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Research Papers: Flows in Complex Systems

Assessment of Drag Models for Geldart A Particles in Bubbling Fluidized Beds

[+] Author and Article Information
Bahareh Estejab

Department of Mechanical Engineering (MC 0238),
Virginia Polytechnic Institute
and State University,
Goodwin Hall, Room 210,
635 Prices Fork Road (0238),
Blacksburg, VA 24061
e-mail: bestejab@vt.edu

Francine Battaglia

Fellow ASME
Department of Mechanical Engineering (MC 0238),
Virginia Polytechnic Institute
and State University,
Goodwin Hall, Room 227,
635 Prices Fork Road (0238),
Blacksburg, VA 24061
e-mail: fbattaglia@vt.edu

1Corresponding author.

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received April 7, 2015; final manuscript received August 19, 2015; published online October 8, 2015. Assoc. Editor: John Abraham.

J. Fluids Eng 138(3), 031105 (Oct 08, 2015) (12 pages) Paper No: FE-15-1245; doi: 10.1115/1.4031490 History: Received April 07, 2015; Revised August 19, 2015

In order to accurately predict the hydrodynamic behavior of gas and solid phases using an Eulerian–Eulerian approach, it is crucial to use appropriate drag models to capture the correct physics. In this study, the performance of seven drag models for fluidization of Geldart A particles of coal, poplar wood, and their mixtures was assessed. In spite of the previous findings that bode badly for using predominately Geldart B drag models for fine particles, the results of our study revealed that if static regions of mass in the fluidized beds are considered, these drag models work well with Geldart A particles. It was found that drag models derived from empirical relationships adopt better with Geldart A particles compared to drag models that were numerically developed. Overall, the Huilin–Gidaspow drag model showed the best performance for both single solid phases and binary mixtures, however, for binary mixtures, Wen–Yu model predictions were also accurate.

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Figures

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Fig. 1

The two-dimensional plane representing the chamber of the cylindrical reactor

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Fig. 2

Bed height of (a) poplar wood and (b) 70:30 coal–poplar wood mass ratio comparing predictions for adjusted and unadjusted initial conditions to experiments. For both simulations, the Gidaspow drag model is used.

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Fig. 3

Bed height of coal for different drag models compared to experiments

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Fig. 4

Bed height of poplar wood for different drag models compared to experiments

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Fig. 5

Void fraction profiles for coal at h/h0 = 1 and Ug = 9.87 cm/s

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Fig. 6

Void fraction profiles for poplar wood at h/h0 = 1 and Ug = 9.87 cm/s

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Fig. 7

Bed height of 90:10 coal–poplar wood mass ratio for different drag models compared to experiments

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Fig. 8

Bed height of 80:20 coal–poplar wood mass ratio for different drag models compared to experiments

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Fig. 9

Bed height of 70:30 coal–poplar wood mass ratio for different drag models compared to experiments

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Fig. 10

Void fraction profiles for 90:10 coal–poplar wood mass ratio at h/h0 = 1 and Ug = 9.87 cm/s

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Fig. 11

Void fraction profiles for 80:20 coal–poplar wood mass ratio at h/h0 = 1 and Ug = 9.87 cm/s

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Fig. 12

Void fraction profiles for 70:30 coal–poplar wood mass ratio at h/h0 = 1 and Ug = 9.87 cm/s

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Fig. 13

Instantaneous void fraction for 70:30 coal–poplar wood mass ratio from 25 to 29 s (Δt = 1 s) at Ug = 9.87 cm/s using (a) HKL and (b) Gidaspow-blend drag models

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Fig. 14

Time-average coal and poplar wood velocity vectors along with velocity magnitude contours for Ug = 9.87 cm/s for 70:30 mass ratio of coal–poplar wood using (a) HKL and (b) Gidaspow-blend drag models

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Fig. 15

Instantaneous coal vertical velocity for 70:30 coal–poplar wood mass ratio from 25 to 29 s (Δt = 1 s) at Ug = 9.87 cm/s using (a) HKL and (b) Gidaspow-blend drag models

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Fig. 16

Instantaneous poplar wood vertical velocity for 70:30 coal–poplar wood mass ratio from 25 to 29 s (Δt = 1 s) at Ug = 9.87 cm/s using (a) HKL and (b) Gidaspow-blend drag models

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