Numerical analysis and experimental validation of hydrodynamics of a thin bubbling fluidized bed for various particle-size distributions using a three-dimensional dense discrete phase model--颗粒学报PARTICUOLOGY

Hashemisohi, A., Wang, L., Shahbazi, A., & Amini, H. (2020). Numerical analysis and experimental validation of hydrodynamics of a thin bubbling fluidized bed for various particle-size distributions using a three-dimensional dense discrete phase model. Particuology, 49, 191-204. https://doi.org/10.1016/j.partic.2019.04.001

Numerical analysis and experimental validation of hydrodynamics of a thin bubbling fluidized bed for various particle-size distributions using a three-dimensional dense discrete phase model

Abolhasan Hashemisohi ^a, Lijun Wang ^{b c *}, Abolghasem Shahbazi ^{b c}, Hossein Amini ^a

a Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC 27411, USA
b Department of Natural Resources and Environmental Design, North Carolina A&T State University, Greensboro, NC 27411, USA
c Department of Chemical, Biological, and Bioengineering, North Carolina A&T State University, Greensboro, NC 27411, USA

10.1016/j.partic.2019.04.001

Volume 49, April 2020, Pages 191-204

Received 10 January 2018, Revised 18 April 2019, Accepted 29 April 2019, Available online 2 July 2019, Version of Record 26 February 2020.

E-mail: lwang@ncat.edu

Highlights

• One mesh cell in thin bed thickness in a DDPM model cannot simulate wall friction.

• Increasing wall friction shifts the circulation center toward the top of the bed.

• Changing specularity factor affects both particle velocity and flow pattern in bed.

• Mixtures with standard deviation to mean diameter of 10% showed segregation.

• Flat size distribution caused non-uniformity even for narrow diameter ranges.

Abstract

A dense discrete phase model combined with the kinetic theory of granular flows was used to study the bubbling characteristics and segregation of poly-dispersed particle mixtures in a thin fluidized bed. Our simulations showed that in using the hybrid Eulerian–Lagrangian method, the common use of one computational cell in the thickness direction of the thin bed does not predict wall friction correctly. Instead, a three-cell discretization of the thickness direction does predict the wall friction well but six cells were needed to prevent overprediction of the bed expansion. The change in specularity factor (SF) of the model not only affected the predictions of the velocity of particles, but also had a considerable impact on their flow pattern. A decrease in SF, which decreases wall friction, showed an over-prediction in the size of bubbles, particle velocities, and void fraction of the bed, and led to a shift in the circulation center toward the bottom of the bed. The segregation of the Geldart B particles was studied in the narrow range from 400 to 600 μm with a standard deviation less than 10% of the average diameter. Simulations showed that large particles accumulated close to the distributor at the bottom of the bed and the center of the bed, but small particles moved towards the wall and top surface. The decrease in the mean particle size and spread in shape of the distribution improves mixing by up to 30% at a superficial gas velocity of around 2.5 times the minimum fluidization velocity. Log-normal mixtures with a small proportion of large particles had the most uniform distribution with a thin layer of jetsam forming at the bottom of the bed. Finally, experimental verification of the segregation and mixing of polydisperse particles with narrow size distribution is suggested.

Graphical abstract

Keywords

Computational fluid dynamics; Fluidization; Particle size distribution; Hybrid Eulerian–Lagrangian model; Dense discrete phase model; Wall friction

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