Multi-scale non-uniform hierarchical filtering model based on fractal theory

Area of Science:

• Environmental Science
• Mechanical Engineering
• Chemical Engineering

Background:

• Gasoline particulate filters (GPFs) are crucial for reducing vehicle emissions.
• Filter trapping efficiency is influenced by various operational factors.
• Accurate modeling is needed to understand dynamic trapping performance.

Purpose of the Study:

• To develop and validate a multi-scale non-uniform hierarchical filtering (MNHF) model for GPFs.
• To analyze the dynamic changes in trapping efficiency during GPF operation.
• To investigate the impact of particle size and filter structure on trapping performance.

Main Methods:

• Fractal theory applied to model multi-scale filter wall characteristics.
• Filter theory and Brownian kinematics used for particle motion prediction.
• Numerical simulations compared with experimental data for validation.

Main Results:

• The MNHF model demonstrated a maximum relative error of 0.7% compared to experimental data.
• The model accurately predicts dynamic trapping performance over time.
• Trapping efficiency decreases in lower filter layers (up to 29.34% reduction).
• Enhanced simulation accuracy for 0.01 μm to 0.5 μm particles at low flow velocities.

Conclusions:

• The MNHF model provides a highly accurate tool for analyzing GPF trapping efficiency.
• Understanding particle dynamics and multi-scale filter properties is key to optimizing GPF performance.
• The model aids in designing more effective gasoline particulate filters for emission control.