Characterization of pressure drop in structured packed beds with a layer stacking model

Packed beds are widely encountered in many energy and chemical process industries, where pressure drop directly impacts pumping power and operational efficiency. In recent years, structured packed beds have emerged as a promising option for reducing flow resistance and improving overall performance. This study employs computational fluid dynamics method to investigate the pressure drop characteristics of structured packed beds with cubic, rhombohedral, and orthorhombic configurations. In all three series, the pressure drop increases with superficial velocity, while the friction factor generally decreases with the modified Reynolds number. The effect of porosity on both quantities is rather complicated: the maximum values usually occur at the minimum porosity, whereas their minimum values are observed at intermediate porosity rather than at the maximum porosity. In the cubic and rhombohedral series, both pressure drop and friction factor decrease with increasing spacing between adjacent particles. In the orthorhombic series, however, the pressure drop and friction factor initially decline, then rise, and finally decline again as the spacing progressively increases. At a given porosity, the rhombohedral series exhibits the lowest flow resistance, followed by the orthorhombic series, with the cubic series having the highest. A comparison between the simulation results and classical empirical correlations indicates that existing porosity‑based models are unsuitable for structured packed beds, with average errors exceeding 20 % for each series. By analogy with flow resistance in sudden contraction‑expansion sections, a new correlation based on the layer stacking model is proposed. The correlation achieves better prediction accuracy than traditional models, with average errors of 11.68 % for the cubic series, 10.17 % for the rhombohedral series, and 4.18 % for the orthorhombic series. The results and models presented in this study are helpful for the optimal design of packed bed reactors.