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

Swirling-flow nanobubble generator is an efficient hydrodynamic cavitation method that can continuously produce enormous quantities of bulk nanobubbles. However, the development of hydrodynamic models of nanobubble generation remains challenging and is rarely found in the literature due to the associated modeling complexity. In this work, a hydrodynamic model was developed to predict bubble size distribution in a swirling-flow type nanobubble generator using a combination of computational fluid dynamics (CFD) and population balance method (PBM). The proposed model was evaluated by considering several combinations of bubble coalescence, breakage, and turbulence models. The results show that the combination of the turbulence coalescence and Luo breakage models predicted better than any other model combination. The selection of appropriate turbulence models could improve the modeling accuracy. The standard k-omega model provided better predictions than other turbulence models for high flow rates, while the standard k-epsilon model was more appropriate for low flow rates. The bubble number density was successfully predicted for a 30 min generation time. The turbulence dissipation rate influenced the bubble number density and mass transfer, and the results of simulation considering this relation corresponded to the experimental results. Therefore, the coupled CFD-PBM model can aid in the design of nanobubble generators using virtual prototypes and reduce the development costs required for broader applications.

Automated summary

In this study, a hydrodynamic model was developed to predict bubble size distribution in a swirling-flow nanobubble generator. The model combined computational fluid dynamics and population balance method and was evaluated with several combinations of turbulence, coalescence, and breakage models. It was found that the combination of turbulence coalescence and Luo breakage models predicted the best. The selection of appropriate turbulence models improved the modeling accuracy.