CFD modeling of droplet permeability in fluidized beds. (July 2022) - Balice, D.M.; Molenaar, C.W.C.; Fochesato, M.; Venier, C.M.; Roghair, I.; Deen, N.G.; van Sint Annaland, M.
Abstract: The deposition of droplets of size [5–22] μ m on the surface of particles in a gas–solid fluidized bed has been investigated employing a one-way coupled CFD-DEM modeling. In this approach the gas phase is solved as a continuum and the free-droplets are solved as Lagrangian objects, while solid particles are assumed to be stationary and act as an obstacle for the fluid flow. In this way, it was possible to calculate the deposition factor, defined as the ratio of droplets deposited on the surface of the particles to the number of droplets injected into the domain as a function of the droplet Stokes number and particle Reynolds number. An empirical correlation was developed that describes the deposition factor as a function of the operating conditions, which is valid for the inertial regime where effects of Brownian motion can be neglected. The final goal of this investigation is to employ the developed correlation in a larger scale CFD-DEM model describing a polymerization fluidized bed reactor operated in condensed mode, i.e. by injecting liquid into the gas–solid suspension through the gas distributor plate or via nozzles in order to improve the heat management. In this kind of modeling the free-droplets phase is described as a continuum and the deposition rate of droplets determines the amount of liquid on the particle surface, which affects for the hydrodynamic (i.e. formation of liquid bridges between particles) and thermal behavior (i.e. liquid evaporation, kinetic and subsequent cooling effect) of the system. Highlights: CFD-DEM simulation of liquid droplets permeating a particle array was performed. Gas flow between the particles was simulated on Eulerian mesh. Droplet deposition factor on the particles was analyzed and a correlation was proposed. Deposition factor dependence on the Stokes number and porosity has been quantified. Deposition probability can be scaled to particle arrays of arbitrary length.