Experimental investigation on mixed jet and mass transfer characteristics of horizontal aeration process

Park, Sang Kyoo; Yang, Hei Cheon

doi:10.1016/j.ijheatmasstransfer.2017.05.120

Cited by 20 publications

(16 citation statements)

References 25 publications

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“…The slope of the lines in Figure represents the value of the overall mass transfer coefficient. In Park and Yang, a value of 1.85 × 10 −3 s −1 was obtained for this case. The values predicted for the three mesh refinements are relatively close in value to each other, with the coarsest refinement being the furthest from the experimental value.…”

Section: Resultssupporting

confidence: 88%

“…Figure shows a comparison between the experimental and predicted gas phase fraction contours for the intermediate mesh. The results show that the penetration length predictions fall below the value of 0.6 m as detailed in Park and Yang . However, as the mesh was refined, the VOF region predicts a longer momentum length.…”

Section: Resultsmentioning

confidence: 72%

“…After obtaining time‐dependent concentration predictions from the simulation, the mass transfer coefficient can be determined from the same expression used by Park and Yang:

K_{2} a = \frac{\ln ((), \frac{C_{italicst} - C_{0}}{C_{italicSt} - C})}{t}

where C st is the saturation concentration for dissolved oxygen; C 0 is the initial oxygen concentration (set to zero); and C is the concentration at time t .…”

Section: Validation Studiesmentioning

confidence: 68%

“…The predicted average bubble sizes for the other two cases is between these two values. Considering the specific power input rates reported by Park and Yang, the trend of the predicted bubble sizes is consistent with the experimental results. However, as discussed in the previous sections, these bubble size estimates cannot be used as definitive values due to other assumed model parameters.…”

Section: Resultsmentioning

confidence: 99%

“…In the current study, the primary objective for this case was to test the sensitivity of predictions to the value of the turbulent dispersion coefficient ( C TD ), and to obtain a reasonable value for use in subsequent case studies. The second case was focused on the validation of the mass transfer modelling approach using the data from Park and Yang . This case involved the horizontal co‐injection of a mixed gas/liquid jet into a tank initially containing deoxygenated water.…”

Section: Validation Studiesmentioning

confidence: 99%

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Multiscale modelling of mass transfer in gas jets and bubble plumes

O'Malley

Haelssig

2019

Can J Chem Eng

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The injection of high‐speed gas streams into liquids is common in many industrial applications, such as sparging in multiphase reactors and contacting in mass transfer devices. Modelling the fluid dynamics and associated heat and mass transfer processes in such a system is complex because it involves many governing scales and drastic changes in physical properties. In this study, one formulation of a multiscale computational fluid dynamics model is proposed to simulate the fluid dynamics and mass transfer in such systems. The model uses volume‐of‐fluid interface capturing in regions where high mesh resolution can be attained and the drift‐flux or mixture model approximation in regions where mesh resolution is too low to directly resolve interface dynamics. The model was developed to provide a tunable, automatic transition between the two modelling approaches for both fluid dynamics and mass transfer predictions. The approach was validated through a comparison with results from two published studies. In the first case, the implementation of the drift‐flux model was validated through the simulation of a dispersed gas bubble plume injected into a cylindrical tank. In the second case, the fluid dynamics and mass transfer predictions were compared to results from an experimental study involving the horizontal injection of air into a rectangular tank filled with water for the application of aeration. The results show that the modelling approach can provide a good prediction of the experimental data using only limited fitting of empirical parameters, making it applicable to a broad range of other applications.

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Section: Resultssupporting

confidence: 88%

Section: Resultsmentioning

confidence: 72%

“…After obtaining time‐dependent concentration predictions from the simulation, the mass transfer coefficient can be determined from the same expression used by Park and Yang:

K_{2} a = \frac{\ln ((), \frac{C_{italicst} - C_{0}}{C_{italicSt} - C})}{t}

where C st is the saturation concentration for dissolved oxygen; C 0 is the initial oxygen concentration (set to zero); and C is the concentration at time t .…”

Section: Validation Studiesmentioning

confidence: 68%

Section: Resultsmentioning

confidence: 99%

Section: Validation Studiesmentioning

confidence: 99%

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