(559d) An Immersed Method to Predict the Pressure Drop Over a Dynamic Bed of Particles

The power input needed to perform particle-fluid separations is critical to many industries such as water treatment and energy production. This power input is dependent upon the pressure drop over the particle bed as the fluid moves through it. Traditionally, the Ergun equation, and variations of it, have been used to predict this pressure drop given a variety of parameters such as the particle shape, packing characteristics, and the fluid viscosity. Recently, efforts have been made to elucidate some of the important physics which dominate this process though numerical modeling using computational fluid dynamics (CFD).  Though some success has been obtained with this method, past implementations of it did not allow for the easy modeling of a dynamic bed of particles as the filter cake is being formed. In this work, an immersed method is explored in which the particulates are dynamically coupled to the CFD mesh. The model is used to predict the pressure drop over the particle bed and the results are compared to experiments.  Moreover, transient analysis of the flow characteristics is performed as the particles settle into a filter cake.