Microstructure–flow characteristic relationships in packed beds of macroporous particles

The pressure‐drop characteristics of packed beds composed of macroporous particles were investigated using a mesoscale simulation framework combining discrete element method (DEM)‐based digital reconstruction of macroporous particles, DEM‐generated packed‐bed structures, and computational fluid dynamics (CFD). The effects of particle diameter, pore diameter, pore number, packing density, and Reynolds number on pressure drop were systematically analyzed. Pressure drop decreased with increasing particle diameter, pore diameter, and pore number, whereas it increased with packing density. A neural‐network surrogate model and Sobol sensitivity analysis revealed that particle diameter was the dominant parameter governing pressure drop. Based on the CFD database, a modified Ergun equation incorporating pore‐scale structural parameters was developed. The proposed correlation significantly improved prediction accuracy compared with conventional Ergun‐type models. These results provide a quantitative microstructure–fluid flow relationship for packed beds of macroporous particles and a practical framework for pressure‐drop prediction and design.