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Deep learning-based automated and universal bubble detection and mask extraction in complex two-phase flows.

Yewon Kim1, Hyungmin Park2,3

  • 1Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Korea.

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|April 27, 2021
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Summary
This summary is machine-generated.

This study introduces an automated tool using Mask R-CNN for precise bubble detection and mask extraction in gas-liquid two-phase flows, significantly improving efficiency and accuracy over traditional methods.

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Area of Science:

  • Fluid dynamics
  • Multiphase flow analysis
  • Image processing

Background:

  • Accurate measurement of interfacial shapes is crucial for analyzing transport phenomena in multiphase flows.
  • Conventional image processing techniques for bubble detection require manual thresholding, leading to high costs and dependence on user expertise.
  • These methods often lack universality across different experimental conditions.

Purpose of the Study:

  • To develop an automated and universally applicable tool for bubble detection and mask extraction in gas-liquid two-phase flows.
  • To overcome the limitations of traditional image processing methods, such as trial-and-error parameter optimization and high processing costs.
  • To enhance the accuracy and efficiency of interfacial shape analysis in multiphase flow research.

Main Methods:

  • Training a Mask R-CNN model on a rigorously optimized dataset for bubble detection and mask extraction.
  • Utilizing a customized weighted loss function to extend the detectable bubble size range, especially for smaller bubbles.
  • Validating the model's performance on various bubbly and bubble-swarm flow conditions.

Main Results:

  • Achieved an Average Precision (AP50) of 98% in validation tests.
  • Demonstrated detection of over 95% of bubbles in unseen bubble-swarm flows, outperforming conventional methods.
  • The automated tool offers more than twice the processing speed for mask extraction compared to traditional approaches, excluding parameter tuning time.

Conclusions:

  • The developed Mask R-CNN-based tool provides a highly accurate and efficient solution for bubble detection and mask extraction in gas-liquid two-phase flows.
  • The tool's universal applicability and speed offer significant advantages over conventional image processing techniques.
  • The open-source availability of the tool (https://github.com/ywflow/BubMask) facilitates its adoption in the research community.