Experimental analysis of bubble size distributions in 2D gas fluidized beds

Busciglio, Antonio; Vella, G.; Micale, G.; Rizzuti, L.

doi:10.1016/j.ces.2010.05.016

Cited by 32 publications

(18 citation statements)

References 17 publications

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“…The bubble grows with the height of the bed column due to the coalescence of bubbles. Bubble sizes level out by a balance between coalescence and breakup 1, 26. The bubble diameter in the bed column using the perforated distributor plate is expressed as 8:

true d_{normalb} = d_{normalb, \infty} - (d_{b, \infty} - d_{b, 0}) normalexp (- 0.3 H / D)

…”

Section: Resultsmentioning

confidence: 99%

Investigation of Bubble Behavior in Gas‐Solid Fluidized Beds with Different Gas Distributors

Jang

Feng

2021

Chem Eng & Technol

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The behavior of gas bubbles in a gas‐solid fluidized bed was investigated by computational fluid dynamics simulation. A two‐phase flow model incorporating the kinetic theory of granular flow was used to study the effect of gas distributor design on the size distribution and the rise velocity of gas bubbles in the bed column. The experiments were conducted on four gas distributors with different orifice sizes and pitches. The geometry of the gas distributor had a significant influence on the bubble behavior in the gas‐solid fluidized bed. The effects of static bed height and superficial gas velocity were also considered. Bubble diameter and bubble rise velocity increased with gas velocity. However, at low bed height, the deviation in bubble size was indistinct because at higher gas velocity the rising tendency of the bubble formed just above the gas distributor increased and the probability of bubble coalescence decreased.

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true d_{normalb} = d_{normalb, \infty} - (d_{b, \infty} - d_{b, 0}) normalexp (- 0.3 H / D)

…”

Section: Resultsmentioning

confidence: 99%

Investigation of Bubble Behavior in Gas‐Solid Fluidized Beds with Different Gas Distributors

Jang

Feng

2021

Chem Eng & Technol

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“…Thanks to the huge number of data available from image analysis technique, it is possible to observe that both Gamma and Log-normal distributions generally adopted in literature give rise to poor agreement with the experimental data for bubbles larger than 5 cm. This effect is probably linked to an oversimplification of the phenomena, that need more sophisticated analysis techniques to be better characterized (Busciglio et al, 2010).…”

Section: Typical Resultsmentioning

confidence: 99%

Measurement of Multiphase Flow Characteristics Via Image Analysis Techniques: The Fluidization Case Study

Busciglio¹,

Vella²,

Micale³

2012

Hydrodynamics - Theory and Model

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“…In these, g 0 , e, p s and I 2D are the solid radial distribution function, particle restitution coefficient, solids pressure and second invariant of the deviatoric stress tensor, for which, further closure terms, sub-models or constitutive relations are needed. λ σ is the solid bulk viscosity, which is another submodel, commonly modeled according to Equation (13) [63]. Θ σ is a so-called granular temperature, which captures the fluctuation of the kinetic energy of the particles.…”

Section: Solids Stress Tensormentioning

confidence: 99%

“…Despite these vast industrial applications of fluidized beds, the local-scale phenomena, such as the flow structures, mixing behaviors solids trajectories and recirculation inside the bed, are poorly understood [10][11][12][13][14]. To a great extent, the lack of a deeper knowledge of the local-scale phenomena on these systems lies on the complexity of the multiphase and multiscale interactions, and the recognized scale dependency of these systems [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Pairing Experimental and Mathematical Modeling Studies on Fluidized Beds for Enhancement of Models Predictive Quality: A Current Status Overview

Uribe

Al‐Dahhan

2021

Processes

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Modeling of gas-solid fluidized systems has been a prevailing challenge over the last few decades. With different approaches and implementing different sub-models to capture the essential multiphase and multiscale phenomena in these systems, major advances have been achieved, even though most models are only subject to a practical validation of macroscopic parameters. The current description of fluidized beds through mathematical models relies on the inclusion of vast sub-models, leading to an unquantifiable degree of uncertainty on the models’ applicability for extrapolation studies. Furthermore, each closure and fitting parameter in the model represents a possible source of deviation, and their optimization, hence, becomes another major challenge. The recent advances in measurement techniques can enable us to troubleshoot and optimize the implemented models and sub-models based on local scale measurements. Local multiphase hydrodynamic information obtained by advanced measurement techniques can enable the validation of local predictions and optimization of the coupled sub-models, leading to the development of simplified and highly predictive models. Thus, pairing advanced experimental studies on these systems with insightful modeling approaches is required to advance the shortcoming and enhance the predictive quality of the models. In this work, an overview of the status of modeling and experimental measurement techniques for gas-solid fluidized beds is presented; then, an overview on pairing both experimental and modeling studies to improve the models’ local predictions for fluidized beds is presented.

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Experimental analysis of bubble size distributions in 2D gas fluidized beds

Cited by 32 publications

References 17 publications

Investigation of Bubble Behavior in Gas‐Solid Fluidized Beds with Different Gas Distributors

Investigation of Bubble Behavior in Gas‐Solid Fluidized Beds with Different Gas Distributors

Measurement of Multiphase Flow Characteristics Via Image Analysis Techniques: The Fluidization Case Study

Pairing Experimental and Mathematical Modeling Studies on Fluidized Beds for Enhancement of Models Predictive Quality: A Current Status Overview

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