Computational modeling demonstrates improved prediction of gas holdup and swarm regimes in microbubble reactors, highlighting the utility of machine learning in multiphase fluid dynamics.
An accurate gas–liquid drag model is crucial for simulating hydrodynamics of microbubble reactors. However, incorporating bubble‐swarm interactions remains challenging. In this study, high‐speed imaging is used to identify gas holdup and the bubble size‐volume distribution (BSVD) as key parameters governing microbubble swarm effects, which are classified as accelerating, hindrance, or weak. A generalized drag modification method based on an artificial neural network (ANN) is proposed to incorporate swarm dynamics across a wide range of operating conditions. The model is validated by comparing high‐fidelity CFD simulations with experimental data. The results show that the ANN‐corrected drag model achieves significantly higher accuracy than conventional drag models in predicting overall gas holdup and reproducing different swarm regimes.
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Zhai et al. (2026) studied this question.
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