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Research Papers: Multiphase Flows

How Computational Grid Refinement in Three Dimensions Affects Computational Fluid Dynamics-Discrete Element Method Results for Psuedo-Two-Dimensional Fluidized Gas–Solid Beds

[+] Author and Article Information
Annette Volk

CFD Research Laboratory,
University of Cincinnati,
Cincinnati, OH 45221
e-mail: volkam@mail.uc.edu

Urmila Ghia

Fellow ASME
CFD Research Laboratory,
University of Cincinnati,
Cincinnati, OH 45221
e-mail: ghiau@ucmail.uc.edu

Milind A. Jog

Fellow ASME
Thermal-Fluids and Thermal Processing Lab,
University of Cincinnati,
Cincinnati, OH 45221
e-mail: Milind.Jog@uc.edu

1Corresponding author.

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received October 26, 2017; final manuscript received June 28, 2018; published online August 6, 2018. Assoc. Editor: Kausik Sarkar.

J. Fluids Eng 140(12), 121303 (Aug 06, 2018) (9 pages) Paper No: FE-17-1693; doi: 10.1115/1.4040763 History: Received October 26, 2017; Revised June 28, 2018

Computational fluid dynamics (CFD)-discrete element method (DEM) simulations are designed to model a pseudo-two-dimensional (2D) fluidized bed, in which bed thickness is minimal compared to height and length. Predicted bed behavior varies as the simulations are conducted on increasingly refined computational grids. Pseudo-2D simulation results, in which a single computational cell spans the bed thickness, are compared against fully-three-dimensional (3D) simulations results. Both pseudo-2D and fully-3D simulations exhibit high accuracy when sufficiently refined. Indicators of bed behavior, such as bed height, bed height fluctuation, bubble generation frequency, and segregation, do not appear to converge as the cell size is reduced. The Koch-Hill and Gidaspow drag laws are alternately employed in the simulations, resulting in different trends of results with computational grid refinement. Grid refinement studies are used to quantify the change in results with grid refinement for both three-dimensional, uniform refinement, and for two-dimensional refinement on pseudo-2D computational grids. Grid refinement study results indicate the total drag converges as the computational grid is refined, for both 3D and pseudo-2D approaches. The grid refinement study results are also used to distinguish the relatively grid-independent results using the Koch-Hill drag law from the highly grid-dependent Gidaspow drag law results. Computational cell size has a significant impact on CFD-DEM results for fluidized beds, but the grid refinement study method can be used to quantify the resulting numerical error.

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Figures

Grahic Jump Location
Fig. 1

Computational domain with CFD boundary conditions

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Fig. 2

Predicted bed center of mass height generally increases with grid refinement

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Fig. 3

Root-mean-square of bed height varies significantly with grid refinement

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Fig. 4

Segregation degree generally increases with decreasing cell size; dashed lines represent spread of reported experimental values [14]

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Fig. 5

Normalized RMS for large particle species with computational cell size; dashed lines represent spread of reported experimental values [14]

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Fig. 6

Normalized RMS for small particle species with computational cell size; dashed lines represent spread of reported experimental values [14]

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Fig. 7

Average number of bubbles present in the bed increases with grid refinement

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Fig. 8

Average bubble diameter increases with grid refinement, with several exceptions

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Fig. 9

Simulations using the Gidaspow drag law are unable to capture the segregation phenomena on refined computational grids, dashed lines represent spread of reported experimental values [14]

Grahic Jump Location
Fig. 10

Grid refinement study is performed for the average total drag force for 3D and pseudo-2D refinement methods

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