Fluidized beds have widespread industrial applications ranging from chemical industries to power plants. The flow inside a fluidized bed system consists of two main phases, a particle phase and the fluid phase. The two phases are strongly coupled to each other through various forces like drag and pressure. Capturing this multiphase phenomenon requires modeling strategies that possess good fidelity over a range of scales. Discrete Element Modeling (DEM) coupled with Computational Fluid Dynamics (CFD) provides a good platform to analyze the complex coupled multiphase hydrodynamics inside fluidized bed systems. Conventional DEM-CFD framework suffers from contradictory spatial resolution requirements for the particle and fluid phases, respectively. This prevents the conventional DEM-CFD method to be applied to geometries that have features comparable to the particle diameter of the solid phase. The novelty of this work lies in the development and validation of a two-grid formulation that removes the resolution restrictions of the conventional DEM-CFD framework. The results obtained from this new framework agree reasonably well with the experiments showing the capability of the new scheme to simulate conditions not possible with conventional DEM-CFD framework. In addition, this research also focuses on performing both 2D and 3D jetting fluidized bed simulations having millions of particles; validate/compare results with experiments and to perform heat transfer studies in a jetting fluidized bed system. The results suggest convective and diffusive mixing for a single jet at higher superficial velocity to be better than the mixing obtained in a multiple jet framework. The comparison with experimental results obtained in a multiple jetting setup shows that a 2D simulation captures the essential jet characteristics near the distributor plate reasonably well while a 3D simulation is needed to capture proper bubble dynamics near the freeboard of the bed. These results give insight into the detailed dynamics of fluidized bed systems and provide a foundation for a better design of these systems. / Ph. D.