VFNet: Multi-View Spatio-Temporal Model for Void Fraction Estimation in Gas-Liquid Two-Phase Flow

2026-09-09

AI Summary

为解决气液两相流中空隙率估计问题，提出VFNet模型，利用多视角视频通过时空神经网络提取特征并融合，以实现精准预测。

Abstract

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.

Problem

• Void Fraction
• Two-Phase Flow
• Spatio-Temporal Model

Innovation

• Dual-branch spatio-temporal neural network
• Synchronized multi-view videos
• Void fraction estimation
• Non-intrusive sensing