Gas-liquid two-phase flow pattern analysis based on multiscale symbolic transfer entropy

This paper presents a multiscale symbolic transfer entropy (MSTE) to extract the features of gas- liquid two-phase flow and distinguish flow patterns effectively. The role of the MSTE in typical chaotic time series is investigated. Then the characteristics of the flow patterns about three gas- liquid two-phase flows are analyzed from the perspective of causal analysis. The results show that the MSTE can identify different flow patterns and characterize the dynamic characteristics of flow patterns, providing a new method for identifying two-phase flow accurately. In addition, the MSTE reduces the influence of noise to a certain extent and preserves the dynamic characteristics based on simplifying the original sequence. Compared with traditional algorithm, the MSTE has fast calculation speed and anti-interference characteristics and can express the essential features well.