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Research Papers: Multiphase Flows

Computational Fluid Dynamics Flow Simulations in Discrete Element Method-Resolved Packed Beds

[+] Author and Article Information
Joanna Zhang

Department of Mechanical and
Aerospace Engineering,
The University of Alabama in Huntsville,
301 Sparkman Drive,
Huntsville, AL 35899

Babak Shotorban

Department of Mechanical and Aerospace
Engineering,
The University of Alabama in Huntsville,
301 Sparkman Drive,
Huntsville, AL 35899

Sami Bayyuk

ESI US R&D, Inc.,
620 Discovery Drive NW,
Suite 120, Building 2,
Huntsville, AL 35806

Sijun Zhang

ESI US R&D, Inc.,
620 Discovery Drive NW,
Suite 120, Building 2,
Huntsville, AL 35806
e-mail: sijun.zhang@esi-group.com

1Present address: Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139.

2Corresponding author.

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received June 7, 2018; final manuscript received October 30, 2018; published online December 24, 2018. Assoc. Editor: Sergio Pirozzoli.

J. Fluids Eng 141(3), 031304 (Dec 24, 2018) (14 pages) Paper No: FE-18-1396; doi: 10.1115/1.4041986 History: Received June 07, 2018; Revised October 30, 2018

This paper presents computational simulations of flows in packed beds and compares the computational pressure-drop results with those given by the Ergun correlation. The computational methodology used in this work follows the combined discrete element method (DEM) and computational fluid dynamics (CFD) technique. DEM is used to predict the locations and packing structure of the particles in the bed, while CFD is used to predict the flow field in the void space surrounding the packed particles. The computational results obtained for irregular packed beds show that the local packing-structure parameters have significant effects not only on the local velocity and pressure fields but also on macroscopic quantities, such as the average pressure gradient along the length of the packed column. The computational results also show that classical correlations based on averaged values, such as the Ergun correlation, have poor predictive accuracy for macroscopic variations along a packed column, and this is mainly because such correlations do not account for local packing-structure parameters. The computational results confirm the existence of sections with linear variation of macroscopic parameters along the length of the packed column, and this leads to the conclusion that accurate results from DEM-CFD methods on shortened columns can be extrapolated to full-length columns. Moreover, it was found that unlike regularly packed beds, the predicted pressure for randomly packed beds experiences an apparent strong recovery near the downstream end of the packed bed, and then experiences a strong dip down to the plateau leading to the exit pressure.

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Figures

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Fig. 1

The geometry of a cylindrical bed with a flat bottom

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Fig. 2

Snapshots showing the formation of a packing of 0.01 m particles at different nondimensional times: (a) 0.0, (b) 25, (c) 50, (d) 75, (e) 100, (f) 125, (g) 150, (h) 175, (i) 200, (j) 225, (k) 250, (l) 275, (m) 300, (n) 325, (o) 350, (p) 375, (q) 400, (r) 425, (s) 450, (t) 475, and (u) 500

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Fig. 3

Special patterns of three structured packing beds: (a) bed 1, (b) bed 2, and (c) bed 3

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Fig. 4

Predicted pressure contours (Pa): (a) bed 1, (b) bed 2, and (c) bed 3

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Fig. 5

Predicted pressure distributions along the central line in the Z-axis

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Fig. 6

Predicted pressure contours (Pa) on the X–Z plane at Y = 0 for different numbers of meshes: (a) set 1, (b) set 2, (c) set 3, and (d) baseline

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Fig. 7

Predicted pressure contours (Pa) on the surfaces of particles for five different numbers of packed particles: (a) 153 particles, (b) 120 particles, (c) 90 particles, (d) 60 particles, and (e) 30 particles

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Fig. 8

Predicted pressure contours (Pa) on the X–Z plane at Y = 0 for five different numbers of packed particles: (a) 153 particles, (b) 120 particles, (c) 90 particles, (d) 60 particles, and (e) 30 particles

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Fig. 9

Predicted pressure distributions along the central line in the Z-axis for the five packed beds: (a) 153 particles, (b) 120 particles, (c) 90 particles, (d) 60 particles, and (e) 30 particles

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Fig. 10

Predicted pressure distributions along the central line in the Z-axis for the five packed beds

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Fig. 11

Predicted and correlated pressure drops and their differences with respect to the bed heights

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Fig. 12

Predicted and correlated pressure drops and bed heights with respect to the particle numbers

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Fig. 13

Predicted pressure drops and the curve fitted pressure drops from longer beds

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