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Characterizing gas-liquid two-phase flow behavior using complex network and deep learning.

Meng-Yu Li1, Rui-Qi Wang2, Jian-Bo Zhang1

  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.

Chaos (Woodbury, N.Y.)
|February 1, 2023
PubMed
Summary
This summary is machine-generated.

Researchers developed a novel complex network approach to analyze gas-liquid two-phase flow dynamics. This method effectively reveals flow evolution and achieves 95.83% accuracy in flow structure recognition using a temporal-spatio convolutional neural network.

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Area of Science:

  • Fluid Dynamics
  • Complex Systems Science
  • Signal Processing

Background:

  • Characterizing polymorphic and unstable gas-liquid two-phase flow is a significant challenge in multiphase flow research.
  • Existing methods struggle to fully capture the dynamic evolution of complex flow structures.

Purpose of the Study:

  • To develop and validate a novel method for characterizing gas-liquid two-phase flow evolution using complex network analysis.
  • To enhance flow structure recognition accuracy through advanced computational techniques.

Main Methods:

  • Dynamic experiments of gas-liquid two-phase flow in a vertical tube using a four-sector distributed conductivity sensor.
  • Adaptive optimal kernel time-frequency representation of multi-channel signals.
  • Complex network construction based on time-frequency energy distribution and global clustering coefficient calculation.
  • Development and application of a temporal-spatio convolutional neural network for flow structure recognition.

Main Results:

  • The complex network approach effectively analyzes multi-channel measurement data to reveal gas-liquid two-phase flow evolutionary mechanisms.
  • Global clustering coefficients serve as quantitative indicators for analyzing dynamic behaviors of various flow structures.
  • The temporal-spatio convolutional neural network achieved a high classification accuracy of 95.83% for flow structure recognition.

Conclusions:

  • The proposed complex network methodology provides a robust framework for understanding gas-liquid two-phase flow dynamics.
  • The integrated approach of signal processing, complex networks, and deep learning offers a powerful tool for multiphase flow characterization and recognition.