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The use of Artificial Neural Networks (ANNs) as a predictive tool has been shown to have a broad range of applications. Earlier work by the authors using ANN models to predict carbon dioxide (CO2), carbon monoxide (CO), oxides of nitrogen (NOx), and particulate matter (PM) from heavy-duty diesel engines and vehicles yielded marginal to excellent results. These ANN models can be a useful tool in inventory prediction, hybrid vehicle design optimization, and incorporated into a feedback loop of an on-board, active fuel injection management system. In this research, the ANN models were trained on continuous engine and emissions data. The engine data were used as inputs to the ANN models and consisted of engine speed, torque, and their respective first and second derivatives over a one, five, and ten second time range. The continuous emissions data were the desired output that the ANN models learned to predict through an iterative training process.

Transit agencies across the United States operate bus fleets primarily powered by diesel, natural gas, and hybrid drive systems. Passenger loading affects the power demanded from the engine, which in turn affects distance-specific emissions and fuel consumption. Analysis shows that the nature of bus activity, taking into account the idle time, tire rolling resistance, wind drag, and acceleration energy, influences the way in which passenger load impacts emissions. Emissions performance and fuel consumption from diesel and natural gas powered buses were characterized by the West Virginia University (WVU) Transportable Emissions Testing Laboratory. A comparison matrix for all three bus technologies included three common driving cycles (the Braunschweig Cycle, the OCTA Cycle, and the ADEME-RATP Paris Cycle). Each bus was tested at three different passenger loading conditions (empty weight, half weight, and full weight).

When preparing inventory models, it is desirable to obtain representative distance-specific emissions factors that truthfully represent the vehicle activity on a particular road (facility) type. Unfortunately, emissions values are often measured using only one test schedule, which represents a single average speed and a specific type of activity. This paper investigated the accuracy of predicting the emissions for a test schedule based on measurements from a different test schedule for the case of a medium heavy-duty truck. First, the traditional Speed Correction Factor (SCF) approach was examined, followed by the use of a power-based model derived from continuous data, followed by an artificial neural network (ANN) approach. The SCF modeling used distance-averaged emissions and cycle-averaged vehicle speed to predict distance-averaged NOx. The power-based modeling was based on linear and polynomial correlations between continuous axle power and NOx.

The Stiller-Smith mechanism is a new mechanism for the translation of linear motion into rotary motion, and has been considered as an alternative to the conventional slider-crank mechanism in the design of internal combustion engines and piston compressors. Piston motion differs between the two mechanisms, being perfectly sinusoidal for the Stiller-Smith case. Plots of dimensionless volume and volume rate-change are presented for one engine cycle. It is argued that the different motion is important when considering rate-based processes such as heat transfer to a cylinder wall and chemical kinetics during combustion. This paper also addresses the fact that a Stiller-Smith engine will be easier to configure for adiabatic operation, with many attendant benefits.

Inventory approaches for truck and bus emissions rely heavily on certification data, and no comprehensive results have been published to date. Two transportable chassis dynamometer laboratories developed and operated by West Virginia University (WVU) have been used extensively to gather realistic emission data from heavy-duty vehicles tested in the field, in controlled, simulated driving conditions. By default, a comprehensive database has been assembled, that comprises a wide variety of vehicles, engines, fuels, and driving scenarios. A subset of these data is analyzed in this paper for an illustration of practical utilization of such information, either for inventory assessments, or for comparative and correlation studies. General guidelines for data screening and analysis approaches are provided, along with examples of specific results and discussions for a selected cross-section of samples.

A study was performed in the spring of 2001 to chemically characterize exhaust emissions from trucks and buses fueled by various test fuels and operated with and without diesel particle filters. This study was part of a multi-year technology validation program designed to evaluate the emissions impact of ultra-low sulfur diesel fuels and passive diesel particle filters (DPF) in several different heavy-duty vehicle fleets operating in Southern California. The overall study of exhaust chemical composition included organic compounds, inorganic ions, individual elements, and particulate matter in various size-cuts. Detailed descriptions of the overall technology validation program and chemical speciation methodology have been provided in previous SAE publications (2002-01-0432 and 2002-01-0433).

Linear engine alternator (LEA) design optimization traditionally has been difficult because each independent variable alters the motion with respect to time, and therefore alters the engine and alternator response to other governing variables. An analogy is drawn to a conventional engine with a very light flywheel, where the rotational speed effectively is not constant. However, when springs are used in conjunction with an LEA, the motion becomes more consistent and more sinusoidal with increasing spring stiffness. This avoids some attractive features, such as variable compression ratio HCCI operation, but aids in reducing cycle-to-cycle variation for conventional combustion modes. To understand the cycle-to-cycle variations, we have developed a comprehensive model of an LEA with a 1kW target power in MATLAB®/Simulink, and an LEA corresponding to that model has been operated in the laboratory.

