Binarization of the gray scale images of droplets during dropwise condensation on textured surfaces

Barati, Solmaz Boroomandi; Martin, Helene; Pinoli, Jean‐Charles; Valette, S.; Gavet, Yann

doi:10.1117/12.2521447

Cited by 1 publication

(2 citation statements)

References 4 publications

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“…For the automatic monitoring of droplets on superhydrophobic surfaces, prior researches turned to image processing, , employing images obtained by optical microscope systems. , However, these conventional computer-vision-based methods face limitations in detecting droplets with high accuracy and in real-time, due to the sensitivity to factors like surface morphology, illumination techniques, and camera angles . Recently, deep learning-based detection methods have made significant breakthroughs − and have been applied to detecting droplets on the silicon nanowire surface .…”

Section: Introductionmentioning

confidence: 99%

“…8,12−14 However, developing a real-time monitoring tool is challenging due to various surface morphologies, 15−17 extreme spatial scale or temporal resolution, 18 and the complex nature of droplet interactions. 13 For the automatic monitoring of droplets on superhydrophobic surfaces, prior researches turned to image processing, 19,20 employing images obtained by optical microscope systems. 21,22 However, these conventional computervision-based methods face limitations in detecting droplets with high accuracy and in real-time, due to the sensitivity to factors like surface morphology, illumination techniques, and camera angles.…”

Section: ■ Introductionmentioning

confidence: 99%

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Deep Learning Enabled Comprehensive Evaluation of Jumping-Droplet Condensation and Frosting

Chen,

Shi,

Kang

et al. 2024

ACS Appl. Mater. Interfaces

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Superhydrophobicity-enabled jumping-droplet condensation and frosting have great potential in various engineering applications, ranging from heat transfer processes to antifog/frost techniques. However, monitoring such droplets is challenging due to the high frequency of droplet behaviors, cross-scale distribution of droplet sizes, and diversity of surface morphologies. Leveraging deep learning, we develop a semisupervised framework that monitors the optical observable process of condensation and frosting. This system is adept at identifying transient droplet distributions and dynamic activities, such as droplet coalescence, jumping, and frosting, on a variety of superhydrophobic surfaces. Utilizing this transient and dynamic information, various physical properties, such as heat flux, jumping characteristics, and frosting rate, can be further quantified, conveying the heat transfer and antifrost performances of each surface perceptually and comprehensively. Furthermore, this framework relies on only a small amount of annotated data and can efficiently adapt to new condensation conditions with varying surface morphologies and illumination techniques. This adaptability is beneficial for optimizing surface designs to enhance condensation heat transfer and antifrosting performance.

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