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The Recognition Algorithm of Two-Phase Flow Patterns Based on GoogLeNet+5 Coord Attention.

Jinsong Zhang1, Xinpeng Wei1, Zhiliang Wang2

  • 1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.

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|February 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced deep learning algorithm for accurate two-phase flow pattern recognition in microchannels. The improved model achieves over 97% accuracy in identifying both liquid-liquid and gas-liquid flow types.

Keywords:
attention mechanismdeep learning algorithmpattern recognitiontwo-phase flow image

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Area of Science:

  • Fluid Dynamics
  • Artificial Intelligence
  • Microfluidics

Background:

  • Two-phase flow in microchannels is crucial in various industrial applications.
  • Accurate recognition of flow patterns is essential for process control and optimization.
  • Deep learning offers promising approaches for automated flow pattern identification.

Purpose of the Study:

  • To enhance the accuracy of deep learning-based flow pattern recognition in microchannel two-phase flows.
  • To develop a novel algorithm combining GoogLeNet with a five-layer Coord attention mechanism.
  • To validate the model's performance on diverse liquid-liquid and gas-liquid flow datasets.

Main Methods:

  • Utilized GoogLeNet with varied convolutional kernels for multi-scale feature extraction.
  • Integrated a five-layer Coord attention mechanism to strengthen channel and spatial features.
  • Trained and tested the optimized model on datasets of NaAlg-Oil, GaInSn-Water, water-soybean oil, water-lubricating oil, and argon-water flows.

Main Results:

  • The combined algorithm achieved 95.09% accuracy in training and 98.12% in testing for liquid-liquid flows.
  • The model demonstrated over 97% recognition accuracy for both liquid-liquid and gas-liquid flow patterns.
  • The enhanced model effectively identified complex flow regimes in microchannels.

Conclusions:

  • The proposed Coord attention and GoogLeNet algorithm significantly improves two-phase flow pattern recognition accuracy.
  • This method offers a robust solution for automated monitoring and control in microfluidic systems.
  • The validated model shows high potential for real-world applications involving diverse two-phase flows.