A Multi-Scale Hybrid CFD-DEM-PBM Description of a Fluid-Bed Granulation Process
<p>Schematic of the coupled multi-scale framework.</p> "> Figure 2
<p>Model Geometry and mesh of the FBG.</p> "> Figure 3
<p>Velocity contour plots. (<b>a</b>) Velocity distribution over 0 s–0.5 s; (<b>b</b>) Velocity distribution over 0.5 s–1.0 s; (<b>c</b>) Velocity distribution over 1.0 s–1.5 s; (<b>d</b>) Velocity distribution over 1.5 s–2.0 s.</p> "> Figure 4
<p>Plots for particle liquid content distribution. (<b>a</b>) Liquid content over 0 s–0.5 s; (<b>b</b>) Liquid content over 1.5 s–2.0 s.</p> "> Figure 5
<p>EDEM<math display="inline"> <msup> <mrow/> <mi>TM</mi> </msup> </math> snapshots of liquid content. (<b>a</b>) Particle liquid content at time = 1 s; (<b>b</b>) Particle liquid content at time = 2 s.</p> "> Figure 6
<p>Liquid content <span class="html-italic">vs.</span> time.</p> "> Figure 7
<p>EDEM<math display="inline"> <msup> <mrow/> <mi>TM</mi> </msup> </math> snapshots of particle diameter. (<b>a</b>) Particle diameter at time = 1 s; (<b>b</b>) Particle diameter at time = 2 s.</p> "> Figure 8
<p>Average diameter <span class="html-italic">vs.</span> time.</p> "> Figure 9
<p>PSD at different time points.</p> "> Figure 10
<p>PSDs for different PBM time step.</p> "> Figure 11
<p>PSD used in DEM simulations to determine collision rates.</p> "> Figure 12
<p>Collision frequency versus particle size for each distribution based on DEM simulations. (<b>a</b>) D1; (<b>b</b>) D2; (<b>c</b>) D3; (<b>d</b>) D4.</p> ">
Abstract
:1. Introduction, Motivation and Objectives
1.1. Objectives
- Present a hybrid CFD-DEM-PBM framework using dynamic two-way coupling.
- Incorporate multi-scale information such that the model can be used to study the detailed process dynamics.
- Study the heterogenous particle velocity distribution and liquid binder distribution.
- Study the evolution of average particle diameter and particle liquid content with time.
2. Background
3. Multi-Scale Model Development
3.1. CFD Model for the Fluidizing Medium
- Flow near wall is laminar and the velocity varies linearly with the distance from wall.
- A no slip boundary condition has been set at the wall.
- A velocity inlet boundary condition has been used for the air entering the geometry.
- An outlet-vent boundary condition has been used at the geometry exit.
3.2. Discrete Element Model
Particle properties | |
Shear modulus | 1 × 10 Nm |
Poisson’s ratio | |
Density | 1030 kgm |
Particle-particle interactions | |
Coefficient of restitution | |
Coefficient of static friction | |
Coefficient of rolling friction | |
Granulator walls | |
Material | Steel |
Shear modulus | 7.6 × 10 Nm |
Poisson’s ratio | |
Density | 7800 kgm |
Particle-wall interactions | |
Coefficient of restitution | |
Coefficient of static friction | |
Coefficient of rolling friction |
3.3. Population Balance Model for FBG
3.4. Information Exchange in the Coupling Framework
- The PBM considers aggregation only, breakage and consolidation has not been incorporated since FBG processes are low shear processes with reduced consolidation and breakage (similar approach has been followed by [48]).
- A simple aggregation kernel has been formulated based on collision frequency and collision efficiency (adapted from [46]).
- The collision efficiency in the aggregation kernel is size independent, non-mechanistic and conditional based on the liquid content of the powder particles (adapted from [47]).
- Liquid addition has been captured in EDEM by creating particles which get deleted from the system upon contact.
- A reasonable number of collisions occur among the particles between any two subsequent time steps.
- The PBM is solved a reasonable number of times such that there is a more consistent distribution of the particle size (as described in Section 4.3)
3.5. Model Outputs
4. Results and Discussion
4.1. Simulation Procedure
- The geometry has been made using ANSYS Design Modeler.
- The geometry has been meshed using ICEM-CFD.
- The mesh file has been imported within FLUENT.
- The mesh has been converted into Polyhedra domain.
- The gravity is defined in the correct direction and a transient simulation is selected.
- The flow model has been selected to be viscous laminar.
- The coupling server has been started.
- The FLUENT is coupled with EDEM for the desired fluid domain by selecting the Eulerian-Eulerian option.
- The coupling server will automatically import the geometry with the specified direction of gravity in EDEM and set the source terms in x-momentum, y-momentum and z-momentum calculation. The value of the simulation parameters of the coupling interface has been set as follows:
- Sample points: The number of points used by FLUENT to calculate the volume fraction of the fluid cell. This value has been set at 10, which means that a large particle can transfer its volume between 10 cells. This particular parameter decides the stability and speed of the simulation. A higher value of sample point may increase the stability but decrease the simulation speed.
- Relaxation factor: The relaxation factors again help with stability and convergence of the solution. Reducing the value helps to increase stability and achieve convergence. Both momentum-MTM-under-relaxation factor and volume under-relaxation factor have been set at 0.7.
- The inlet fluid velocity has been defined as 30 m/s.
- The custom contact model and custom factory (for PBM calculation) have been imported within EDEM.
- The material properties, particle-particle and particle-wall interaction parameters as given in Table 1 have been set in EDEM.
- The initial PSD has been created in EDEM.
- The liquid particles have been created in EDEM (the liquid addition starts at 0.2 s).
- Once the EDEM simulation is set up, initialize the solution in FLUENT.
- Run the calculation.
4.2. Model Geometry
4.3. Multi-Scale Model Results
5. Conclusions
Acknowledgments
Conflicts of Interest
Appendix. Effect of PSD on Collision Frequency
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Sen, M.; Barrasso, D.; Singh, R.; Ramachandran, R. A Multi-Scale Hybrid CFD-DEM-PBM Description of a Fluid-Bed Granulation Process. Processes 2014, 2, 89-111. https://doi.org/10.3390/pr2010089
Sen M, Barrasso D, Singh R, Ramachandran R. A Multi-Scale Hybrid CFD-DEM-PBM Description of a Fluid-Bed Granulation Process. Processes. 2014; 2(1):89-111. https://doi.org/10.3390/pr2010089
Chicago/Turabian StyleSen, Maitraye, Dana Barrasso, Ravendra Singh, and Rohit Ramachandran. 2014. "A Multi-Scale Hybrid CFD-DEM-PBM Description of a Fluid-Bed Granulation Process" Processes 2, no. 1: 89-111. https://doi.org/10.3390/pr2010089
APA StyleSen, M., Barrasso, D., Singh, R., & Ramachandran, R. (2014). A Multi-Scale Hybrid CFD-DEM-PBM Description of a Fluid-Bed Granulation Process. Processes, 2(1), 89-111. https://doi.org/10.3390/pr2010089