Gas Volume Fraction Measurement and Flow Pattern Identification and Analysis in Gas-Liquid Two-Phase Flow Using a Multi-Source Cross-Attentional Task Network

To address gas-liquid two-phase flow modeling, this study proposes a Multi-source Cross-Attentional Task Network (MCATN) for simultaneous gas volume fraction (GVF) measurement and flow pattern identification. The model features a parallel CNN-Transformer structure for joint extraction of local and global features, improving GVF prediction accuracy. A cross-attention mechanism is employed to fuse multi-source features with adaptive weighting, enhancing sensitivity to critical patterns. Furthermore, multivariate multiscale symbolic entropy (mvMSE) is introduced to extract dynamic complexity features, aiding in the discrimination of subtle flow pattern differences. Experimental results show that MCATN achieves superior performance, with a GVF measurement MAE of 0.0015 and R² of 0.9999, and a flow pattern identification accuracy of 97.65%. Beyond classification and regression tasks, mvMSE also enables flow characteristics analysis by capturing temporal structural variations across multiple time scales, offering deeper insight into the complexity evolution of gas-liquid two-phase flow under varying operating conditions. Overall, MCATN demonstrates strong generalization and robustness in structure and multi-task optimization, providing a comprehensive and interpretable solution for complex flow behavior modeling in marine pipeline systems.