Bubble Size Distribution in Two-Dimensional Gas–Solid Fluidized Beds

2012 ◽  
Vol 51 (18) ◽  
pp. 6571-6579 ◽  
Author(s):  
S. Movahedirad ◽  
A. Molaei Dehkordi ◽  
M. Banaei ◽  
N. G. Deen ◽  
M. van Sint Annaland ◽  
...  
Keyword(s):  
Size Distribution ◽  
Bubble Size ◽  
Fluidized Beds ◽  
Bubble Size Distribution ◽  
Two Dimensional

Local bubble-size distribution in fluidized beds

AIChE Journal ◽  
2000 ◽  
Vol 46 (7) ◽  
pp. 1340-1347 ◽  
Author(s):  
D. Santana ◽  
A. Macías-Machín
Keyword(s):  
Size Distribution ◽  
Bubble Size ◽  
Fluidized Beds ◽  
Bubble Size Distribution ◽  
Local Bubble

scholarly journals Maximum entropy estimation of the bubble size distribution in fluidized beds

2009 ◽  
Vol 64 (10) ◽  
pp. 2307-2319 ◽  
Author(s):  
C. Sobrino ◽  
J.A. Almendros-Ibáñez ◽  
D. Santana ◽  
C. Vázquez ◽  
M. de Vega
Keyword(s):  
Maximum Entropy ◽  
Size Distribution ◽  
Bubble Size ◽  
Fluidized Beds ◽  
Bubble Size Distribution ◽  
Entropy Estimation ◽  
Maximum Entropy Estimation

Physiochemical properties and reproducibility of air-based sodium tetradecyl sulphate foam using the Tessari method

2016 ◽  
Vol 32 (6) ◽  
pp. 390-396 ◽  
Author(s):  
Mike R Watkins ◽  
Richard J Oliver
Keyword(s):  
Size Distribution ◽  
Bubble Size ◽  
Bubble Size Distribution ◽  
Foam Density ◽  
Physiochemical Properties ◽  
Significant Difference ◽  
Experienced Operator ◽  
Sodium Tetradecyl Sulphate ◽  
Foam Production ◽  
Made In

Objectives The objectives were to examine the density, bubble size distribution and durability of sodium tetradecyl sulphate foam and the consistency of production of foam by a number of different operators using the Tessari method. Methods 1% and 3% sodium tetradecyl sulphate sclerosant foam was produced by an experienced operator and a group of inexperienced operators using either a 1:3 or 1:4 liquid:air ratio and the Tessari method. The foam density, bubble size distribution and foam durability were measured on freshly prepared foam from each operator. Results The foam density measurements were similar for each of the 1:3 preparations and for each of the 1:4 preparations but not affected by the sclerosant concentration. The bubble size for all preparations were very small immediately after preparation but progressively coalesced to become a micro-foam (<250 µm) after the first 30 s up until 2 min. Both the 1% and 3% solution foams developed liquid more rapidly when made in a 1:3 ratio (37 s) than in a 1:4 ratio (45 s) but all combinations took similar times to reach 0.4 ml liquid formation. For all the experiments, there was no statistical significant difference between operators. Conclusions The Tessari method of foam production for sodium tetradecyl sulphate sclerosant is consistent and reproducible even when made by inexperienced operators. The best quality foam with micro bubbles should be used within the first minute after production.

scholarly journals Development of Bubble Characteristics on Chute Spillway Bottom

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1129
Author(s):  
Ruidi Bai ◽  
Chang Liu ◽  
Bingyang Feng ◽  
Shanjun Liu ◽  
Faxing Zhang
Keyword(s):  
Size Distribution ◽  
Bubble Size ◽  
Dissipation Rate ◽  
Bubble Diameter ◽  
Bubble Size Distribution ◽  
Impact Zone ◽  
Air Bubble ◽  
Air Supply ◽  
Bubble Characteristics ◽  
The Impact

Chute aerators introduce a large air discharge through air supply ducts to prevent cavitation erosion on spillways. There is not much information on the microcosmic air bubble characteristics near the chute bottom. This study was focused on examining the bottom air-water flow properties by performing a series of model tests that eliminated the upper aeration and illustrated the potential for bubble variation processes on the chute bottom. In comparison with the strong air detrainment in the impact zone, the bottom air bubble frequency decreased slightly. Observations showed that range of probability of the bubble chord length tended to decrease sharply in the impact zone and by a lesser extent in the equilibrium zone. A distinct mechanism to control the bubble size distribution, depending on bubble diameter, was proposed. For bubbles larger than about 1–2 mm, the bubble size distribution followed a—5/3 power-law scaling with diameter. Using the relationship between the local dissipation rate and bubble size, the bottom dissipation rate was found to increase along the chute bottom, and the corresponding Hinze scale showed a good agreement with the observations.

CFD Simulation of Gas Dispersion in a Stirred Tank of Dual Rushton Turbines

2017 ◽  
Vol 15 (4) ◽  
Author(s):  
Xinju Li ◽  
Xiaoping Guan ◽  
Rongtao Zhou ◽  
Ning Yang ◽  
Mingyan Liu
Keyword(s):  
Flow Field ◽  
Size Distribution ◽  
Liquid Flow ◽  
Bubble Size ◽  
Great Influence ◽  
Gas Holdup ◽  
Bubble Size Distribution ◽  
Stirred Tank ◽  
Treatment Methods ◽  
Gas Dispersion

Abstract3D Eulerian-Eulerian model was applied to simulate the gas-liquid two-phase flow in a stirred tank of dual Rushton turbines using computational fluid dynamics (CFD). The effects of two different bubble treatment methods (constant bubble sizevs. population balance model, PBM) and two different coalescence models (Luo modelvs. Zaichik model) on the prediction of liquid flow field, local gas holdup or bubble size distribution were studied. The results indicate that there is less difference between the predictions of liquid flow field and gas holdup using the above models, and the use of PBM did not show any advantage over the constant bubble size model under lower gas holdup. However, bubble treatment methods have great influence on the local gas holdup under larger gas flow rate. All the models could reasonably predict the gas holdup distribution in the tank operated at a low aeration rate except the region far from the shaft. Different coalescence models have great influence on the prediction of bubble size distribution (BSD). Both the Luo model and Zaichik model could qualitatively estimate the BSD, showing the turning points near the impellers along the height, but the quantitative agreement with experiments is not achieved. The former over-predicts the BSD and the latter under-predicts, showing that the existing PBM models need to be further developed to incorporate more physics.

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