(381e) Modified Ergun Equation for the Pressure Drop Prediction of Packed Beds with Size-Bidisperse and Polydisperse Particles

We study the gas pressure drop in gas-solid packed beds with size-bidisperse and -polydisperse spherical particles using experimental and experimentally validated coupled CFD-DEM numerical methods. For bidisperse systems, the existence of small particles can increase the overall packing density (compared to monodisperse systems), and in turn affect the length scale to characterize the flow behavior in packed beds. Consequently, the original Ergun equation using the Sauter mean diameter as the particle size input can underestimate pressure drop for all considered conditions up to 30% when compared with experimental results. Therefore, we propose a modified form of Ergun equation, where we still use the Sauter mean diameter () in the inertial term, but use (the ratio of second moment (area weighed) to first moment (number weighed) of particle diameters) in the viscous term. Because considers both particle volume (body force) and surface area (friction), it better represents more dynamic fluid-particle interactions in the inertial flow regime. In contrast, because only considers particle surface area, it better represents the friction effect, which dominates in the viscous regime. This modified model is also consistent with the original Ergun equation in the monodisperse spherical particle situation, where =. The modified equation yields substantial improvement for the pressure drop prediction. The deviation of the modified Ergun equation is generally less than 3% compared to CFD-DEM and experimental results, a significant improvement from 30% difference when is used in both inertial and viscous terms. We also validated the proposed equation for several examples of polydisperse particle mixtures. Future work needs to focus on non-spherical particle systems to further validate the modified Ergun equation proposed in this work to provide more accurate predictions for various practical applications.