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A Deep Learning Perspective on Dropwise Condensation
Advanced Science ( IF 14.3 ) Pub Date : 2021-09-24 , DOI: 10.1002/advs.202101794 Youngjoon Suh 1 , Jonggyu Lee 1 , Peter Simadiris 1 , Xiao Yan 2 , Soumyadip Sett 2 , Longnan Li 2 , Kazi Fazle Rabbi 2 , Nenad Miljkovic 2, 3, 4, 5 , Yoonjin Won 1
Advanced Science ( IF 14.3 ) Pub Date : 2021-09-24 , DOI: 10.1002/advs.202101794 Youngjoon Suh 1 , Jonggyu Lee 1 , Peter Simadiris 1 , Xiao Yan 2 , Soumyadip Sett 2 , Longnan Li 2 , Kazi Fazle Rabbi 2 , Nenad Miljkovic 2, 3, 4, 5 , Yoonjin Won 1
Affiliation
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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.
中文翻译：
逐滴凝聚的深度学习视角
冷凝现象在自然界和工业中普遍存在。表面上的非均质冷凝以液滴成核、生长和离开的连续循环为代表。对控制冷凝的热流体过程的机械理解的核心是从高度瞬态的液滴群中快速、高保真地提取可解释的物理描述符。然而，利用传统成像技术从动态对象中提取可量化的测量值对研究人员提出了挑战。在这里，展示了一种基于智能视觉的框架，它将经典热流成像技术与深度学习相结合，从根本上解决这一挑战。深度学习框架可以自主利用物理描述符，并分别以 300 nm 和 200 ms 的极端时空分辨率量化热性能。以数据为中心的分析最终表明，与经典理解相反，整体冷凝性能由每个液滴的传热率和液滴群体密度之间的关键权衡决定。基于视觉的方法提供了一个强大的工具，不仅可以通过利用大数据来研究相变过程，还可以研究热科学界内外的任何基于成核的过程。
更新日期：2021-11-17
中文翻译：
逐滴凝聚的深度学习视角
冷凝现象在自然界和工业中普遍存在。表面上的非均质冷凝以液滴成核、生长和离开的连续循环为代表。对控制冷凝的热流体过程的机械理解的核心是从高度瞬态的液滴群中快速、高保真地提取可解释的物理描述符。然而，利用传统成像技术从动态对象中提取可量化的测量值对研究人员提出了挑战。在这里，展示了一种基于智能视觉的框架，它将经典热流成像技术与深度学习相结合，从根本上解决这一挑战。深度学习框架可以自主利用物理描述符，并分别以 300 nm 和 200 ms 的极端时空分辨率量化热性能。以数据为中心的分析最终表明，与经典理解相反，整体冷凝性能由每个液滴的传热率和液滴群体密度之间的关键权衡决定。基于视觉的方法提供了一个强大的工具，不仅可以通过利用大数据来研究相变过程，还可以研究热科学界内外的任何基于成核的过程。
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