Numerical Model of Filtration Efficiency Based on Fractal Characteristics of Particulate Matter and Particle Filter

Abstract

Fractal theory was used to characterize particles and particle trapping parameters to accurately predict the particle filtration process inside a gasoline engine particle filter (GPF). The particles were fractal aggregates, and the fractal dimension (Df) was introduced to redefine the particle size. The porous medium inside the particle filter was a solid phase fractal. The pore tortuosity fractal dimension (Dt) and the pore area fractal dimension (Da) were introduced to define the fiber length of the trap. The Brownian diffusion coefficient and permeability were modified. A new fractal numerical model of GPF filtration efficiency was proposed based on the classical filtration theory. The results show that the fractal expansion model of filtration efficiency has good applicability. The influence of GPF structural parameters on filtration efficiency and pressure drop was analyzed. In this study, two performance metrics, trapping efficiency and pressure drop, were considered by fractal expansion filtration modeling. It is possible to increase or decrease filtration efficiency by adjusting the porosity and pore diameter.