Abstract
Abstract 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
