TY - JOUR
T1 - Gas-liquid flow pattern analysis based on graph connectivity and graph-variate dynamic connectivity of ERT
AU - Tan, Chao
AU - Shen, Ying
AU - Smith, Keith
AU - Dong, Feng
AU - Escudero, Javier
N1 - Funding Information: Manuscript received July 22, 2018; revised October 15, 2018; accepted November 15, 2018. Date of publication December 17, 2018; date of current version April 17, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61473206 and Grant 61611130126 and in part by the Natural Science Foundation of Tianjin under Grant 17JCZDJC38400. The work of K. Smith was supported in part by Health Data Research UK, an initiative funded by UK Research and Innovation Councils, in part by the National Institute for Health Research (England), in part by the UK Devolved Administrations, and in part by leading medical research charities. The Associate Editor coordinating the review process was Ruqiang Yan. (Corresponding author: Chao Tan.) C. Tan and Y. Shen are with the Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, Ministry of Education, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China (e-mail: [email protected]; [email protected]).
Publisher Copyright: © 2018 IEEE.
C. Tan, Y. Shen, K. Smith, F. Dong and J. Escudero, "Gas–Liquid Flow Pattern Analysis Based on Graph Connectivity and Graph-Variate Dynamic Connectivity of ERT," in IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 5, pp. 1590-1601, May 2019, doi: 10.1109/TIM.2018.2884548
PY - 2019/4/17
Y1 - 2019/4/17
N2 - 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.
AB - 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.
KW - electrical resistance tomography (ERT)
KW - flow pattern recognition
KW - gas-water two-phase flow
KW - graph-variate dynamic (GVD) connectivity
KW - electric conductivity
KW - electric potential
KW - electric resistance
KW - fluid mechanics
KW - graph theory
KW - graphic methods
KW - pattern recognition
KW - time series analysis
KW - tomography
KW - multivariate time series
UR - http://www.scopus.com/inward/record.url?scp=85058885030&partnerID=8YFLogxK
U2 - 10.1109/TIM.2018.2884548
DO - 10.1109/TIM.2018.2884548
M3 - Article
AN - SCOPUS:85058885030
SN - 0018-9456
VL - 68
SP - 1590
EP - 1601
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
IS - 5
M1 - 8579243
ER -