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The occurrence of cavitation inside injectors is generally undesirable since it can cause material erosion and result in deviations from the expected injector performance. Previous numerical work employing an injector geometry measured with x-ray diagnostics and operating with a high-volatility straight-run gasoline (SRG) has shown that: (1) most of the cavitation is generally observed at low needle lifts, (2) needle motion is responsible for asymmetric structures in the internal flow as well as large pressure and velocity gradients that trigger phase transition at the orifice inlets, and (3) cavitation affects the injector discharge coefficient and distribution of injected fuel. To explore the potential for material damage within the injector orifices due to cavitation cloud collapse, the cavitation-induced erosion risk assessment (CIERA) tool has been applied for the first time to the realistic geometry of a heavy-duty injector using the CONVERGE software.

Experimental data and modeling work have shown that gasoline-like fuels can potentially be used to simultaneously achieve high efficiency and low pollutant emissions in compression ignition engines. Demonstrating that existing hardware systems are tolerant to these fuels is a key step in harnessing this potential. In this study, a 400-hour North Atlantic Treaty Organization (NATO) test cycle was used to assess the overall robustness of a Cummins XPI common-rail injection system operating with gasoline-like fuel. The cycle was designed to accelerate wear and identify any significant failure modes that could appear under normal operating conditions. Although prior work has investigated injection system durability with a wide variety of alternative fuels, this study uniquely focuses on a high-volatility, low-viscosity, gasoline-like fuel that has been dosed with lubricity additive.

After the realization of very low exhaust gas emissions and corresponding OBD requirements to fulfill Euro VI and Tier 4 legislation, the focus in heavy-duty powertrain development is on the reduction of fuel consumption and thus CO₂ emissions again. Besides this, the total vehicle operation costs play another major role. A holistic view of the overall powertrain system including the combustion process, exhaust gas aftertreatment, energy recuperation and energy storage is necessary in order to obtain the best possible system for a given application. A management system coordinating the energy flow between the different subsystems while guaranteeing low exhaust emissions plays a major part in operating such complex architectures under optimal conditions.

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).

West Virginia University has conducted research to characterize the emissions from medium-duty vehicles operating on Fischer-Tropsch synthetic gas-to-liquid compression ignition fuel. The West Virginia University Transportable Heavy Vehicle Emissions Testing Laboratory was used to collect data for gaseous emissions (carbon dioxide, carbon monoxide, oxides of nitrogen, and total hydrocarbon) while the vehicles were exercised through a representative driving schedule, the New York City Bus Cycle (NYCB). Artificial neural networks were used to model emissions to enhance the capabilities of computer-based vehicle operation simulators. This modeling process is presented in this paper. Vehicle velocity, acceleration, torque at rear axel, and exhaust temperature were used as inputs to the neural networks. For each of the four gaseous emissions considered, one set of training data and one set of validating data were used, both based on the New York City Bus Cycle.

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.

Although diesel engines still power most of the heavy-duty transit buses in the United States, many major cities are also operating fleets where a significant percentage of buses is powered by lean-burn natural gas engines. Emissions from these buses are often expressed in distance-specific units of grams per mile (g/mile) or grams per kilometer (g/km), but the driving cycle or route employed during emissions measurement has a strong influence on the reported results. A driving cycle that demands less energy per unit distance than others results in higher fuel economy and lower distance-specific oxides of nitrogen emissions. In addition to energy per unit distance, the degree to which the driving cycle is transient in nature can also affect emissions.

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.

When heavy-duty truck emissions are expressed in distance-specific units (such as g/mile), the values may depend strongly on the nature of the test cycle or schedule. Prior studies have compared emissions gained using different schedules and have proposed techniques for translating emissions factor rates between schedules. This paper reviews emissions data from the 5-mode CARB HHDDT Schedule, UDDS Schedule, and a steady-state cycle (AC5080), with reference to each other. NOX and PM emissions are the two components of emissions which are reviewed. A heavy-duty chassis dynamometer was used for emissions characterization along with a full scale dilution tunnel. The vehicle test weights were simulated at 30,000 lbs, 56,000 lbs, and 66,000 lbs. For each vehicle, average data from one mode or cycle have been compared with average data for a different mode or cycle.

