Research Projects

The Discrete Element Method (DEM) is a useful tool for simulating rheological phenomena in granular materials, and when coupled with computational fluid dynamics, it can predict features of fluid particle systems like fluidized beds. Population balance modeling (PBM) is the most convenient tool for tracking particle size distribution due to processes such as nucleation, growth, breakage, and aggregation. However, incorporating these processes in DEM or CFD-DEM is cumbersome and computationally expensive. PBM's accuracy depends on the accuracy of aggregation or breakage kernels, which are difficult to derive from first principles and are obtained empirically for most cases. The interest lies in coupling PBM with DEM or CFD-DEM to produce kernels with computational efficiency. DEM or CFD-DEM simulations are periodically executed to extract desired information for modeling kernels. The dimension of the PBM model depends on the number of essential properties influencing particle formation mechanisms. However, DEM simulations are slow and coarsegrained particles are often used to finish the simulation in reasonable time. This work aims to develop a consistent coarsegraining method that can generate reliable kernels for PBM, ensuring the efficiency and accuracy of the CFD-DEM-PBM coupled simulation scheme. The DEM framework will be used to estimate particle movement and identify zones for a compartmental model for PBM.