Dataset Information

A Deep Learning Perspective on Dropwise Condensation.

ABSTRACT: 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.

SUBMITTER: Suh Y

PROVIDER: S-EPMC8596129 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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A Deep Learning Perspective on Dropwise Condensation.

Suh Youngjoon Y Lee Jonggyu J Simadiris Peter P Yan Xiao X Sett Soumyadip S Li Longnan L Rabbi Kazi Fazle KF Miljkovic Nenad N Won Yoonjin Y

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20210924 22

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 pos ...[more]

PMID: 34561960

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Project description:Durability of a lubricant-infused surface (LIS) is critical for heat transfer, especially in condensation-based applications. Although LIS promotes dropwise condensation, each departing droplet condensate acts as a lubricant-depleting agent due to the formation of wetting ridge and cloaking layer around the condensate, thus gradually leading to drop pinning on the underlying rough topography. Condensation heat transfer further deteriorates in the presence of non-condensable gases (NCGs) requiring special experimental arrangements to eliminate NCGs due to a decrease in the availability of nucleation sites. To address these issues while simultaneously improving heat-transfer performance of LIS in condensation-based systems, we report fabrication of both fresh LIS and a lubricant-depleted LIS using silicon porous nanochannel wicks as an underlying substrate. Strong capillarity in the nanochannels helps retain silicone oil (polydimethylsiloxane) on the surface even after it is severely depleted under tap water. The effect of oil viscosity was investigated for drop mobility and condensation heat transfer under ambient conditions, i.e., in the presence of NCGs. While fresh LIS prepared using 5 cSt silicone oil exhibited a low roll-off angle (∼1°) and excellent water drop (5 μL) sliding velocity ∼66 mm s-1, it underwent rapid depletion as compared to higher viscosity oils. Condensation performed on depleted nanochannel LIS with higher viscosity oil (50 cSt) resulted in a heat-transfer coefficient (HTC) of ∼2.33 kW m-2 K-1, which is a ∼162% improvement over flat Si-LIS (50 cSt). Such LIS promote fast drop shedding as is evident from the little change in the fraction of drops with diameter <500 μm from ∼98% to only ∼93% after 4 h of condensation. Improvement in HTC was also seen in condensation experiments conducted for 3 days where a steady HTC of ∼1.46 kW m-2 K-1 was achieved over the last 2 days. The ability of reported LIS to maintain long-term hydrophobicity and dropwise condensation will aid in designing condensation-based systems with improved heat-transfer performance.

Dataset Information

A Deep Learning Perspective on Dropwise Condensation.

Publications

A Deep Learning Perspective on Dropwise Condensation.

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