Multi-fluid simulation of turbulent bubbly pipe flows

Hosokawa, Shigeo; Tomiyama, Akio

doi:10.1016/j.ces.2009.09.017

Cited by 79 publications

(127 citation statements)

References 19 publications

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“…Here, U r is the relative velocity between the phases and the subscript c identifies the 381 continuous phase, which is water for all the experiments in Table 1 Here, F is also a function of the bubble aspect ratio E. The effect of aspect ratio on the drag 403 coefficient has been discussed in detail by Hosokawa and Tomiyama (2009). Generally, 404 experimental evidence shows an aspect ratio closer to 1 near a solid wall, which causes an 405 increase in the drag coefficient in the near-wall region.…”

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confidence: 99%

“…In this work, 19 measurement sets from 6 286 different sources were selected, which allows validation of the models proposed over a wide 287 range of parameters and operating conditions. The database includes measurements from 288 Serizawa et al (1975a-c), Wang et al (1987), Liu and Bankoff (1993ab), Liu (1998), 289 Kashinsky and Radin (1999) and Hosokawa and Tomiyama (2009). These data extend to air-290 water upward and downward flows in pipes, characterized by both wall-peaked and core-291 peaked void profiles.…”

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confidence: 99%

“…The presence of 259 bubbles changed the turbulence energy spectrum, causing a shift of the energy to lower length 260 scales that are of the order of the bubble diameter. Hosokawa and Tomiyama (2009) presented 261 measurements of the radial distribution of void fraction, bubble aspect ratio, phase velocities, 262 turbulence kinetic energy and Reynolds stresses for an upward air-water bubbly flow in a 25 263 mm ID pipe. Turbulence suppression was observed at high liquid velocity, whereas 264 turbulence augmentation was more typical of low liquid velocity conditions.…”

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Multiphase turbulence in bubbly flows: RANS simulations

Colombo

Fairweather

2015

International Journal of Multiphase Flow

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Multiphase turbulence in bubbly flows: RANS simulations

Colombo

Fairweather

2015

International Journal of Multiphase Flow

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“…Development of numerical methods for accurately predicting bubbly flows has been desired to assist the design and improvement of these systems. Since the bubbly flows are intrinsically fluctuating, numerical methods frequently adopt turbulence models for liquid phase turbulence, most of which are based on a transport equation of turbulence kinetic energy (1)(2)(3)(4) . Understanding the turbulence kinetic energy (TKE) budget in the bubbly flows and validation of the turbulence models are therefore key issues to improve accuracy of numerical predictions of bubbly flows.…”

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Evaluation of Turbulence Kinetic Energy Budget in Turbulent Flows by Using Photobleaching Molecular Tagging Velocimetry

Hosokawa

Mizumoto

Tomiyama

2012

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Understanding turbulence kinetic energy (TKE) budget in gas-liquid two-phase bubbly flows is indispensable to develop and improve turbulence models for the bubbly flows. Simultaneous measurement of velocity and velocity gradients with a spatial resolution smaller than the Kolmogorov scale is required to evaluate the TKE budget experimentally. We therefore proposed a molecular tagging velocimetry based on photobleaching reaction (PB-MTV) and applied it to turbulent flows in a square duct to demonstrate the possibility of evaluation of TKE budget. In this study, we improved PB-MTV in its processing speed by utilizing GPGPU (General Purpose Graphic Processing Unit) to increase sample number in measurements. We measured TKE budget in a turbulent water flow in a square duct by using the PB-MTV at the same turbulent Reynolds number as DNS data provided by Horiuti, and compared the measured data with the DNS data to validate PB-MTV for evaluation of TKE budget. We also measured TKE budget in a bubbly flow in the square duct to examine effects of bubbles on TKE budget. As a result, we found that (1) PB-MTV can accurately evaluate TKE budget in turbulent flows, (2) bubbles affect the production and diffusion rates of TKE and do not affect the dissipation rate so much, and (3) the model proposed by Troshko and Hassan can reasonably estimate the production rate of the bubble-induced pseudo turbulence.

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“…The drag coefficient, CD, was calculated 202 using the model of Tomiyama et al (2002a), where the effect of the bubble aspect ratio on the drag 203 was also accounted for (Hosokawa and Tomiyama, 2009) …”

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RANS simulation of bubble coalescence and break-up in bubbly two-phase flows

Colombo

Fairweather

2016

Chemical Engineering Science

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In bubbly flows, the bubble size distribution dictates the interfacial area available for the interphase 12 transfer processes and, therefore, understanding the behaviour and the average features of the 13 bubble population is crucial for the prediction of these kinds of flows. In this work, by means of the 14 STAR-CCM+ code, the S population balance model is coupled with an Eulerian-Eulerian two-fluid 15 approach and tested against data on upward bubbly pipe flows. The S model, based on the moments 16 of the bubble size distribution, tracks the evolution of the bubble sizes due to bubble break-up and 17 bubble coalescence. Good accuracy for the average bubble diameter, the velocity and the void 18 fraction radial profiles is achieved with a modified coalescence source. Numerical results show that 19 better predictions are obtained when these flows are considered to be coalescence dominated, but, 20 nevertheless, additional knowledge is required to progress in the development of coalescence and 21 break-up models that include all the possible responsible mechanisms. In this regard, there is a 22 requirement for experimental data that will allow validation of both the predicted bubble diameter 23 distribution and the intensity of the turbulence in the continuous phase which has a significant 24 impact on coalescence and break-up models. An advanced version of the model described, that 25 2 includes a Reynolds stress turbulence formulation and two groups of bubbles to account for the 26 opposite behaviour of spherical bubbles, which accumulate close to the pipe wall, and cap bubbles, 27 that migrate towards the pipe centre, is proposed. The Reynolds stress model is found to better 28 handle the interactions between the turbulence and the interphase forces, and the use of only two 29 bubble groups seems sufficient to describe the whole bubble spectrum and the bubbly flow regime 30 up to the transition to slug flow. 31 32

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Multi-fluid simulation of turbulent bubbly pipe flows

Cited by 79 publications

References 19 publications

Multiphase turbulence in bubbly flows: RANS simulations

Multiphase turbulence in bubbly flows: RANS simulations

Evaluation of Turbulence Kinetic Energy Budget in Turbulent Flows by Using Photobleaching Molecular Tagging Velocimetry

RANS simulation of bubble coalescence and break-up in bubbly two-phase flows

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