A Deep Learning Perspective on Dropwise Condensation. Issue 22 (24th September 2021)

• Record Type: Journal Article
• Authors: Suh, Youngjoon; Lee, Jonggyu; Simadiris, Peter; Yan, Xiao; Sett, Soumyadip; Li, Longnan; Rabbi, Kazi Fazle; Miljkovic, Nenad; Won, Yoonjin
• Is Part Of: Advanced science. Volume 8:Issue 22(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-24
• Subjects: AI computer vision -- deep learning -- droplet statistics -- dropwise condensation -- real‐time heat transfer mapping; Science -- Periodicals; 505
• DOI: 10.1002/advs.202101794
• Languages: English
• ISSNs: 2198-3844

Abstract

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. Abstract : A vision‐based framework utilizing artificial intelligence is proposed to meet the challenges in acquiring physical descriptors of dropwise condensation. Using this framework, the study investigates the relationship between droplet statistics and heat and mass transfer with unprecedented spatio‐temporal resolutions. The results show the importance of codesigning heat transfer rate per droplet and droplet number density to optimize heat transfer performance.