Flow through randomly packed beds of rough particles: Model prediction and experimental validation

This study investigates the influence of particle roughness on pressure drop in randomly packed beds through experiments and theoretical modeling using the Quinn fluid flow model (QFFM). While classical frameworks like the Ergun and Kozeny–Carman equations assume smooth particles, real-world applications often involve rough or irregularly shaped particles, leading to significant deviations in predicted pressure drops. The QFFM, which integrates Nikuradse's roughness effects into a modified Ergun equation, is presented as a universal predictor for both packed and empty conduits across all flow regimes (creeping to turbulent). Experiments were conducted on packed beds with spherical and cylindrical particles, with surface roughness quantified via profilometry; additionally, experimental data for irregular particles (clinker) from the literature were used for validation. The QFFM solver accurately back-calculated key parameters (e.g., wall normalization coefficient and porosity) and demonstrated that roughness reduces dimensionless permeability, increasing pressure drop. Results show excellent agreement between measured and predicted data, with mean absolute errors below 1% for smooth particles. A dimensionless analysis reveals distinct scaling for rough vs smooth systems, bridging Nikuradse's pipe-flow findings to closed conduits. The QFFM's ability to simultaneously resolve design and operational parameters underscores its utility for industrial applications involving textured or irregular particles.