Velocity fluctuation interpretation in the near wall region of a dense riser

Breault, Ronald W.; Guenther, Chris; Shadle, Lawrence J.

doi:10.1016/j.powtec.2007.08.018

Cited by 46 publications

(25 citation statements)

References 10 publications

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“…Once, an experimentally verified flow pattern has been established, the IIT computer code computes gas and solids Reynolds stresses and dispersion coefficients. The calculation methodology of the dispersion coefficients is identical to that used at NETL for conversion of their measured velocity data into dispersion coefficients (Breault et al, 2008). The agreement between the CFD computations and the experiments is reasonable in view of the simplification of the three-dimensional geometry, particularly the asymmetry caused by the blind tee on top of the NETL riser.…”

Section: Introductionmentioning

confidence: 86%

“…There are two non-process categories of fluctuating energies (as defined above), those due to random particle oscillations and recognized as granular temperature, and those due to cluster or bubble motion which have been termed particle turbulent kinetic energy in Breault et al (2008). From the kinetic theory model, the granular temperature is calculated directly from the code.…”

Section: Granular Temperaturementioning

confidence: 99%

“…Breault et al, 2008;Gidaspow et al, 2004) using kinetic theory and velocimetry that there are three kinds of fluctuating energies in fluidization:…”

Section: Introductionmentioning

confidence: 99%

“…More recently they have used a laser doppler velocimetry (LDV) technique to measure the instantaneous particle velocities of cork particles (Breault et al, 2005). The measurements of instantaneous particle velocities enable us to compute the dispersion coefficients using the autocorrelation technique (Breault et al, 2008;Jiradilok et al, 2006Jiradilok et al, , 2007.…”

Section: Introductionmentioning

confidence: 99%

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Computation of turbulence and dispersion of cork in the NETL riser

Jiradilok

Gidaspow

Breault

et al. 2008

Chemical Engineering Science

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Section: Introductionmentioning

confidence: 86%

Section: Granular Temperaturementioning

confidence: 99%

“…Breault et al, 2008;Gidaspow et al, 2004) using kinetic theory and velocimetry that there are three kinds of fluctuating energies in fluidization:…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Computation of turbulence and dispersion of cork in the NETL riser

Jiradilok

Gidaspow

Breault

et al. 2008

Chemical Engineering Science

Self Cite

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“…A further check on the validity of our measurements is the time criterion proposed by Breault et al (2008), which requires the temporal sampling resolution to be smaller than the collision time estimate:…”

Section: Autocorrelation Functions and Particle Msdmentioning

confidence: 99%

Granular temperature in a liquid fluidized bed as revealed by diffusing wave spectroscopy

Živković¹,

Biggs²,

Glass³

et al. 2009

Chemical Engineering Science

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We report granular temperature and solid fraction fields for a thin rectangular bed (20×200 mm crosssection and 500 mm high) of glass particles (mean diameter of 165 m and density of 2500 kg/m 3 ) fluidized by water for superficial velocities ranging from 0.05U t , which is approximately double the minimum fluidization velocity, to 0.49U t , where U t is the particle terminal velocity estimated by fitting the Richardson-Zaki correlation to the bed expansion data. At superficial velocities below 0.336U t , the solid fraction and granular temperature are uniform throughout the bed. At higher superficial velocities, the solid fraction tends to decrease with height above the distributor, whilst the granular temperature first increases to a maximum before decaying towards the top of the bed. Correlation of the mean granular temperature with the mean solid fraction and the local granular temperature with the local solid fraction both suggest that the granular temperature in the liquid fluidized bed can be described solely in terms of the solid fraction. The granular temperature increases monotonically with solid fraction to a maximum at ≈ 0.18 where it then decreases monotonically as approaches the close-packed limit.

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Toward a mesoscale‐structure‐based kinetic theory for heterogeneous gas‐solid flow: Particle velocity distribution function

Wang

Zhao

2016

AIChE Journal

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Mesoscience has recently been proposed as a possible general concept for describing complex systems far from equilibrium, however, concrete formulations are needed, and particularly, a statistical mechanics foundation of mesoscience remains to be explored. To this end, the mathematical theory of stochastic geometry is combined with the energy minimization multi-scale (EMMS) principle under the concept of mesoscience to propose a statistical mechanics framework. An EMMS-based particle velocity distribution function is then derived as an example to show how the proposed framework works, and more importantly, as a first key step toward a generalized kinetic theory for heterogeneous gas-solid flow. It was shown that the resultant EMMS-based distribution is bimodal, instead of the widely-used Maxwellian distribution, but it reduces to the Maxwellian distribution when the gas-solid system is homogeneous. The EMMS-based distribution is finally validated by comparing its prediction of the variance of solid concentration fluctuation and granular temperature with experimental data available in literature.

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Velocity fluctuation interpretation in the near wall region of a dense riser

Cited by 46 publications

References 10 publications

Computation of turbulence and dispersion of cork in the NETL riser

Computation of turbulence and dispersion of cork in the NETL riser

Granular temperature in a liquid fluidized bed as revealed by diffusing wave spectroscopy

Toward a mesoscale‐structure‐based kinetic theory for heterogeneous gas‐solid flow: Particle velocity distribution function

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