Traditional emissions inventories for trucks and buses have relied on diesel engine emissions certification data, in units of g/bhp-hr, processed to yield a value in g/mile without a detailed accounting of the vehicle activity. Research has revealed a variety of other options for inventory prediction, including the use of emissions factors based upon instantaneous engine power and instantaneous vehicle behavior. The objective of this paper is to provide tabular factors for use with vehicle activity information to describe the instantaneous emissions from each heavy-duty vehicle considered. To produce these tables, a large body of data was obtained from the research efforts of the West Virginia University-Transportable Heavy Duty Emissions Testing Laboratories (TransLabs). These data were available as continuous records of vehicle speed (hence also acceleration), vehicle power, and emissions of carbon monoxide (CO), oxides of nitrogen (NOx), and hydrocarbons (HC). Data for particulate matter (PM) were available only as a composite value for a whole vehicle test cycle, but using a best effort approach, the PM was distributed in time in proportion to the CO. Emissions values, in g/sec, were binned according to the speed and acceleration of a vehicle, and it was shown that the emissions could be predicted with reasonable accuracy by applying this table to the original speed and acceleration data. The test cycle used was found to have a significant effect on the emissions value predicted. Tables were created for vehicles grouped by type (large transit buses, small transit buses, and tractor-trailers) and by range of model year. These model year ranges were bounded by U.S. national changes in emissions standards. The result is that a suite of tables is available for application to emissions predictions for trucks and buses with known activity, or as modeled by TRANSIMS, a vehicle activity simulation model from Los Alamos National Laboratories.
Heavy-duty diesel vehicles are substantial contributors of oxides of nitrogen (NO(x)) and particulate matter (PM) while carbon monoxide and hydrocarbon (HC) emissions from diesel vehicles receive less attention. Truck emissions inventories have traditionally employed average fuel economy and engine efficiency factors to translate certification into distance-specific (g/mi) data, so that inventories do not take into account the real effects of truck operating weight on emissions. The objective of this research was to examine weight corrections for class 7 and 8 vehicles (over 26 000 lb (11 793 kg) gross vehicle weight) from a theoretical point of view and to present a collection of original data on the topic. It was found by combining an empirical equation with theoretical truck loads that the NO(x) emissions increased by approximately 54% for a doubling of test weight. Emissions data were gathered from specific tests performed using different test weights and using various test schedules, which can consist of cycles or routes. It was found experimentally that NO(x) emissions have a nearly linear correlation with vehicle weight and did not vary much from vehicle to vehicle. NO(x) emissions were also found to be insensitive to transient operation in the test schedule. The observed trends correlate well with the theory presented, and hence, the NO(x) emissions can be predicted reasonably accurately using the theory. If NO(x) data were considered in fuel-specific (g/gal) units, they did not vary with the test weight. HC emissions were found to be insensitive to the vehicle weight. CO and PM emissions were found to be a strong function of weight during transient operation. Under transient operation, the CO emissions value increased by 36% for an increase in test weight from 42 000 (19 051 kg) to 56 000 lb (25 401 kg). However, CO and PM were found to be insensitive to the vehicle weight during nearly steady-state operation.
Emissions from heavy-duty diesel vehicles are known to contribute a substantial fraction of the oxides of nitrogen (NO X), and particulate matter (PM) to the atmospheric inventory. Prediction of heavy-duty diesel vehicle emissions inventory is substantially less mature than the prediction of gasoline vehicle emissions. Heavy-duty truck emissions are affected by various parameters like vehicle weight/load, driving schedule used, and injection timing control strategies employed to operate the engine at more fuel-efficient (but higher NO X) mode. Research has revealed a variety of options for inventory prediction, including the use of emissions factors based upon instantaneous engine power and instantaneous vehicle behavior. Effects of various parameters on the heavy-duty diesel emissions were studied in great detail and a speed-acceleration based emissions prediction approach was developed for heavy-duty diesel vehicle emissions prediction. A suite of emissions factor tables was generated for emissions inventory prediction. Driving schedules, vehicle weight, and off-cycle injection strategy were found to affect emissions to varying extents. Detailed analyses of a large body of data enabled to quantitatively as well as qualitatively characterize effect of various parameters on heavy duty diesel vehicle emissions. A doubling of vehicle weight was found to result in roughly a 50% increase in NO X emissions. The accuracy was found to improve with the inclusion of a large number of data covering wide range of model year groups and driving schedules. Off-cycle operation was found to increase the NO X emissions by more than double. The speed-acceleration model predicted the emissions with reasonable accuracy. iv ACKNOWLEDGEMENTS Many people deserve a lot of thanks and credit for helping me complete this research and dissertation. First, I thank Nigel for giving me the opportunity to attend graduate school and for all the help and able guidance provided during the course of my stay here. He has always been an inspiration for me. I admire his sincerity and dedication in whatever he does. Next, I thank the rest of my committee, Mridul Gautam, Gregory Thompson, Scott Wayne and Shahab Mohaghegh for their help and the time they've devoted to this research.
As glass wafer products become more specialized and die sizes get smaller, the demand for high‐speed singulation of glass wafers is increasing. Corning Laser Technologies utilizes the advantages of precise laser dicing to process optimized glass types with adapted CTE values, high transmission, and specific refractive indices to enable existing and emerging applications.
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