The California Air Resources Board (ARB) developed a Medium Heavy-Duty Truck (MHDT) schedule by selecting and joining microtrips from real-world MHDT. The MHDT consisted of three modes; namely, a Lower Speed Transient, a Higher Speed Transient, and a Cruise mode. The maximum speeds of these modes were 28.9, 58.2 and 66.0 mph, respectively. Each mode represented statistically selected truck behavior patterns in California. The MHDT is intended to be applied to emissions characterization of trucks (14,001 to 33,000lb gross vehicle weight) exercised on a chassis dynamometer. This paper presents the creation of the MHDT and an examination of repeatability of emissions data from MHDT driven through this schedule. Two trucks were procured to acquire data using the MHDT schedule. The first, a GMC truck with an 8.2-liter Isuzu engine and a standard transmission, was tested at laden weight (90% GVW, 17,550lb) and at unladen weight (50% GVW, 9,750lb).

Few real-world data exist to describe the contribution of diesel vehicles to the emissions inventory, although it is widely acknowledged that diesel vehicles are a significant contributor to oxides of nitrogen (NOx) and particulate matter (PM) in Southern California. New data were acquired during the Gasoline/Diesel PM Split Study, designed to collect emissions data for source profiling of PM emissions from diesel- and gasoline-powered engines in the South Coast (Los Angeles) Air Basin in 2001. Regulated gases, PM and carbon dioxide (CO2) were measured from 34 diesel vehicles operating in the Southern California area. Two were transit buses, 16 were trucks over 33,000 lbs. in weight, 8 were 14,001 lbs. to 33,000 lbs. in weight and 8 were under 14,001 lbs. in weight. The vehicles were also grouped by model year for recruiting and data analysis.

Emissions tests for heavy -duty diesel-fueled engines and vehicles are normally performed using engine dynamometers and chassis dynamometers, respectively, with laboratory grade gaseous concentration measurement analyzers and supporting test equipment. However, a considerable effort has been recently expended on developing in-use, on-board tools to measure brake-specific emissions from heavy -duty vehicles with the highest degree of accuracy and precision. This alternative testing methodology would supplement the emissions data that is collected from engine and chassis dynamometer tests. The on-board emissions testing methodology entails actively recording emissions and vehicle operating parameters (engine speed and load, vehicle speed etc.) from vehicles while they are operating on the road. This paper focuses on in-use measurements of NOX with zirconium oxide sensors and other portable NOX detectors.

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.

West Virginia University evaluated diesel oxidation catalysts (DOC) and lean-NOX catalysts as part of Diesel Emissions Control-Sulfur Effects (DECSE) project. In order to perform thermal aging of the DOC and lean-NOX catalysts simultaneously and economically, each catalyst was sized to accommodate half of the engine exhaust flow. Simultaneous catalyst aging was then achieved by splitting the engine exhaust into two streams such that approximately half of the total exhaust flowed through the DOC and half through the lean-NOX catalyst. This necessitated splitting the engine exhaust into two streams during emissions measurements. Throttling valves installed in each branch of the split exhaust were adjusted so that approximately half the engine exhaust passed though the active catalyst under evaluation and into a full flow dilution tunnel for emissions measurement.

Hybrid diesel electric buses offer the advantage of superior fuel economy through use of regenerative braking and lowered transient emissions by reducing the need of the engine to follow load as closely as in a conventional bus. With the support of the Department of Energy (DOE), five Lockheed Martin-Orion hybrid diesel-electric buses were operated on the West Virginia University Transportable Laboratory in Brooklyn, New York. The buses were exercised through a new cycle, termed the Manhattan cycle, that was representative of today's bus use as well as the accepted Central Business District Cycle and New York Bus Cycle. Emissions data were corrected for the state of charge of the batteries. The emissions can be expressed in units of grams/mile, grams/axle hp-hr and grams/gallon fuel. The role of improved fuel economy in reducing oxides of nitrogen relative to conventional automatic buses is evident in the data.

