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.

Bubble Formation From Porous Plates in Liquid Cross-Flow

2018 ◽  
Author(s):  
Thomas Shepard ◽  
Eric Ruud ◽  
Henry Kinane ◽  
Deify Law ◽  
Kohl Ordahl
Keyword(s):  
Size Distribution ◽  
Bubble Size ◽  
Bubble Diameter ◽  
Bubble Formation ◽  
Cross Flow ◽  
Rectangular Channels ◽  
Gas Liquid Flow ◽  
Liquid Flows ◽  
Processing Techniques ◽  
The Impact

Controlling bubble diameter and bubble size distribution is important for a variety of applications and active fields of research. In this study the formation of bubbles from porous plates in a liquid cross-flow is examined experimentally. By injecting air through porous plates of various media grades (0.2 to 100) into liquid flows in rectangular channels of varying aspect ratio (1–10) and gas/liquid flow rates the impact of the various factors is presented. Image processing techniques were used to measure bubble diameters and capture their formation from the porous plates. Mean bubble diameters ranged from 0.06–1.21 mm. The present work expands upon the work of [1] and further identifies the relative importance of wall shear stress, air injector pore size and gas to liquid mass flow ratio on bubble size and size distribution.

scholarly journals Air entrainment and bubble statistics in breaking waves

2016 ◽  
Vol 801 ◽  
pp. 91-129 ◽  
Author(s):  
Luc Deike ◽  
W. Kendall Melville ◽  
Stéphane Popinet
Keyword(s):  
Size Distribution ◽  
Bubble Size ◽  
Dissipation Rate ◽  
Unit Length ◽  
Bubble Radius ◽  
Air Entrainment ◽  
Breaking Waves ◽  
Bubble Size Distribution ◽  
Breaking Wave ◽  
Bubble Plume

We investigate air entrainment and bubble statistics in three-dimensional breaking waves through novel direct numerical simulations of the two-phase air–water flow, resolving the length scales relevant for the bubble formation problem, the capillary length and the Hinze scale. The dissipation due to breaking is found to be in good agreement with previous experimental observations and inertial scaling arguments. The air entrainment properties and bubble size statistics are investigated for various initial characteristic wave slopes. For radii larger than the Hinze scale, the bubble size distribution, can be described by $N(r,t)=B(V_{0}/2{\rm\pi})({\it\varepsilon}(t-{\rm\Delta}{\it\tau})/Wg)r^{-10/3}r_{m}^{-2/3}$ during the active breaking stages, where ${\it\varepsilon}(t-{\rm\Delta}{\it\tau})$ is the time-dependent turbulent dissipation rate, with ${\rm\Delta}{\it\tau}$ the collapse time of the initial air pocket entrained by the breaking wave, $W$ a weighted vertical velocity of the bubble plume, $r_{m}$ the maximum bubble radius, $g$ gravity, $V_{0}$ the initial volume of air entrained, $r$ the bubble radius and $B$ a dimensionless constant. The active breaking time-averaged bubble size distribution is described by $\bar{N}(r)=B(1/2{\rm\pi})({\it\epsilon}_{l}L_{c}/Wg{\it\rho})r^{-10/3}r_{m}^{-2/3}$, where ${\it\epsilon}_{l}$ is the wave dissipation rate per unit length of breaking crest, ${\it\rho}$ the water density and $L_{c}$ the length of breaking crest. Finally, the averaged total volume of entrained air, $\bar{V}$, per breaking event can be simply related to ${\it\epsilon}_{l}$ by $\bar{V}=B({\it\epsilon}_{l}L_{c}/Wg{\it\rho})$, which leads to a relationship for a characteristic slope, $S$, of $\bar{V}\propto S^{5/2}$. We propose a phenomenological turbulent bubble break-up model based on earlier models and the balance between mechanical dissipation and work done against buoyancy forces. The model is consistent with the numerical results and existing experimental results.

scholarly journals Measurement of Bubble Size, Gas holdup and Interfacial Area in Bubble Columns using Image Processing Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 4475-4479
Keyword(s):  
Image Processing ◽  
Size Distribution ◽  
Bubble Size ◽  
Bubble Column ◽  
Rectangular Cross Section ◽  
Bubble Diameter ◽  
Water System ◽  
Interfacial Area ◽  
Gas Holdup ◽  
Bubble Size Distribution

Bubble sizes in bubble column affect transfer processes. Therefore, it’s important to calculate bubble size and interfacial area. Bubble size distribution (BSD) in a bubble column of rectangular cross section with dimensions 0.2m x 0.02m was measured using photographic method (400 fps) for air-water system. Gas holdup, Sauter-mean bubble diameter, aspect ratio and specific interfacial area were estimated from BSD. Effect of superficial gas velocity and static bed height on these parameters was investigated. The bubble size distribution exhibited mono-modal distribution showing the presence of non-uniform homogeneous bubbling regime. The frames of video were analysed using image processing steps to obtain major and minor axis of elliptical bubbles. Values of d32, , and ai were estimated from the data. The value of d32 increased with increasing Ug but is independent of Hs. The values of d32 were somewhat higher than the values reported by other investigators. The value of ai increases with increasing Ug and with decreasing Hs. Present values of compared well with the data reported in literature.

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.

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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