Abstract
Two-phase flow is widely encountered in process engineering and related scientific research. Understanding flow patterns and their transitions is important to discover the fluid mechanics of two-phase flow. In order to investigate the complexity of horizontal gas–water two-phase flow and accurately identify the flow pattern, a 16-electrode electrical resistance tomography was used to collect the spatial distribution of phase fraction. The experimental data are compressed and treated as a 16-D time series corresponding to the average response of the phase distribution in the field of each exciting electrode, which can be studied with graph-based techniques. Three connectivity metrics—correlation, coherence, and the phase-lag index are extracted from the multivariate time series, which correspond to the amplitude, power, and phase-based connectivity among signals, respectively. Together, these connectivity metrics make a comprehensive description of the characteristics of each flow pattern and reveal the transition process of flow patterns. The dynamic characteristics of typical flow patterns are then analyzed using the method of graph-variate signal analysis named graph-variate dynamic connectivity.
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