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An artificial neural network-based drag model modification method for microbubble flow
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DOI：10.1002/aic.70639.png)
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Abstract
En 中文
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.
Keywords:
ANN
CFD simulation
drag model
microbubble
microbubble swarm effect
Journal
IF:
4
Papers:
1.1W
Citations:
2.9W
Organization
Cited Papers
Hydrodynamics in microbubble column reactors: Effect of operating conditions and liquid properties
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