Simulating wet gas–solid fluidized beds using coarse-grid CFD-DEM

Validating a Numerical Simulation of the ConsiGma(R) Coater

Continuous manufacturing is increasingly used in the pharmaceutical industry, as it promises to deliver better product quality while simultaneously increasing production flexibility. GEA developed a semi-continuous tablet coater which can be integrated into a continuous tableting line, accelerating the switch from traditional batch production to the continuous mode of operation. The latter offers certain advantages over batch production, e.g., operational flexibility, increased process/product quality, and decreased cost. However, process understanding is the key element for process control. In this regard, computational tools can improve the fundamental understanding and process performance, especially those related to new processes, such as continuous tablet coating where process mechanics remain unclear. The discrete element method (DEM) and computational fluid dynamics (CFD) are two methods that allow transition from empirical process design to a mechanistic understanding of the individual process units. The developed coupling model allows to track the heat, mass, and momentum exchange between the tablet and fluid phase. The goal of this work was to develop and validate a high-fidelity CFD-DEM simulation model of the tablet coating process in the GEA ConsiGma® coater. After the model development, simulation results for the tablet movement, coating quality, and heat and mass transfer during the coating process were validated and compared to the experimental outcomes. The experimental and simulation results agreed well on all accounts measured, indicating that the model can be used in further studies to investigate the operating space of the continuous tablet coating process.

A Dynamic Coarse Grain Discrete Element Method for Gas-Solid Fluidized Beds by Considering Particle-Group Crushing and Polymerization

The discrete element method (DEM) coupled with computational fluid dynamics (CFD) is used extensively for the numerical simulation of gas-solid fluidized beds. In order to improve the efficiency of this approach, a coarse grain model of the DEM was proposed in the literature. In this model, a group of original particles are treated as a large-sized particle based on the initial particle distribution, and during the whole simulation process the number and components of these particle-groups remain unchanged. However, collisions between particles can lead to frequent crushing and polymerization of particle-groups. This fact has typically been ignored, so the purpose of this paper is to rationalize the coarse grain DEM-CFD model by considering the dynamic particle-group crushing and polymerization. In particular, the effective size of each particle-group is measured by a quantity called equivalent particle-group diameter, whose definition references the equivalent cluster diameter used by the energy-minimization multi-scale (EMMS) model. Then a particle-group crushing criterion is presented based on the mismatch between the equivalent diameter and actual diameter of a particle-group. As to the polymerization of two colliding particle-groups, their velocity difference after collision is chosen as a criterion. Moreover, considering the flow heterogeneity induced by the particle cluster formation, the EMMS drag force model is adopted in this work. Simulations are carried out by using a finite volume method (FVM) with non-staggered grids. For decoupling the Navier-Stokes equations, the semi-implicit method for pressure linked equations revised (SIMPLER) algorithm is used. The simulation results show that the proposed dynamic coarse grain DEM-CFD method has better performance than the original one.

Toward Constitutive Models for Momentum, Species, and Energy Transport in Gas-Particle Flows

As multiscale structures are inherent in multiphase flows, constitutive models employed in conjunction with transport equations for momentum, species, and energy are scale dependent. We suggest that this scale dependency can be better quantified through deep learning techniques and formulation of transport equations for additional quantities such as drift velocity and analogies for species, energy, and momentum transfer. How one should incorporate interparticle forces, which arise through van der Waals interaction, dynamic liquid bridges between wet particles, and tribocharging, in multiscale models warrants further study. Development of multiscale models that account for all the known interactions would improve confidence in the use of simulations to explore design options, decrease the number of pilot-scale experiments, and accelerate commercialization of new technologies.