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Relationship among Various Particle Characterization Metrics Using GDI Engine Based Light-Duty Vehicles
Technical Paper
2018-01-0353
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
In recent years, gasoline direct injection (GDI) engines have been widely used by manufacturers in light-duty to meet stringent fuel economy and emissions standards. This study focuses on the relationship between various particle metrics such as number, size, surface area and mass of dilute exhaust particles from 12 different light-duty vehicles equipped with GDI engines. The campaign included the measurement of total particulate matter (PM) using Title 40 CFR Part 1066 compliant filter measurement, soot mass using photo-acoustics based analyzer, organic carbon (OC) & elemental carbon (EC) mass using thermo-optical analysis of quartz filter samples, solid particle number using European Union Regulation No. 49 compliant number system and solid particle size/number using an electrical mobility based size spectrometer. The measurement campaign involved testing each vehicle over 16 Unified Driving Cycles, also called LA-92 drive cycles which is a more aggressive drive cycle than the Federal Test Procedure (FTP-75) drive cycle. We examined relationships between 1) the three different mass based measurement techniques, 2) the two different number based measurement techniques, 3) mass and number measurements, 4) derived mass (from measured size distribution) and measured mass. Such information can be invaluable to researchers studying the effect of combustion generated particles on human health. Currently, PM2.5 mass is the widely used metric to assess health impact due to the absence of sufficient data for other metrics. Development of a database of relationships between different metrics for a widely used engine platform such as the GDI becomes critically important to stakeholders.
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Premnath, V., Khalek, I., and Morgan, P., "Relationship among Various Particle Characterization Metrics Using GDI Engine Based Light-Duty Vehicles," SAE Technical Paper 2018-01-0353, 2018, https://doi.org/10.4271/2018-01-0353.Data Sets - Support Documents
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