Discrete particle modeling of granular temperature distribution in a bubbling fluidized bed

He, Yurong; Wang, Tianyu; Deen, NG Niels; Annaland, Martin van Sint; Kuipers, J.A.M.; Wen, Dongsheng

doi:10.1016/j.partic.2012.02.001

Cited by 48 publications

(12 citation statements)

References 26 publications

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“…With a rapid development of computer technology and new mathematical models, numerical simulation is becoming an indispensable tool of investigating the complex behavior of the gas–solid two‐phase flow . The discrete element method (DEM) is widely used among researchers in numerical investigations.…”

Section: Introductionmentioning

confidence: 99%

Experimental and numerical study on a bubbling fluidized bed with wet particles

Wang

Tang

et al. 2016

AIChE Journal

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As liquid bridge between particles acts an important role in the particle system, it is of considerable significance to analyze the flow hydrodynamics of wet particles in fluidized beds, which will improve the reactor design and process optimization. Thus, experimental and numerical investigations on wet particles in a bubbling fluidized bed are conducted in current work. On experimental side, particle image velocimetry (PIV) technology is employed with a designed bubbling fluidized bed. The silicone oil is used in this work because it is nonvolatile and transparent. On numerical side, a modified discrete element method (DEM) numerical method is developed by compositing an additional liquid‐bridge module into the traditional soft‐sphere interaction model. Most of the physical parameters are chosen to correspond to the experimental settings. Good agreements of particle velocity are found between the DEM simulation and PIV measurement. The performance of different liquid contents and superficial gas velocities are examined. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1970–1985, 2016

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Section: Introductionmentioning

confidence: 99%

Experimental and numerical study on a bubbling fluidized bed with wet particles

Wang

Tang

et al. 2016

AIChE Journal

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“…It has proven to be a successful tool for studying conventional fluidized beds in literature (He et al, 2012;Luo et al, 2014;van Wachem et al, 2001;Yang et al, 2014;Zhong et al, 2006). For simulating nanoparticle fluidization, as the particle element we use fragments of the large, complex agglomerates present in the bed: the so-called simple agglomerates.…”

Section: Introductionmentioning

confidence: 99%

An adhesive CFD‐DEM model for simulating nanoparticle agglomerate fluidization

et al. 2016

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Nanoparticle fluidization is an efficient technique to disperse and process nanoparticles. Previous studies show that nanoparticles are not fluidized individually, but as agglomerates with hierarchical fractal structures. In this study, an adhesive CFD-DEM (Computational Fluid Dynamics -Discrete Element Modelling) model is developed, in which we use the simple agglomerate as the discrete element, which are the building blocks of the larger complex agglomerates found in a fluidized bed. We show that both the particle contact model and drag force interaction in the conventional CFD-DEM model need modification for properly simulating fluidization of nanoparticle agglomerates. The contact model includes collision mechanisms of elastic-plastic, cohesive and viscoelastic forces, and the drag force is approximately corrected by a scale factor resulting from particle agglomeration. The model is tested for different cases, including the normal impact, response of angle, and fluidization. The simulation results are promising. With increasing particle cohesive force, the fluidized bed goes from a uniform fluid-like regime, to an agglomerate bubbling regime, and finally to defluidization. The current study provides a tool for gaining insights into the characteristics of nanoparticle fluidization.

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“…Cody, Goldfarb, Storch, and Norris (1996), Wang and Ge (2006), and Wang, van der Hoef, and Kuipers (2010) investigated the particle granular temperature in dense gas-fluidized beds and bubbling fluidized beds. He et al (2012) and Godlieb, Deen, and Kuipers (2008) used the DPM model to obtain the particle granular temperature distribution. Lu and Hsiau Nomenclature C s Smagorinsky constant d p particle diameter (m) e n normal coefficient of restitution g gravity (m/s 2 ) I a moment of inertia (kg m 2 ) m a particle mass (kg) m the number of frames that are averaged in time n particle number in a unit volume N p number of all the particles P pressure (Pa) r particle position (m) r a particle position vector (m) Re…”

Section: Introductionmentioning

confidence: 99%