Computational fluid dynamics-population balance model approach with drag force of bubble swarms for polydispersed bubbly flow in continuous casting mold.

This article focuses on evaluating and improving drag models for predicting gas–liquid two-phase flow, specifically bubble dynamics, in continuous casting (CC) molds using a coupled computational fluid dynamics (CFD) and population balance model (PBM) approach. Various single bubble drag models were assessed under different turbulence intensities and gas flow rates, revealing that most models except the Grace model predict bubble size distribution (BSD) reasonably well but tend to overestimate bubble diameters near the nozzle at high gas flow rates. To address this, a novel drag correction factor was developed that incorporates both the hindrance effect of small bubbles and the accelerating effect of large bubbles based on local gas holdup and BSD, improving prediction accuracy by reducing the mean relative error by 69.33%. The study also found that modifying drag force via effective viscosity to account for micro-scale turbulence is ineffective, whereas considering turbulence through the ratio of Kolmogorov scale to bubble size enhances simulation fidelity. Overall, the research highlights the importance of accounting for bubble swarm effects and turbulence in drag modeling to accurately simulate bubble behavior and flow patterns in CC molds.