Techniques have been developed to sample and speciate dilute heavy duty diesel exhaust to determine the specific reactivities and the ozone forming potential. While the Auto/Oil Air Quality Improvement Research Program (AQIRP) has conducted a comprehensive investigation to develop data on potential improvements in vehicle emissions and air quality from reformulated gasoline and various other alternative fuels. However, the development of sampling protocols and speciation of heavy duty diesel exhaust is still in its infancy [1, 2, 3, 4, 5 and 6]. This paper focuses on the first phase of the heavy duty diesel speciation program, that involves the development of a unique set of sampling protocols for the gas phase, semi-volatile and particulate matter from the exhaust of engines operating on different types of diesel fuel. Effects of sampling trains, sampling temperatures, semi-volatile adsorbents and driving cycles are being investigated.

Results of a research effort directed towards identifying and measuring the genotoxic properties of respirable particulate matter involved in mining exposures, especially those which may synergistically affect genotoxic hazard, are presented. Particulate matter emissions from a direct injection diesel engine have been sampled and assayed to determine the genotoxic potential as a function of engine operating conditions. Diesel exhaust from a Caterpillar 3304 diesel engine, representative of the ones found in underground mines, rated 100 hp at 2200 rpm is diluted in a multi-tube mini-dilution tunnel and the particulate matter is collected on 70 mm fluorocarbon coated glass fiber filters as well as on 8″ x 10″ hi-volume filters. A six mode steady state duty cycle was used to relate engine operating conditions to the genotoxic potential.

The goal of the Diesel Emissions Control-Sulfur Effects (DECSE) program was to determine the impact of diesel fuel sulfur levels on emissions control devices that could lower emissions of oxides of nitrogen (NOX) and particulate matter (PM) from on-highway trucks and buses. West Virginia University (WVU) performed evaluations of lean-NOx catalysts to determine the effects of fuel sulfur content on emissions reduction efficiency and catalyst durability in the first 250 hours of operation. A Cummins ISM370 engine (10.8 liter, 370 horsepower), typical of heavy -duty truck applications, was utilized to evaluate high-temperature lean-NOX catalyst while a Navistar T444E (7.3 liter, 210 horsepower), typical of medium-duty applications, was used to evaluate low-temperature catalyst. Catalysts were evaluated periodically during the first 250 hours of exposure to exhaust from engines operated on 3ppm, 30ppm, 150ppm and 350ppm sulfur content diesel fuel.

Relationships between diesel particulate matter (PM) mass and gaseous emissions mass produced by engines have been explored to determine whether any gaseous species may be used as surrogates to infer PM quantitatively. It was recognized that sulfur content of fuel might independently influence PM mass, since PM historically is composed of elemental carbon, organic carbon, sulfuric acid, ash and wear particles. Previous research has suggested that PM may be correlated with carbon monoxide (CO) for an engine that is exercised through a variety of speed and load cycles, but that the correlation does not extend to a group of engines. Large databases from the E-55/59 and Gasoline/Diesel PM Split programs were employed, along with the IBIS bus emissions database and several additional data sets for on- and off-road engines to examine possible relationships.

Heavy duty diesel vehicle (HDDV) emissions are known to affect air quality, but few studies have quantified the real-world contribution to the inventory. The objective of this study was to provide data that may enable ambient emissions investigators to m,odel the air quality more accurately. The 25 vehicles reported in this paper are from the first phase of a program to determine representative regulated emissions from Heavy Heavy-Duty Diesel Trucks (HHDDT) operating in Southern California. Emissions data were gathered using a chassis dynamometer, full flow dilution tunnel, and research grade analyzers. The subject program employed two truck test weights and four new test modes (one was idle operation), in addition to the Urban Dynamometer Driving Schedule (UDDS), and the AC50/80 cycle. The reason for such a broad test cycle scope was to determine thoroughly how HHDDT emissions are influenced by operating cycle to improve accuracy of models.

Heavy-Duty Diesel (HDD) engines' particulate matter (PM) emissions are most often measured quantitatively by weighing filters that collect diluted exhaust samples pre- and post-test. PM sampling systems that dilute exhaust gas and collect PM samples have different effects on measured PM data. Those effects usually contribute to inter-laboratory variance. The U.S. Environmental Protection Agency (EPA)'s 2007 PM emission measurement regulations for the test of HDD engines should reduce variability, but must also cope with PM mass that is an order of magnitude lower than legacy engine testing. To support the design of a 2007 US standard HDD PM emission sampling system, a parametric study based on a systematic Simulink® model was performed. This model acted as an auxiliary design tool when setting up a new 2007 HDD PM emission sampling system in a heavy-duty test cell at West Virginia University (WVU). It was also designed to provide assistance in post-test data processing.

Alternative compression ignition engine fuels are of interest both to reduce emissions and to reduce U.S. petroleum fuel demand. A Malaysian Fischer-Tropsch gas-to-liquid fuel was compared with California #2 diesel by characterizing emissions from over the road Class 8 tractors with Caterpillar 3176 engines, using a chassis dynamometer and full scale dilution tunnel. The 5-Mile route was employed as the test schedule, with a test weight of 42,000 lb. Levels of oxides of nitrogen (NOx) were reduced by an average of 12% and particulate matter (PM) by 25% for the Fischer-Tropsch fuel over the California diesel fuel. Another distillate fuel produced catalytically from Fischer-Tropsch products originally derived from natural gas by Mossgas was also compared with 49-state #2 diesel by characterizing emissions from Detroit Diesel 6V-92 powered transit buses, three of them equipped with catalytic converters and rebuilt engines, and three without.

