruments is indeed controversial due to the contribution of volatile organics to small nanoparticles. In this work, an LTC engine was operated with various biofuel blends, such as blends (B20) of soy bean oil (soy methyl ester, SME20) and palm oil (palm methyl ester, PME20), as well as an ultra-low-sulfur diesel fuel.

The objective of this research is a detailed investigation of particulate sizing and number count from a spark-ignited, direct-injection (SIDI) engine at different operating conditions. The engine is a 549 [cc] single-cylinder, four-valve engine with a flat-top piston, fueled by Tier II EEE. A baseline engine operating condition, with a low number of particulates, was established and repeatability at this condition was ascertained. This baseline condition is specified as 2000 rpm, 320 kPa IMEP, 280 [°bTDC] end of injection (EOI), and 25 [°bTDC] ignition timing. The particle size distributions were recorded for particle sizes between 7 and 289 [nm]. The baseline particle size distribution was relatively flat, around 1E6 [dN/dlogDp], for particle diameters between 7 and 100 [nm], before dropping off to decreasing numbers at larger diameters. Distributions resulting from a matrix of different engine conditions were recorded.

Diesel particulate samples were collected from a light duty engine operated at a single speed-load point with a range of biodiesel and conventional fuel blends. The oxidation reactivity of the samples was characterized in a laboratory reactor, and BET surface area measurements were made at several points during oxidation of the fixed carbon component of both types of particulate. The fixed carbon component of biodiesel particulate has a significantly higher surface area for the initial stages of oxidation, but the surface areas for the two particulates become similar as fixed carbon oxidation proceeds beyond 40%. When fixed carbon oxidation rates are normalized to total surface area, it is possible to describe the oxidation rates of the fixed carbon portion of both types of particulates with a single set of Arrhenius parameters. The measured surface area evolution during particle oxidation was found to be inconsistent with shrinking sphere oxidation.

Sources of unburned hydrocarbon (UHC) emissions are examined for a highly dilute (10% oxygen concentration), moderately boosted (1.5 bar), low load (3.0 bar IMEP) operating condition in a single-cylinder, light-duty, optically accessible diesel engine undergoing partially-premixed low-temperature combustion (LTC). The evolution of the in-cylinder spatial distribution of UHC is observed throughout the combustion event through measurement of liquid fuel distributions via elastic light scattering, vapor and liquid fuel distributions via laser-induced fluorescence, and velocity fields via particle image velocimetry (PIV). The measurements are complemented by and contrasted with the predictions of multi-dimensional simulations employing a realistic, though reduced, chemical mechanism to describe the combustion process.

This paper investigates flow and combustion in a full-cycle simulation of a four-stroke, three-valve HCCI engine by visualizing the flow with pathlines. Pathlines trace massless particles in a transient flow field. In addition to visualization, pathlines are used here to trace the history, or evolution, of flow fields and species. In this study evolution is followed from the intake port through combustion. Pathline analysis follows packets of intake charge in time and space from induction through combustion. The local scalar fields traversed by the individual packets in terms of velocity magnitude, turbulence, species concentration and temperatures are extracted from the simulation results. The results show how the intake event establishes local chemical and thermal environments in-cylinder and how the species respond (chemically react) to the local field.

An overall diesel engine and aftertreatment system model has been created that integrates diesel engine, exhaust system, engine emissions, and diesel particulate filter (DPF) models using MATLAB Simulink. The 1-D engine and exhaust system models were developed using WAVE. The engine emissions model combines a phenomenological soot model with artificial neural networks to predict engine out soot emissions. Experimental data from a light-duty diesel engine was used to calibrate both the engine and engine emissions models. The DPF model predicts the behavior of a clean and particulate-loaded catalyzed wall-flow filter. Experimental data was used to validate this sub-model individually. Several model integration issues were identified and addressed. These included time-step selection, continuous vs. limited triggering of sub-models, and code structuring for simulation speed. Required time-steps for different sub models varied by orders of magnitude.

A procedure has been developed to build system level predictive models that incorporate physical laws as well as information derived from experimental data. In particular a soot model was developed, trained and tested using experimental data. It was seen that the model could fit available experimental data given sufficient training time. Future accuracy on data points not encountered during training was estimated and seen to be good. The approach relies on the physical phenomena predicted by an existing system level phenomenological soot model coupled with ‘weights’ which use experimental data to adjust the predicted physical sub-model parameters to fit the data. This approach has developed from attempts at incorporating physical phenomena into neural networks for predicting emissions. Model training uses neural network training concepts.

An experimental study was carried out to investigate the effects of fuel injection timing on detailed chemical composition and size distributions of diesel particulate matter (PM) and regulated gaseous emissions in a modern heavy-duty D.I. diesel engine. These measurements were made for two different diesel fuels: No. 2 diesel (Fuel A) and ultra low sulfur diesel (Fuel B). A single-cylinder 2.3-liter D.I. diesel engine equipped with an electronically controlled unit injection system was used in the experiments. PM measurements were made with an enhanced full-dilution tunnel system at the Engine Research Center (ERC) of the University of Wisconsin-Madison (UW-Madison) [1, 2]. The engine was run under 2 selected modes (25% and 75% loads at 1200 rpm) of the California Air Resources Board (CARB) 8-mode test cycle.

A single cylinder CFR research engine has been run in HCCI combustion mode for a range of temperatures and fuel compositions. The data indicate that the best HCCI operation, as measured by a combination of successful combustion with low ISFC, occurs at or near the rich limit of operation. Analysis of the pressure and heat release histories indicated the presence, or absence, and impact of the fuel's NTC ignition behavior on establishing successful HCCI operation. The auto-ignition trends observed were in complete agreement with previous results found in the literature. Furthermore, analysis of the importance of the fuel's octane sensitivity, through assessment of an octane index, successfully explained the changes in the fuels auto-ignition tendency with changes in engine operating conditions.

This paper will focus on the spray characteristics of a high pressure (up to 155 MPa) accumulator type injector in a high pressure (chosen density) quiescent spray chamber. The injector uses a standard single orifice nozzle which produces a full cone spray. Using this apparatus, we are examining the fundamental aspects of high pressure spray formation under controlled conditions. Experimental data was collected using high speed photography (10,000 frames per second) which used a pulsed copper-vapor laser as a light source. Two photographic techniques are being utilized. Direct attenuation allows measurement of tip penetration, spray cone angle, and injection duration. Scattering from a sheet of laser light perpendicular to the camera field of view is being developed in an attempt to resolve inner spray cone structure. In addition to the quantitative data from the high speed photography, injector accumulator pressure, supply pressure and injection rate histories were recorded.

The heat release type models have been classified as zero-dimensional because they have no spatial resolution and therefore don't contain any information on the fluid mechanics in them. They have been a significant contribution because they incorporate the rate processes occurring in the engine and are an aid in the analysis of the data. Because they are thermodynamic models it is necessary to define the control volumes on which the thermodynamic analysis is to be performed. The different control volume descriptions, called one, two and three zone models, and the analysis of the combustion event using these models is discussed. Finally a description of second law zero-dimensional models is given. These models have similar limitations as the First Law models; no spatial resolution and a control volume definition is required. These models are useful because they enable one to analyze the magnitude of the losses that occur in the different processes which comprise the engine cycle.