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To eliminate the need for flow-disrupting intrusive probes and fragile fluid-specific assumptions, this work presents VFNet, a dual-branch network that estimates gas-liquid void fractions directly from synchronized multi-view video. The model integrates a localized spatial fusion branch with a global spatio-temporal branch to dynamically refine a coarse geometric baseline using both multi-angle views and temporal evolution. Trained on computational fluid dynamics (CFD) simulations, VFNet outperforms standard learning-based and classical baselines while successfully generalizing to improve downstream flow-pattern classification on real-world fluid systems.
Accurate void fraction estimation no longer requires flow-disrupting physical probes: multi-view video coupled with spatio-temporal modeling recovers complex multiphase fluid parameters and directly transfers from synthetic CFD to real experimental flows.
Void fraction, which quantifies the proportion of the fluid flow volume occupied by the gas phase, is a key parameter in the characterization of gas-liquid two-phase flow. Existing estimation methods either rely on flow assumptions that do not generalize across different fluids or on intrusive sensing that disturbs the flow behavior. We propose VFNet, a dual-branch spatio-temporal neural network for void-fraction prediction from synchronized multi-view videos of two-phase flow. A local branch extracts features from confined spatial regions and fuses the synchronized dual views, while a spatio-temporal branch captures the global evolution of the flow across space and time to refine a coarse geometric estimate. Trained on simulated computational fluid dynamics (CFD) data with known ground-truth void fractions and evaluated against both learning-based and traditional baselines, VFNet achieves the best performance across a broad range of metrics and also improves downstream flow-pattern classification on real two-phase flow data.
