Two-phase flow patterns characteristics analysis based on image and conductance sensors

Zhenya Wang; Ningde Jin; Chun Wang; Jinxiang Wang

doi:10.1117/12.806280

13 October 2008 Two-phase flow patterns characteristics analysis based on image and conductance sensors

Zhenya Wang, Ningde Jin, Chun Wang, Jinxiang Wang

Proceedings Volume 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence; 71270Q (2008) https://doi.org/10.1117/12.806280
Event: Seventh International Symposium on Instrumentation and Control Technology, 2008, Beijing, China

Abstract

In order to study the temporal and spatial evolution characteristics of gas-liquid two-phase flow pattern, the two-phase flow monitoring system composed of high-speed dynamic camera and Vertical Multi-Electrode Array conductance sensor (VMEA) was utilized to shoot dynamic images and acquire the conductance fluctuating signals of 5 typical vertical gas-liquid two-phase flow patterns in a 125mm i.d. upward pipe. Gray level co-occurrence matrix (GLCM) was used to extract four time-varying characteristic parameter indices which represented different flow image texture structures and also Lempel-Ziv complexity of them were calculated. Then the transition of flow structure and flow property were comprehensively analyzed, combining the result derived from image information with recurrence plots (RPs) and Lempel-Ziv complexity of conductance fluctuating signals. The study showed that the line texture structure of RPs enabled to indicate flow pattern characteristics; the flow image texture structure characteristic parameters sequence described the variance of flow structure and dynamical complexity of different flow patterns.

Citation Download Citation

Zhenya Wang, Ningde Jin, Chun Wang, and Jinxiang Wang "Two-phase flow patterns characteristics analysis based on image and conductance sensors", Proc. SPIE 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence, 71270Q (13 October 2008); https://doi.org/10.1117/12.806280

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