Gas-liquid Flow Pattern Analysis Based on Graph Connectivity and Graph-Variate Dynamic (GVD) Connectivity of ERT

Chao Tan, Ying Shen, Keith Smith, Feng Dong, Javier Escudero

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)1590-1601
Number of pages12
JournalIEEE Transactions on Instrumentation and Measurement
Issue number5
Early online date17 Dec 2018
Publication statusPublished - 1 May 2019


  • Voltage measurement
  • Electrodes
  • Conductivity
  • Tomography
  • Time series analysis
  • Electric potential
  • Time measurement
  • Electrical resistance tomography (ERT)
  • flow pattern recognition
  • gas-water two-phase flow
  • graph-variate dynamic (GVD) connectivity


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