Modular particle-resolved simulation of trickle-bed hydrodynamics with validation

Trickle-bed reactors are widely used for three-phase catalytic processes, yet their performance remains strongly governed by complex hydrodynamic phenomena such as partial wetting, rivulet formation, and liquid maldistribution. Predicting these effects in realistic packed beds remains challenging due to the computational cost of continuum-based simulations and the limited generality of empirical correlations. In this work, a modular and Lagrangian modelling framework is applied to investigate hydrodynamics in trickle-bed reactors operating in the trickle-flow regime. The novelty of the framework lies in its scalable Lagrangian rivulet-network formulation, which directly links particle-scale packing morphology with local liquid pathways, catalyst wetting, liquid holdup and bed-scale maldistribution without requiring full continuum-based multiphase CFD. The framework combines realistic packing generation with a rivulet-based description of liquid flow, enabling explicit representation of capillarity-driven liquid pathways without resorting to full computational fluid dynamics. The model is validated against independent experimental datasets: spatially resolved liquid saturation measurements obtained from wire-mesh sensors reported in the literature and in-house measurements of static holdup, dynamic holdup, and liquid maldistribution. Static holdup is predicted accurately (0.060) without parameter tuning, closely matching the experimental value of 0.064±0.002. Dynamic holdup and total liquid saturation increase with liquid flowrate, and the model reproduces these trends quantitatively for superficial liquid velocities of 3.54 and 7.07 cm s⁻¹. Predicted saturation values differ from experimental measurements by only a few percent across the packed bed. The framework also captures the weak dependence of saturation and maldistribution on gas flowrate, consistent with the low Reynolds numbers (approx. 6–13) and Weber numbers (<10⁻³) characterising the investigated trickle-flow regime. Analysis of the predicted liquid distribution reveals an increase in rivulet number from approximately 5.6 × 10³ to 6.5 × 10³ as liquid flowrate increases, accompanied by a corresponding rise in wetted area and improved radial uniformity. Outlet maldistribution factors are reproduced within approximately 5% of experimental values, matching the experimental repeatability of the measurements. These results demonstrate that the modular Lagrangian framework captures the dominant geometric and interfacial mechanisms governing trickle-bed hydrodynamics while maintaining computational efficiency for large packed beds.