Micro-visualization of clusters in a fast fluidized bed

Li, Hongzhong; Xia, Yashen; Tung, Yuanki; Kwauk, Mooson

doi:10.1016/0032-5910(91)80035-h

Cited by 92 publications

(47 citation statements)

References 5 publications

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“…For dilute flows such as in risers, however, micro-scale and meso-scale overlap and no clear length scale separation can be distinguished (l = 0.88 mm > d cl = 0.25 mm, for s = 0.01). In fact, micro-visualization also suggests that the size of particle structure is highly variable, spanning from few particles to nearly the reactor diameter (Li et al, 1991). In such cases, meso-scale is not so distinct from micro-and macro-scales.…”

Section: Why Structure-dependent Drag Force?mentioning

confidence: 94%

Simulation of gas–solid two-phase flow by a multi-scale CFD approach—of the EMMS model to the sub-grid level

Wang

2007

Chemical Engineering Science

388

194

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Section: Why Structure-dependent Drag Force?mentioning

confidence: 94%

Simulation of gas–solid two-phase flow by a multi-scale CFD approach—of the EMMS model to the sub-grid level

Wang

2007

Chemical Engineering Science

388

194

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“…Tuzla et al (1998) indicated that clusters also exist in the downflow of the solids in the downer. Experimental measurements of cluster velocity and length have been reported by researchers (e.g., Horio et al, 1988;Li et al, 1991;Soong et al, 1995) and correlations are also available for estimating the cluster size (e.g., Xu and Kato, 1999). However, almost no attention has been paid to clusters in dense bed due to more complex flow structure of solids in the dense bed of bubbling and turbulent fluidized beds compared to that of fast fluidization.…”

Section: Introductionmentioning

confidence: 96%

Flow structure of the solids in gas–solid fluidized beds

Mostoufi

Chaouki

2004

Chemical Engineering Science

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“…With respect to the impact of operating conditions, Figure 168 shows that the effect of the four operating conditions investigated on cluster appearance probability is generally insignificant. It is noted in previous work that cluster appearance probability increases (Brereton and Grace 1993;Zou, Li et al 1994; Wei, Yang et al 1995;Manyele, Parssinen et al 2002), decreases (Li, Xia et al 1991), or is insensitive (Li, Zhu et al 1995) to an increase of solid loading (m); these seemingly contradictory previous results can again be traced to data obtained from only a limited portion of the riser and lacking local details. For example, comparing Figure 168c and d at h/H = 0.16 reveals that increasing m increases appearance probability at the wall (r/R = 1) but decreases apperance probbility at the center (r/R = 0), which implies that a lack of a more complete dataset may lead to opposite trends; whereas the complete landscape given in Figure 168 shows clearly that the small change due to m is insignificant compared to the greater profile variations associated with the effect of riser height.…”

mentioning

confidence: 66%

Development, Verification, and Validation of Multiphase Models for Polydisperse Flows

Hrenya¹,

Cocco²,

Fox³

et al. 2011

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This report describes in detail the technical findings of the DOE Award entitled "Development, Verification, and Validation of Multiphase Models for Polydisperse Flows." The focus was on high-velocity, gas-solid flows with a range of particle sizes. A complete mathematical model was developed based on first principles and incorporated into MFIX. The solid-phase description took two forms: the Kinetic Theory of Granular Flows (KTGF) and Discrete Quadrature Method of Moments (DQMOM). The gas-solid drag law for polydisperse flows was developed over a range of flow conditions using Discrete Numerical Simulations (DNS). These models were verified via examination of a range of limiting cases and comparison with Discrete Element Method (DEM) data. Validation took the form of comparison with both DEM and experimental data. Experiments were conducted in three separate circulating fluidized beds (CFB's), with emphasis on the riser section. Measurements included bulk quantities like pressure drop and elutriation, as well as axial and radial measurements of bubble characteristics, cluster characteristics, solids flux, and differential pressure drops (axial only). Monodisperse systems were compared to their binary and continuous particle size distribution (PSD) counterparts. The continuous distributions examined included Gaussian, lognormal, and NETLprovided data for a coal gasifier.FINAL TECHNICAL 4 DE-FC26-07NT43098 5 EXECUTIVE SUMMARYThis report is the final technical report for the award DE-FC26-07NT43098 entitled "Development, Verification, and Validation of Multiphase Models for Polydisperse Flows." Below is a summary of work completed under the grant for each of the major goals. Goal I: Continuum Theory for Solid PhaseTwo separate and complementary continuum theories were derived to model polydisperse solids: the kinetic theory of granular flow (KTGF) and the discrete quadrature method of moments (DQMOM). Both theories are based on first principles with no adjustable parameters. The polydisperse KTGF and DQMOM were then encoded in MFIX, and underwent a wide range of verification testing to ensure, to the best possible extent, that no coding errors were present. These verification tests included both granular and gas-solid flows, including cases where an analytical solution and/or DEM (discrete element method) data and/or simple test cases. For the case of KTGF, another set of constitutive equations were derived which rigorously incorporated the gas phase for a simplified case of monodisperse systems at low Reynolds numbers. This derivation used the acceleration model developed in Task 2 from direct numerical simulations (DNS) in the starting kinetic equation, and indicated the effect of the fluid phase on the resulting constitutive relations. Goal II: Improved Gas-Particle Drag Laws -effect of particle size distributionIn this portion of the effort, two types of direct numerical simulations (DNS) were used to extract the drag force experienced by particles in a polydisperse suspension. These two methods, na...

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Micro-visualization of clusters in a fast fluidized bed

Cited by 92 publications

References 5 publications

Simulation of gas–solid two-phase flow by a multi-scale CFD approach—of the EMMS model to the sub-grid level

Simulation of gas–solid two-phase flow by a multi-scale CFD approach—of the EMMS model to the sub-grid level

Flow structure of the solids in gas–solid fluidized beds

Development, Verification, and Validation of Multiphase Models for Polydisperse Flows

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