Dual primary full-flow dilution tunnels represent an integral part of a heavy-duty transportable emissions measurement laboratory designed and constructed to comply with US Code of Federal Regulations (CFR) 40 Part 1065 requirements. Few data exist to characterize the evolution of particulate matter (PM) in full scale dilution tunnels, particularly at very low PM mass levels. Size distributions of ultra-fine particles in diesel exhaust from a naturally aspirated, 2.4 liter, 40 kW ISUZU C240 diesel engine equipped with a diesel particulate filter (DPF) were studied in one set of standard primary and secondary dilution tunnels with varied dilution ratios. Particle size distribution data, during steady-state engine operation, were collected using a Cambustion DMS500 Fast Particulate Spectrometer. Measurements were made at four positions that spanned the tunnel cross section after the mixing orifice plate for the primary dilution tunnel and at the outlet of the secondary dilution tunnel.

When heavy-duty truck emissions rates are expressed in distance-specific units (such as g/mile), average speed and the degree of transient behavior of the vehicle activity can affect the emissions rate. Previous one-dimensional studies have shown some correlation of distance-specific emissions rates between cycles. This paper reviews emissions data sets from the 5-mode CARB Heavy Heavy-Duty Diesel Truck (HHDDT) Schedule, the Heavy Duty Urban Dynamometer Driving Schedule (UDDS) and an inspection and maintenance cycle, known as the AC5080. A heavy-duty chassis dynamometer was used for emissions characterization along with a full-scale dilution tunnel. The vehicle test weights were simulated at 56,000 lbs. Two-dimensional correlations were used to predict the emissions rate on one mode or cycle from the rates of two other modes or cycles.

The recent oil crisis has once again emphasized the need to develop both fuel efficient engines and alternately fueled engines, particularly for automotive applications. Engines which burn coal or coal pyrolysis products are attractive, but ignition delay and metal erosion problems continue to limit high speed operation of such engines. Further, the throttled spark ignition engine often used with methanol and natural gas does not prove an efficient or tolerant device for the combustion of a wide range of fuel. Therefore, an novel approach must be taken in order to achieve the efficient and flexible operation of such an engine. A novel design of a fuel tolerant engine suitable for burning coal fuels separates the combustion from the piston in order to have more careful flame control and to exclude the particulate matter from the engine's piston rings.

The emissions reduction benefits of Fischer-Tropsch (FT) diesel fuel have been shown in several recent published studies in both engine testing and in-use vehicle testing. FT diesel fuel shows significant advantages in reducing regulated engine emissions over conventional diesel fuel primarily to: its zero sulfur specification, implying reduced particulate matter (PM) emissions, its relatively lower aromaticity, and its relatively high cetane rating. However, the actual effect of FT diesel formulation on the in-cylinder combustion characteristics of unmodified modern heavy-duty diesel engines is not well documented. As a result, a Navistar T444E (V8, 7.3 liter) engine, instrumented for in-cylinder pressure measurement, was installed on an engine dynamometer and subjected to steady-state emissions measurement using both conventional Federal low sulfur pump diesel and a natural gas-derived FT fuel.

Biodiesel fuels benefit both from being a renewable energy source and from decreasing in carbon monoxide (CO), total hydrocarbons (THC), and particulate matter (PM) emissions relative to petroleum diesel. The oxides of nitrogen (NOx) emissions from biodiesel blended fuels reported in the literature vary relative to baseline diesel NOx, with no NOx change or a NOx decrease found by some to an increase in NOx found by others. To explore differences in NOx, two Cummins ISM engines (1999 and 2004) were operated on 20% biodiesel blends during the heavy-duty transient FTP cycle and the steady state Supplemental Emissions Test. For the 2004 Cummins ISM engine, in-cylinder pressure data were collected during the steady state and transient tests. Three types of biodiesel fuels were used in the blends: soy, tallow (animal fat), and cottonseed. The FTP integrated emissions of the B20 blends produced a 20-35% reduction in PM and no change or up to a 4.3% increase in NOx over the neat diesel.

Conventional crank-based engines are limited by mechanical, thermal, and combustion inefficiencies. The free piston of a linear engine generator reduces frictional losses by avoiding the rotational motion and crankshaft linkages. Instead, electrical power is generated by the oscillation of a translator through a linear stator. Because the free piston is not geometrically constrained, dead center positions are not specifically known. This results in a struggle against adverse events like misfire, stall, over-fueling, or rapid load changes. It is the belief that incorporating springs will have the dual benefit of increasing frequency and providing a restoring force to aid in greater cycle to cycle stability. For dual free piston linear engines the addition of springs has not been fully explored, despite growing interest and literature.