Packed bed unit operations are required for many commercial chemical processes. The ability to a priori predict void fraction and pressure drop in a packed bed would significantly improve reactor design as well as allow for optimization around catalyst performance, catalyst design, and the resulting process pressure drop. Traditionally, the packed bed reactor designs are based on a homogeneous model with averaged empirical correlations. These correlations are often inapplicable for low tube-to-particle diameter ratios (D/d < 4) in which tube wall and local phenomena dominate. In this work, the discrete element method (DEM) and computational fluid dynamics (CFD) are coupled to model a fixed bed reactor with low tube-to-particle diameter ratios (D/d < 4). DEM is used to generate a realistic random packing structure for the packed bed with spherical or cylindrical particles, which is then imported into the CFD, preprocessor (Gambit) to generate the mesh for the CFD simulation. Two types of experiments were conducted: the laboratory-scale experiments with up to similar to 150 particles to allow simulation of entire packed beds in CFD, including random packing and structured packing, and the plant-scale experiments conducted with up to similar to 1500 randomly packed particles. The concept of a "porosity correction factor" was introduced to compensate for the effect of porosity deviation between actual packing and the CFD model, which can be accumulated during DEM simulation, and particle shrinkage for purposes of grid generation in the CFD model. The predicted pressure drops match well with the experimental measurements with errors less than the desired limit (10%) for industrial design of packed bed reactors. The pressure drops calculated by the empirical correlations confirmed the inconsistency and unreliability of the empirical correlations for the packed beds with low tube-to-particle diameter ratios (D/d < 4) as well as the advantage of the DEM/CFD approach.