CFD-PBM Coupled modeling of bubble size distribution in a swirling-flow nanobubble generator

Sutikno, Priyono; Soelaiman, Tubagus Ahmad Fauzi; Sugiarto, Anto Tri

doi:10.1080/19942060.2022.2043186

Cited by 12 publications

(7 citation statements)

References 57 publications

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“…The Luo and Svendsen breakup and Turbulent coalescence models were compatible with the experimental data with the lowest relative error of −24.8%. Alam et al also found that the Luo and Svendsen-Turbulent model combination could best predict the bubble size distribution.…”

Section: Resultsmentioning

confidence: 99%

“…For the swirl flow bubble generator, Alam et al reported that the combination of the Turbulent aggregation model , and the Lehr breakage model has the best compatibility with PIV experimental results. However, recently, they concluded that the Turbulent coalescence , and Luo and Svendsen breakage models have better results than all other model combinations in the swirl flow nanobubble generator . Also, in the compact flotation unit (CFU), the flow pattern is mainly a swirling flow because of a tangential inlet.…”

Section: Modeling and Simulationsmentioning

confidence: 99%

“…It is convenient to obtain a bubble generator’s flow field information and bubble generation performance at the initial research stage. The computational fluid dynamics coupled population balance model (CFD-PBM) has been developed for the bubble column, flotation, and nanobubble generation. , Alam et al developed a CFD-PBM approach to predict the bubble size distribution in a nanobubble generator with swirl flow. The results indicated that the Luo and Svendsen breakup and Turbulent coalescence model combination had the best compatibility with the experimental data.…”

Section: Introductionmentioning

confidence: 99%

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Microbubble Dispersion Process Intensification Using Novel Internal Baffles

Chen

Song

2022

Ind. Eng. Chem. Res.

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A bubble generator is a crucial component in the flotation system, and the bubble size is directly related to bubble mineralization. This paper focused on the microbubble size control of a swirl flow microbubble generator. The computational fluid dynamics coupled population balance model (CFD-PBM) was developed to evaluate the flow characteristic and the bubble generation performance of the microbubble generator with five novel internal baffles. The results showed that the baffle could improve the bubble dispersion without extra energy loss, and the single helical baffle could generate tiny bubbles with a Sauter mean diameter of 0.36 mm at a liquid flow rate of 2000 L/h. The baffle could limit the positive axial velocity and hinder the tangential velocity effect in the straight pipe section, weakening the swirl flow effect. Hence, although the baffle slightly reduced the pressure gradient magnitude and the turbulent intensity, it still presented an effective improvement in the bubble dispersion.

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Section: Resultsmentioning

confidence: 99%

Section: Modeling and Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Microbubble Dispersion Process Intensification Using Novel Internal Baffles

Chen

Song

2022

Ind. Eng. Chem. Res.

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“…The CFD two-phase flow simulation was based on a Euler-Euler approach as depicted in our previous study [14,17]. In this work, the CFD-PBM coupled simulation was applied to predict the nanobubble size distribution by involving the blended model of the turbulence coalescence model [18,19], and the Luo breakage model [20], while the growth phenomenon was neglected.…”

Section: Model Description 21 Cfd-pbm Coupled Modelingmentioning

confidence: 99%

“…Fig. 1 depicts a 3D geometric representation of a swirlingflow bubble generator linked to a bubble column [17]. The gravitational acceleration was set at 9.81 m s -2 in the vertical direction opposite to the liquid inlet.…”

Section: Model Description 21 Cfd-pbm Coupled Modelingmentioning

confidence: 99%

Population Balance Modeling and Multi‐Response Optimization of a Swirling‐Flow Nanobubble Generator

2022

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A novel model is proposed to optimize a swirling‐flow nanobubble generator utilizing a combined model of computational fluid dynamics‐population balance method (CFD‐PBM) coupled model and response surface method (RSM). The CFD‐PBM coupled model was validated by experiments based on the bubble size distribution and mass transfer. The validated model was utilized as an input for optimization using RSM. Optimization involved three factors and four target responses obtained by applying a central composite design (CCD). The adequacy of the models was evaluated by analysis of variance. Based on the optimization results, the proposed model can produce an optimized and feasible solution as a reference in the design of a nanobubble generator and can be applied further on a larger scale.

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