GPF Modeling and Multi-Objective Optimization Investigation

Pengze  Yang, Texas A&M University

Gasoline Direct Injection (GDI) engines are known to produce lower concentrations of smaller diameter PM as compared to Conventional Diesel Combustion (CDC). Whereas Diesel Particulate Filters (DPFs) rely on the soot cake to increase the efficiency of filtration, the smaller and fewer particulates in GDI exhaust translate to an absence of soot cake formation on the filter channel wall which makes DPFs ineffective for GDI engines. In addition, GDI engines are known to be more sensitive to back pressure, so simply reducing the porosity of DPFs would likely lead to reduced engine efficiency. Therefore, GPFs with high filtration efficiency and low back pressure penalty are extremely desirable.

The goal of this computational project is to investigate approaches to increase the filtration efficiency of GPFs while keeping the back pressure penalty at a minimum. We have developed a model that is capable of predicting the filtration behavior and pressure drop of clean GPFs in ANSYS Workbench and laminar flow model is used in FLUENT to obtain the velocity and pressure field in the GPF. A user defined function is implemented to simulate the filtration behavior of particles through the porous wall, tuned by experimental data. We compared model predictions to the results of clean, uncoated GPF filtration experiments and then investigated the effect of modifying the geometry  to increase the filtration efficiency while lower the pressure drop.

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