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Volume 112
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3D flow measurement of droplets in a microchannel based on a dual-camera depth from defocus imaging system
Depth from defocus; Microchannel flow; Dual-channel imaging; Particle tracking velocimetry; Droplet dynamics
a School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
b Shanghai Key Laboratory of Multiphase Flow and Heat Transfer for Power Engineering, Shanghai, 200093, China
10.1016/j.partic.2026.02.025
Volume 112, May 2026, Pages 74-86
Received 24 November 2025, Revised 9 February 2026, Accepted 24 February 2026, Available online 12 March 2026, Version of Record 17 March 2026.
E-mail: zhouwu@usst.edu.cn
Highlights

• Single-lens dual-camera measurement system.

• Particle tracking velocity measurement by defocused particle image.

• Particle depth prediction based on convolutional neural network models.

• The 3D flow characteristics of droplets in the microchannel.


Abstract

To investigate complex transport phenomena in droplet microfluidic systems, this study develops a single-lens dual-channel 3D flow measurement system based on depth from defocus. The system employs a telecentric lens combined with a beam splitter structure, eliminating localization ambiguity by acquiring particle images at different defocus positions. A convolutional neural network (CNN) fusion model achieves 3D particle localization with a depth resolution of approximately 1 μm and depth prediction errors maintained within ±10%. Combined with 3D Particle Tracking Velocimetry (3D-PTV), the system reconstructs the internal flow field within droplets in rectangular microchannels, achieving a velocity measurement error of 8.54%. Experimental results reveal shear-induced circulation near the droplet interface and internal recirculation patterns, elucidating the motion characteristics of droplets confined within microchannels.

Graphical abstract
Keywords
Depth from defocus; Microchannel flow; Dual-channel imaging; Particle tracking velocimetry; Droplet dynamics
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