Special Section on 2018 Clean Energy

Numerical Approaches for Modeling Gas–Solid Fluidized Bed Reactors: Comparison of Models and Application to Different Technical Problems

[+] Author and Article Information
Peter Ostermeier

Institute for Energy Systems,
Technical University of Munich,
Boltzmannstr. 15,
Garching 85748, Germany
e-mail: peter.ostermeier@tum.de

Annelies Vandersickel, Stephan Gleis

Institute for Energy Systems,
Technical University of Munich,
Boltzmannstr. 15,
Garching 85748, Germany

Hartmut Spliethoff

Institute for Energy Systems,
Technical University of Munich,
Boltzmannstr. 15,
Garching 85748, Germany;
Bavarian Center for Applied Energy
Research (ZAE Bayern),
Walther-Meissner-Str. 6,
Garching 85748, Germany

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received June 14, 2018; final manuscript received March 15, 2019; published online April 19, 2019. Assoc. Editor: Ashwani K. Gupta.

J. Energy Resour. Technol 141(7), 070707 (Apr 19, 2019) (10 pages) Paper No: JERT-18-1433; doi: 10.1115/1.4043327 History: Received June 14, 2018; Revised March 15, 2019

Gas–solid fluidized bed reactors play an important role in many industrial applications. Nevertheless, there is a lack of knowledge of the processes occurring inside the bed, which impedes proper design and upscaling. In this work, numerical approaches in the Eulerian and the Lagrangian framework are compared and applied in order to investigate internal fluidized bed phenomena. The considered system uses steam/air/nitrogen as fluidization gas, entering the three-dimensional geometry through a Tuyere nozzle distributor, and calcium oxide/corundum/calcium carbonate as solid bed material. In the two-fluid model (TFM) and the multifluid model (MFM), both gas and powder are modeled as Eulerian phases. The size distribution of the particles is approximated by one or more granular phases with corresponding mean diameters and a sphericity factor accounting for their nonspherical shape. The solid–solid and fluid–solid interactions are considered by incorporating the kinetic theory of granular flow (KTGF) and a drag model, which is modified by the aforementioned sphericity factor. The dense discrete phase model (DDPM) can be interpreted as a hybrid model, where the interactions are also modeled using the KTGF; however, the particles are clustered to parcels and tracked in a Lagrangian way, resulting in a more accurate and computational affordable resolution of the size distribution. In the computational fluid dynamics–discrete element method (CFD–DEM) approach, particle collisions are calculated using the DEM. Thereby, more detailed interparticulate phenomena (e.g., cohesion) can be assessed. The three approaches (TFM, DDPM, CFD–DEM) are evaluated in terms of grid- and time-independency as well as computational demand. The TFM and CFD–DEM models show qualitative accordance and are therefore applied for further investigations. The MFM (as a variation of the TFM) is applied in order to simulate hydrodynamics and heat transfer to immersed objects in a small-scale experimental test rig because the MFM can handle the required small computational cells. Corundum is used as a nearly monodisperse powder, being more suitable for Eulerian models, and air is used as fluidization gas. Simulation results are compared to experimental data in order to validate the approach. The CFD–DEM model is applied in order to predict mixing behavior and cohesion effects of a polydisperse calcium carbonate powder in a larger scale energy storage reactor.

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Grahic Jump Location
Fig. 1

Geometry and exemplary computational mesh of the test rig in millimeter [17]

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Fig. 2

Time-averaged global bed height with extrema and pressure drop (left), solid volume fraction (middle), and axial particle velocity at a height of 200 mm (right) for the TFM grid and fluid time-step independency study [17]

Grahic Jump Location
Fig. 3

Time-averaged global bed height with extrema and pressure drop (left), solid volume fraction (middle), and axial particle velocity at a height of 200 mm (right) for the DDPM grid and time-step independency study [17]

Grahic Jump Location
Fig. 4

Time-averaged global bed height with extrema and pressure drop (left), solid volume fraction (middle), and axial particle velocity at a height of 200 mm (right) for the CFD–DEM grid, parcel, and particle time-step independency study [17]

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Fig. 5

Time-averaged global bed height with extrema and pressure drop (left), solid volume fraction (middle), and axial particle velocity at a height of 200 mm (right) for the CFD–DEM variation, including a comparison to the TFM

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Fig. 6

Time-averaged contours of MEAN (left) and RMSE (middle left) particle velocity in m/s with qualitative particle flow field (left and middle left), and contours of MEAN (middle right) and RMSE (right) solid volume fraction with qualitative gas phase flow field (middle right and right) for the TFM8 granular conductivity model (top) and the CFD–DEM 1 × 105 f Hertzian model (bottom) at the symmetry plane of the reactor geometry

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Fig. 7

Contours of instant (left) and time-averaged (right) solid volume fraction with time-averaged gas (left) and particle (right) flow fields for a superficial air velocity of 0.20 m/s [19]

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Fig. 8

Particle contact time at the tube surface at a height of 10 cm (left), comparison of numerical and experimental angle-dependent local heat transfer coefficient for one tube position and superficial air velocity (middle), and comparison of all numerical and experimental local heat transfer coefficient with 20% error interval (right) [20]

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Fig. 9

Particle size distribution of the investigated calcium carbonate (q3: measured frequency distribution, Q3: measured cumulative distribution, CFD1: numerical frequency distribution with particles larger than 27.5 μm, CFD2: numerical frequency distribution with particles larger than 97.5 μm)

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Fig. 10

Depiction of the considered cohesion regime

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Fig. 11

Transient segregation behavior of the particles in the reactor (filtered by parcels containing particles larger than 97.5 μm on the left and smaller than 97.5 μm on the right side of the reactor, CFD1)

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Fig. 12

(a) Fluidization behavior in the CFD2 model after 14 s for four different cohesion effects (left: parcels colored by particle size, right: contours of solid volume fraction), (b) bed height and pressure drop, (c) cross-sectional averaged solids volume fraction distribution, and (d) radial averaged axial particle velocity at a height of 200 mm



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