A review of dropwise condensation: Theory, modeling, experiments, and applications

Fil, Bachir El; Kini, Girish; Garimella, Srinivas

doi:10.1016/j.ijheatmasstransfer.2020.120172

Cited by 112 publications

(41 citation statements)

References 164 publications

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“…The ambiguity between the heat transfer and droplet statistics stems from the difficulty in measuring heat flux from condensation experiments operating in low superheats. As mentioned in past studies, [ 55 ] characterizing the heat flux from a condensing surface has posed a challenge for researchers for decades due to the small temperature differences (Δ T = 1−3 K ) that are measured, which are usually within the uncertainty of the thermocouples used for the measurement. The natural difficulty of extracting instance‐level features from surfaces with large droplet desnsities has been an additional bottleneck for researchers.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

A Deep Learning Perspective on Dropwise Condensation

et al. 2021

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“…Self‐propulsion of highly wetting liquids regardless of the surface orientations has the potential to enhance condensation in many industrial processes and reduce the equipment sizes, weights, and costs, such as heat exchanger, refrigeration, organic Rankine cycle, heating, ventilation, and air conditioning (HVAC) systems. [ 1,2 ] However, due to the ultralow surface tensions, highly wetting liquids tend to spread as a thin film and become strongly pinned on solid surfaces. [ 3 ] Hence, it is challenging to promote the rapid removal of those liquid droplets from surfaces.…”

Section: Introductionmentioning

confidence: 99%

Gradient Quasi‐Liquid Surface Enabled Self‐Propulsion of Highly Wetting Liquids

Zhang

Guo

Sarma

et al. 2021

Adv Funct Materials

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Self‐propulsion of highly wetting liquids is important in heat exchanger, air conditioning, and refrigeration systems. However, it is challenging to achieve such a spontaneous motion as these liquids tend to wet all the surfaces due to their ultralow surface tensions. Despite that extensive asymmetric surface structures and gradient chemical coatings are developed for directional droplet transport, they will be flooded and covered by these liquids. Here, this challenge is addressed by creating a gradient quasi‐liquid surface to achieve the self‐propulsion of droplets with surface tensions down to 10.0 mN m−1. Such a surface engineered by tethering flexible polymers with gradient grafting density shows ultralow contact angle hysteresis (<1o) to highly wetting liquids. Thus, the surface can simultaneously provide sufficient driving forces through the gradient wettability and negligible retention forces through the slippery boundary lubrication for spontaneous droplet movement. Moreover, continual self‐propulsion of tiny droplets is achieved by spraying highly wetting liquids in simulated condensation conditions and demonstrates that adding temperature gradient can further accelerate the self‐propulsion. The study provides a new paradigm to promote passive removal of highly wetting droplets, leading to potential impacts in enhancing condensation heat transfer regardless of surface orientations.

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“…[3][4][5][6][7][8][9] In condensation, the condensate either forms a film of liquid on the surface which is called filmwise condensation (FWC), condenses as droplets (DWC), or a combination of both. 10,11 DWC is a phase-change process where vapor changes to liquid in the form of discrete drops on an unwetted surface, 12 which due to its high efficiency, has received much attention from investigators. The surface characteristics are the key factors in determining whether the vapor condenses as droplets, film of liquid, or a combination of both, which eventually plays a vital role in heat transfer characteristics.…”

Section: Introductionmentioning

confidence: 99%

Experimental analysis of dropwise condensation heat transfer on a finned tube: Impact of pitch size

Aminian

Saffari

2021

Proceedings of the Institution of Mechanical Engineers, Part A:

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Condensation is one of the essential processes in diverse industries due to its widespread use in various industrial applications such as power generation, water desalination, and air conditioning. Much research has been conducted to achieve better efficiencies and better heat transfer performances in condensers in the past decades. Condensation is divided into dropwise and filmwise based on the surface free energy, surface roughnesses, and condensate characteristics. This study investigated the influence of the 1-Octadecanethiol coating on vertically grooved copper tube’s condensation heat transfer characteristics. The hydrophobic surfaces have been created using self-assembled monolayers (SAMs) on the pure copper tubes (99.9% Cu). Moreover, four different pitch sizes of 1.5, 2, 2.5, and 3.5 mm have been implemented on the surface. Finally, the heat flux and the heat transfer coefficient as functions of logarithmic mean temperature difference are reported in the result section. For validation, the results obtained from the experiment were compared with available data in the literature, and an acceptable agreement was achieved. According to the results, it was found that the 1.5-mm pitch size has the highest heat flux, and the 3.5-mm pitch size has the lowest heat flux. Additionally, it can be inferred that the maximum heat flux of 696.71 kW/m2 was attributed to the 1.5-mm pitch size for logarithmic mean temperature differences of 64.3 K, which is approximately 1.24 times higher compared to the plain tube.

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A review of dropwise condensation: Theory, modeling, experiments, and applications

Cited by 112 publications

References 164 publications

A Deep Learning Perspective on Dropwise Condensation

A Deep Learning Perspective on Dropwise Condensation

Gradient Quasi‐Liquid Surface Enabled Self‐Propulsion of Highly Wetting Liquids

Experimental analysis of dropwise condensation heat transfer on a finned tube: Impact of pitch size

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