Scale-up of bottom-bed dynamics and axial solids-distribution in circulating fluidized beds of Geldart-B particles

Schouten, J.C.; Zijerveld, Robert C.; Bleek, C.M. van den

doi:10.1016/s0009-2509(98)00352-2

Cited by 28 publications

(17 citation statements)

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“…was more regular with relatively low K, and K slightly increased along the axis of the riser in the bottom section of the wall region. The solids flow in the bottom zone of CFB risers was reported to resemble a bubbling/turbulent bed [16][17][18][19][20][21][22]. This study confirmed the previous statement with relatively low K in the bottom section of the riser.…”

Section: Axial Flow Development In the Riser And The Downersupporting

confidence: 85%

A Comparison of Flow Dynamics and Flow Structure in a Riser and a Downer

Zhu

Briens

2007

Chem Eng & Technol

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Flow development and flow dynamics were systematically investigated using local solids concentration measurements in a pair consisting of a downer (0.1 m I.D., 9.3 m high) and a riser of the same diameter (0.1 m I.D., 15.1 m high). Both statistical and chaos analysis were employed. Values for the Kolmogorov entropy (K), correlation dimension (D), and Hurst exponent (H) were estimated from time series of solids concentration measurements. Axial distributions of chaos parameters were more complex in the downer than those in the riser, especially in the entrance section. Flow in the downer was more uniform with a flatter core in all the radial profiles of chaos parameters. The radial profiles of K varied significantly with increasing axial levels due to different clustering behavior in the wall region of the downer. In both the riser and the downer, anti-persistent flow in the core region and persistent flow behavior near the wall were identified from the profiles of H. Different flow behavior in the region close to the wall in the downer and riser was characterized from the combination of the three chaos parameters. Relationships between chaos parameters and local time-averaged solids holdup in the core and wall regions of the developed sections in both the downer and riser were also analyzed.

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Section: Axial Flow Development In the Riser And The Downersupporting

confidence: 85%

A Comparison of Flow Dynamics and Flow Structure in a Riser and a Downer

Zhu

Briens

2007

Chem Eng & Technol

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“…In addition, the gas-solids flow in CFB systems usually exhibits time-variant, multiscale and nonlinear dynamic behaviors conduced by the chaotic interaction between the void and the solids (dense cluster) phases. 7,[15][16][17] These behaviors are always reflected by the fluctuations of solids holdup data. Employing the time-averaged parameters, i.e., mean and standard deviation, as the threshold criterion for cluster identification might neglect the time-variant, multiscale and nonlinear components of solids holdup fluctuations.…”

Section: Introductionmentioning

confidence: 97%

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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“…The average absolute deviation was used as the cut-off length to estimate the Kolmogorov entropy and since the maximum norm is used to compute the interpoint distance, an embedding dimension of 1 is sufficient for a reliable estimate of the entropy. Schouten et al (1996Schouten et al ( , 1999 described the close relationship between the Kolmogorov entropy and flow macrostructure. These authors used the Kolmogorov entropy to identify the bubble pattern, and related K to the excess gas velocity, U ex = U 0 − U mf , assumed to form the bubbles.…”

Section: Kolmogorov Entropymentioning

confidence: 99%

“…However, the complexity of their hydrodynamics makes the scale-up of these systems difficult, to the extent that many research efforts have focused on scaling and modeling gas-solid fluidized beds (Glicksman, 1984;Matsen, 1996;Moses, 1996;Glicksman et al, 1994;Glicksman and Yule, 1995;Levenspiel, 2002). Moreover, since the non-linear hydrodynamic behavior of fluidized beds may be of a chaotic nature, in this past decade or so, some authors (Van den Bleek and Schouten et al, 1996Schouten et al, , 1999 have tried to develop a chaos scale-up methodology. This type of method needs to take into account the information balance.…”

Section: Introductionmentioning

confidence: 99%