Effect of Mesoscopic Void Structure on Permeability Performance of Porous Asphalt Mixtures by Integration of Computational Fluid Dynamics and Discrete Element Method

Porous asphalt mixtures (PAC) with high connectivity porosity can significantly reduce surface runoff and play a more critical role in sustainable paving. This study investigated the permeability performance of PAC-13 porous asphalt mixtures with three target porosities and four design-density levels using computational fluid dynamics (CFD) and the discrete element method (DEM). Solid-volume-fraction mapping, pressure and velocity responses, and permeability coefficients were used to characterize the seepage behavior, while equivalent void diameter and fractal dimension were introduced to describe mesoscopic void features. The correlation between mesoscopic void structure and permeability was analyzed. Results showed that the internal void distribution of PAC-13 exhibited clear spatial heterogeneity, and the overall porosity alone could not fully characterize the pore structure. Increasing the target porosity from 20% to 22% reduced the average peak pressure by approximately 23.5%. The average permeability coefficient increases with the porosities from 18%, 20%, and 22%, with the highest value of 0.00393 m/s obtained at 22% target porosity and a design-density level of 0.90. Correlation analysis showed that the permeability coefficient was positively correlated with target porosity, equivalent void diameter, and fractal dimension. A regression model incorporating these macro–mesoscopic parameters achieved an (R2) of 0.91. These findings indicate that PAC-13 drainage performance should be evaluated by combining overall porosity with mesoscopic void-structure parameters. The findings rely exclusively on computer simulation, and further experimental tests should be conducted.