AAAR 31st Annual Conference
October 8-12, 2012
Hyatt Regency Minneapolis
Minneapolis, Minnesota, USA
Abstract View
Light-Duty Diesel Engine Exhaust Particle Number Distribution Differences between Petro-Diesel and Different Blends of Soy Biodiesel Fuels
TYLER FERALIO, Britt Holmén, Jim Dunshee, University of Vermont
Abstract Number: 140 Working Group: Combustion
Abstract Recent energy and climate policies, such as EISA, encourage the production and use of biofuels. As a result, there has been an increase in the use of biodiesel in the transportation sector despite limited understanding of its health and environmental effects. In urban areas, transportation sources are one of the main contributors to particulate air pollution. This shift in fuel use, therefore, affects the particle number size distribution and particle composition found in the air we breathe. The aim of this study is to quantify the differences in emissions from these fuels in order to increase understanding of the potential health effects. To that end, preliminary ‘engine-out’ emissions data were collected from a Volkswagen 1.9 Liter SDi naturally aspirated light-duty diesel engine running on different blends of soy-based biodiesel. The data were collected while the dynamometer was loading the engine to maintain 2000 RPM at 45 percent throttle (approximately 40 percent load at this RPM). Soy biodiesel fuel blends were B0, B20, B50, and B100. Tailpipe exhaust was diluted using a mini-diluter system with a dilution ratio of approximately 56 and 1Hz particle distributions (32 channels, 5.6 - 560nm) were measured with a TSI 3090 EEPS.
Results are similar to trends found in the literature. The particle number distribution of petro-diesel was unimodal (modal diameter 52.3nm) with an average peak concentration of 2.7416e7 particles/cc. As the concentration of biodiesel was increased, the PN distribution developed additional particle diameter modes. The B100 distribution showed three distinct modes centered at 10.8, 17.8, and 31.7nm with peak concentrations averaging 1.3829e8, 1.2726e8, and 1.3381e8 particles/cc, respectively.
These preliminary data will be used to build an artificial neural network model to predict PN emissions as a function of engine operating parameters and fuel type using standard OBD-II data and measured fuel properties.