Transient dynamics of solid concentration in downer fluidized bed

Tuzla, Kemal; Sharma, Arun; Chen, J.C.; Schiewe, T.; Wirth, Karl‐Ernst; Molerus, O.

doi:10.1016/s0032-5910(98)00137-5

Cited by 86 publications

(45 citation statements)

References 4 publications

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“…[10][11][12] To quantitatively and objectively characterize the dynamic behavior of clusters from solids holdup fluctuation data, an unambiguous criterion is necessary to determine the threshold that discriminates the dense cluster phase from the continuous phase.…”

Section: Introductionmentioning

confidence: 99%

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Multiresolution analysis on identification and dynamics of clusters in a circulating fluidized bed

Yang

Leu

2009

AIChE Journal

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). An appropriate level of approximation subsignal was systematically specified as a threshold for cluster identification, based on multiresolution analysis (MRA) of wavelet transformation. By the established threshold, the dynamic properties of clusters including the appearance time fraction of clusters F cl , average cluster duration time s cl , cluster frequency f cl , and local average solids holdup in clusters e sc , at different radial and axial positions were determined under the turbulent, transition and fast fluidization flow regimes. The results also describe the dynamic properties of clusters and flow patterns in the splash zone along with the dense bottom region of the circulating fluidized beds.

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Section: Introductionmentioning

confidence: 99%

“…deviation. According to Soong et al's 10 criterion, Tuzla et al 11 introduced the standard that the solids holdup of a cluster must be greater than the time-averaged solids holdup by at least two times the standard deviation (2r s criterion). Sharma et al 12 also modified Soong et al's 10 criterion and proposed a mean-referenced criterion.…”

Section: Introductionmentioning

confidence: 99%

Multiresolution analysis on identification and dynamics of clusters in a circulating fluidized bed

Yang

Leu

2009

AIChE Journal

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“…This way the clusters can be identified and considered as such when an instantaneous solid fraction exceeds that limit. This criterion was used by Tuzla et al (1998) to detect clusters in a downer fluidized bed. Sharma et al (2000) slightly changed the above criterion on the basis of experimental evidence.…”

Section: Soongmentioning

confidence: 99%

“…An illustrative application of the mean-referenced criterion is presented in Figure 1 After a cluster is identified, its four basic characteristics, as defined by Tuzla et al (1998) and Sharma et al (2000), can be calculated. These characteristics are the mean duration time, the frequency of occurrence, the existence time fraction and the mean solid concentration.…”

Section: Soongmentioning

confidence: 99%

“…Their analyses were of experimental measurements obtained with a capacitance probe, which provided instantaneous local volumetric solid fraction in a 15 cm diameter circulating fluidized bed. Despite the fact that the methodology was first applied to results of experiments (Soong et al, 1993apud Sharma et al, 2000Tuzla et al, 1998;Sharma et al, 2000), it was also recently applied by Helland et al (2002) to results of numerical simulation. …”

Section: Introductionmentioning

confidence: 99%

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A numerical simulation analysis of the effect of the interface drag function on cluster evolution in a CFB riser gas-solid flow

Cabezas‐Gómez¹,

Milioli²

2004

Braz. J. Chem. Eng.

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-The dynamics of formation, dissipation and breaking of coherent structures in the riser gas-solid flow of a circulating fluidized bed (CFB) are evaluated by numerical simulation. The simulation is performed using the MICEFLOW code, which includes IIT's two-fluid hydrodynamic model B. The methodology for cluster characterization is used from Sharma et al. and is based on determination of four characteristics, average lifetime, average volumetric fraction of solid, existence time fraction and frequency of occurrence. Clusters are identified applying a criterion for the time average value of the volumetric solid fraction. A qualitative analysis of the influence of different drag function correlations on the hydrodynamics of the flow, including the evolution of coherent structures, is performed. The simulation predictions are also compared to experimental results. The results indicate that the choice of a correlation for drag function should be quite judicious. Finally it is shown that the mean clustering criteria of Sharma et al. should be modified to take into account other factors that influence cluster dynamics.

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CPFD flow pattern simulation in downer reactors

Abbasi

Islam

Ege³

et al. 2012

AIChE Journal

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This study contributes with a computational fluid dynamic simulation based on the numerical solution of continuity and momentum balance equations in a three‐dimensional (3‐D) framework. The proposed down flow gas–solid suspension model includes a unit configuration and CD drag coefficients recommended for these units. Computational particle fluid dynamics (CPFD) calculations using suitable boundary conditions and a Barracuda (version: 14.5.2) software allow predicting local solid densification and asymmetric “wavy flows.” In addition, this model forecasts for the conditions of this study higher particle velocity than gas velocity, once the flow reaches 1 m from the gas injector. These findings are accompanied with observations about the intrinsic rotational character of the flow. CPFD numerical 3‐D calculations show that both gas and particle velocities involve the following: (a) an axial velocity component, (b) a radial velocity component (about 5% of axial velocity component), and (c) an angular velocity component. The calculated velocity components and the rotational flow pattern are established for a wide range of solid flux/gas flux ratios. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1635–1647, 2013

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Transient dynamics of solid concentration in downer fluidized bed

Cited by 86 publications

References 4 publications

Multiresolution analysis on identification and dynamics of clusters in a circulating fluidized bed

Multiresolution analysis on identification and dynamics of clusters in a circulating fluidized bed

A numerical simulation analysis of the effect of the interface drag function on cluster evolution in a CFB riser gas-solid flow

CPFD flow pattern simulation in downer reactors

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