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Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
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Bubble velocimetry using the conventional and CNN-based optical flow algorithms.

Daehyun Choi1, Hyunseok Kim1,2, Hyungmin Park3,4

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

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|July 13, 2022
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Summary
This summary is machine-generated.

New optical flow methods accurately measure bubble velocities in gas-liquid flows, outperforming traditional techniques, especially in high void fraction scenarios. A fine-tuned convolutional neural network (CNN) model shows superior performance for complex bubble dynamics.

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Area of Science:

  • Fluid Dynamics
  • Two-Phase Flow Measurement
  • Optical Measurement Techniques

Background:

  • Accurate measurement of bubble dynamics is crucial for understanding gas-liquid two-phase flows.
  • Conventional Particle Tracking Velocimetry (PTV) faces limitations with high void fractions and complex bubble interactions.

Purpose of the Study:

  • Introduce and validate novel optical flow-based bubble velocimetry methods.
  • Compare the performance of Convolutional Neural Network (CNN)-based and traditional optical flow algorithms against PTV.
  • Assess the effectiveness of these methods in challenging flow conditions, including high void fractions.

Main Methods:

  • Optical flow algorithms, including Lucas-Kanade, Farnebäck, and CNN-based models, were employed.
  • A CNN model was fine-tuned using synthetic bubble images with varied properties (density, diameter, velocity).
  • Methods were validated against Particle Tracking Velocimetry (PTV) in diverse gas-liquid flow regimes.

Main Results:

  • All optical flow methods accurately captured unsteady single bubble velocities.
  • The fine-tuned CNN model provided the most accurate fluctuating velocity components compared to PTV.
  • Optical flow methods successfully measured bubble velocities where PTV failed due to high void fractions and bubble overlap.

Conclusions:

  • Optical flow bubble velocimetry offers a robust alternative to PTV, particularly for high void fraction gas-liquid flows.
  • The fine-tuned CNN-based optical flow method demonstrates superior accuracy and efficiency in capturing complex bubble motions, including overlapped bubbles.