Recognition the Droplets in Gray Scale Images of Dropwise Condensation on Pillared Surfaces

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

doi:10.5121/csit.2017.71109

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…As a result, the alternative approach for instance‐level quantification has been leveraging standard computer vision algorithms such as thresholding, k ‐means clustering, edge detection and Voronoi diagrams. [ 26 , 27 ] These traditional computer vision methods offer swift analysis of multiple objects, but are unsuitable for the analysis of long‐duration condensation experiments because they require strict environmental control and human intervention to extract reliable features from image datasets. [ 28 ]…”

Section: Introductionmentioning

confidence: 99%

“…[9,10] Condensation involves the nucleation of droplets on a surface detection and Voronoi diagrams. [26,27] These traditional computer vision methods offer swift analysis of multiple objects, but are unsuitable for the analysis of long-duration condensation experiments because they require strict environmental control and human intervention to extract reliable features from image datasets. [28] The development of precise and automatic object prototyping of instances that can provide physical descriptors would represent a game-changing innovation for thermofluidic engineering.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Perspective on Dropwise Condensation

et al. 2021

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Condensation is ubiquitous in nature and industry. Heterogeneous condensation on surfaces is typified by the continuous cycle of droplet nucleation, growth, and departure. Central to the mechanistic understanding of the thermofluidic processes governing condensation is the rapid and high-fidelity extraction of interpretable physical descriptors from the highly transient droplet population. However, extracting quantifiable measures out of dynamic objects with conventional imaging technologies poses a challenge to researchers. Here, an intelligent vision-based framework is demonstrated that unites classical thermofluidic imaging techniques with deep learning to fundamentally address this challenge. The deep learning framework can autonomously harness physical descriptors and quantify thermal performance at extreme spatio-temporal resolutions of 300 nm and 200 ms, respectively. The data-centric analysis conclusively shows that contrary to classical understanding, the overall condensation performance is governed by a key tradeoff between heat transfer rate per individual droplet and droplet population density. The vision-based approach presents a powerful tool for the study of not only phase-change processes but also any nucleation-based process within and beyond the thermal science community through the harnessing of big data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deep Learning Perspective on Dropwise Condensation

et al. 2021

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“…These categories are defined using statistics of the grayscale intensities present in the image. Details can be found in: 6 4 different algorithms are proposed, one for each category, based on:…”

Section: Image Processing Technique For the Droplets On Pillared Surfacementioning

confidence: 99%

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

Barati

Martin

Pinoli

et al. 2019

Fourteenth International Conference on Quality Control by Artificial Vision

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“…This improvement is reported even ten times in the literature [3]. The considerable heat transfer capacity of DWC has convinced many investigators to study methods of producing DWC and its underlying phenomena [4][5][6][7][8]. DWC occurs on hydrophobic and superhydrophobic surfaces where the surface is reluctant to attract water.…”

Section: Introductionmentioning

confidence: 99%

Influence of different parameters of preparing self-assembled monolayers on copper surfaces in the dropwise condensation heat transfer: an experimental study

Bahrami

Azizi

Saffari

2019

J Braz. Soc. Mech. Sci. Eng.

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Dropwise condensation (DWC) is one of the phase change heat transfer regimes, which due to its high capacity has received much attention from investigators. To produce DWC, it is necessary to have hydrophobic surfaces. Low surface energy coatings have been usually used to produce hydrophobic surfaces. These coatings due to their low thermal conductivities must have the minimum thickness. Therefore, self-assembled monolayers coatings like stearic acid or 1-octadecanethiol coatings (with thicknesses as high as a few nanometers) have been used in the literature. To create hydrophobic coatings by these materials, the surfaces are immersed in hydrophobic solutions with particular concentrations and in a specific time. There is not any general investigation on the effects of the time and concentration of these coatings on DWC in the literature. In this study, first, a proper apparatus for DWC experiments is designed and fabricated. Then, the effects of concentration and immersing time of the two mentioned materials on DWC phenomena have been studied. Between four coating times and solution concentrations of 1-octadecanethiol, concentration of 0.025 M at coating time of 30 min increases DWC heat transfer coefficient by about 4-7 times with respect to filmwise condensation in different subcooling temperatures. Concentration of 0.0025 M at coating time of 1 hour of stearic acid also increases the heat transfer coefficient by about 3-4 times. As well as the 1-octadecanethiol coating has a more uniform structure and higher heat transfer coefficient with respect to stearic acid coating.

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Recognition the Droplets in Gray Scale Images of Dropwise Condensation on Pillared Surfaces

Abstract: This study deals with developing an image processing algorithm that is able to recognize

Cited by 5 publications

References 10 publications

A Deep Learning Perspective on Dropwise Condensation

A Deep Learning Perspective on Dropwise Condensation

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

Influence of different parameters of preparing self-assembled monolayers on copper surfaces in the dropwise condensation heat transfer: an experimental study

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