Significant numbers of transit buses now operate on natural gas. With support of the U.S. Department of Energy, the National Renewable Energy Laboratory has evaluated the cost, performance, and emissions of alternative fuel buses around the country. In this study, three natural gas and three closely matched diesel buses were compared. The buses, built by World Trans, were 26′5″long and used 1997 Cummins B-series engines. Particulate matter and oxides of nitrogen emissions from the natural gas buses were significantly lower than those from the diesel buses. However, the diesel buses had lower operating costs and higher fuel efficiency than the natural gas buses.

Emissions from heavy-duty vehicles may be reduced through the introduction of clean diesel formulations, and through the use of catalyzed particulate matter filters that can enjoy increased longevity and performance if ultra-low sulfur diesel is used. Twenty over-the-road tractors with Detroit Diesel Series 60 engines were selected for this study. Five trucks were operated on California (CA) specification diesel (CARB), five were operated on ARCO (now BP Amoco) EC diesel (ECD), five were operated on ARCO ECD with a Johnson-Matthey Continuously Regenerating Technology (CRT) filter and five were operated on ARCO ECD with an Engelhard Diesel Particulate Filter (DPX). The truck emissions were characterized using a transportable chassis dynamometer, full-scale dilution tunnel, research grade gas analyzers and filters for particulate matter (PM) mass collection. Two test schedules, the 5 mile route and the city-suburban (heavy vehicle) route (CSR), were employed.

Emissions from trucks and buses equipped with Caterpillar dual-fuel natural gas (DFNG) engines were measured at two chassis dynamometer facilities: the West Virginia University (WVU) Transportable Emissions Laboratory and the Los Angeles Metropolitan Transportation Authority (LA MTA). Emissions were measured over four different driving cycles. The average emissions from the trucks and buses using DFNG engines operating in dual-fuel mode showed the same trends in all tests - reduced oxides of nitrogen (NOx) and particulate matter (PM) emissions and increased hydrocarbon and carbon monoxide (CO) emissions - when compared to similar diesel trucks and buses. The extent of NOx reduction was dependent on the type of test cycle used.

Dual fuel engines employing pilot diesel injection to ignite premixed natural gas provide an opportunity for liquid petroleum fuel replacement and for reduced emissions of oxides of nitrogen (NOx) and particulate matter (PM). A Navistar T444E turbocharged V8 engine was converted to operate in dual fuel mode by metering the compressed natural gas (CNG) with an IMPCO Technologies, Inc. regulator and electronic valve while retaining the stock Navistar Hydraulically-Actuated Electronically-Controlled Unit Injection (HEUI) system for diesel pilot injection. A dedicated controller was designed and constructed to allow manual control of diesel fuel injection pulsewidth (FIPW), diesel injection advance (ADV), hydraulic injection control pressure (ICP) and natural gas mass flow. The controller employed two Microchip, Inc. PIC-based microcontrollers: one to perform initialization of a Silicon Systems, Inc. 67F867 engine interface peripheral, and the other to perform the runtime algorithms.

This paper explores the feasibility of using in-cylinder pressure-based variables to predict gaseous exhaust emissions levels from a Navistar T444 direct injection diesel engine through the use of neural networks. The networks were trained using in-cylinder pressure derived variables generated at steady state conditions over a wide speed and load test matrix. The networks were then validated on previously “unseen” real-time data obtained from the Federal Test Procedure cycle through the use of a high speed digital signal processor data acquisition system. Once fully trained, the DSP-based system developed in this work allows the real-time prediction of NOX and CO2 emissions from this engine on a cycle-by-cycle basis without requiring emissions measurement.