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Throat Unit Collector Modeling of Gasoline Particulate Filter Performance

Published July 26, 2019 by SAE International in United States
Throat Unit Collector Modeling of Gasoline Particulate Filter Performance
Citation: Yang, P., Pate, M., and Strzelec, A., "Throat Unit Collector Modeling of Gasoline Particulate Filter Performance," SAE Int. J. Engines 12(4):417-426, 2019,
Language: English


The wide application of Gasoline Direct Injection (GDI) engines and the increasingly stringent Particulate Matter (PM) and Particulate Number (PN) regulations make Gasoline Particulate Filters (GPFs) with high filtration efficiency and low pressure drop highly desirable. However, due to the specifics of GDI operation and GDI PM, the design of these filters is even more challenging as compared to their diesel counterparts. Computational Fluid Dynamics (CFD) studies have been shown to be an effective way to investigate filter performance. In particular, our previous two-dimensional (2D) CFD study explicated the pore size and pore-size distribution effects on GPF filtration efficiency and pressure drop. The “throat unit collector” model developed in this study furthers this work in order to characterize the GPF wall microstructure more precisely. Throat unit collectors with different diameter ratios were created and simulated in ANSYS FLUENT to calculate the size-dependent particle filtration efficiency. The simulation results indicated a nonlinear change of single-collector efficiency, as the efficiency first decreased and then increased with a decreasing throat unit collector diameter ratio. The simulation results also showed the total wall filtration efficiency increased as the throat unit collector diameter ratio decreased. The throat unit collector model was also used to simulate the wall filtration efficiency change during particulates loading. The decrease of pore size in throat unit collector was used to mimic the pore-bridging process during loading. This study showed that the throat unit collector model is able to predict the GPF filtration performance at initial or loading state by only requiring the principle properties of the GPF (mean pore size, porosity, wall thickness) without model tuning.