Extended Model for Filtration in Gasoline Particulate Filters under Practical Driving Conditions

Area of Science:

• Automotive Engineering
• Environmental Science
• Materials Science

Background:

• Gasoline particulate filters (GPFs) are crucial for reducing particulate matter emissions from gasoline direct injection engines.
• Existing filtration models often lack accuracy under high space velocity conditions.
• Experimental data on fresh filter efficiency, especially at high space velocities, is limited.

Purpose of the Study:

• To develop an extended filtration model for predicting gasoline particulate filter (GPF) performance under practical driving conditions.
• To experimentally determine particle-size-resolved fresh filtration efficiency for various cordierite filters.
• To investigate the impact of filter properties (pore-size distribution, wall thickness) on filtration efficiency.

Main Methods:

• Development of an extended filtration model incorporating a new inertial deposition correlation.
• Experimental determination of fresh filtration efficiency using a dynamic engine test bench.
• Analysis of steady-state and transient cold-start conditions.
• Heterogeneous multiscale modeling framework for GPF.

Main Results:

• The developed model accurately predicts observed filtration efficiency trends, including stabilization at high space velocities.
• Experimental data revealed the influence of pore-size distribution and wall thickness on filtration.
• The model demonstrates distinct behaviors compared to established models at varying space velocities.

Conclusions:

• The extended multiscale model provides reliable predictions for GPF particle number filtration.
• The study addresses a gap in experimental data for high space velocity conditions.
• The findings contribute to optimizing GPF design and performance for emission control.