Rotor-stator mixers have a broad spectrum of applications in chemical, petrochemical and pharmaceutical processes since they produce the high shear fields for emulsification and dispersion processes. To assess device performance and quantify mixing and dispersion capabilities, analyzing the velocity field data due to the rotor-stator interactions is crucial. Experimental 2-D velocity data have previously been acquired using Particle Image Velocity (PIV) for an in-line IKA prototype mixer which contains single rows of 12 rotor teeth and 14 stator teeth. The working fluid was water in turbulent flow. In this thesis, the development and validation of a Computational Fluid Dynamics (CFD) model is reported along with the comparison between the CFD and PIV data. The CFD model geometry and mesh were developed within ANSYS Workbench with a fully transient sliding mesh 3-D RANS simulations performed with Fluent using the realizable k-ϵ turbulence model. To begin, the effect of mesh density and wall treatment were systematically tested to optimize the CFD simulation settings. With respect to post processing, the numerical data were sampled in a stator slot at 9 rotor tooth positions on a grid that closely mimicked that for PIV data acquisition. The comparisons were made for three different rotor speeds (10, 20, and 26 revolutions per second) but at the same volumetric throughput (1.3 liters per second).

The study of near-wall modelling options considered Non-Equilibrium Wall Functions (NEWF) and Enhanced Wall Treatment (EWT). Both produced similar results but EWT showed advantage in computational efficiency. In the mesh independence study, 3 mesh levels were created with approximately 2, 6, and 16 million cells. The study revealed that the mesh level with 6 million cells was sufficient to insure grid independence at reasonable accuracy. The CFD and PIV data compared favorably in many aspects. On average, CFD predicted the location of mixing layer and rotor tip vortices within 6.0% of the stator slot width compared to the PIV data. CFD also successfully identified 23 out of 27 (85.1%) mixing layer and rotor tip vortices captured by PIV. Differences were observed as well. The CFD simulations consistently yielded higher