A Deep Learning Perspective on Dropwise Condensation (Adv. Sci. 22/2021). Issue 22 (17th November 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: 2021-11-17
• Subjects: Science -- Periodicals; 505
• DOI: 10.1002/advs.202170153
• Languages: English
• ISSNs: 2198-3844

Abstract

Abstract : Dropwise Condensation The relationship between droplet statistics and heat and mass transfer has long remained unclear due to the challenge in quantifying the overwhelming number of rigorous physical descriptors embodied in dropwise condensation. By developing a vision‐based strategy utilizing artificial intelligence, Nenad Miljkovic, Yoonjin Won, and co‐workers show in article number 2101794 that there exists a quantifiable tradeoff between droplet size and density that can be harnessed to develop optimal surface guidelines for achieving stable dropwise condensation. The illustration shows an artificially intelligent droplet that represents the decades‐old statistical mysteries of dropwise condensation. The droplet is opening to reveal the data‐driven relationships between droplet statistics and heat and mass transfer.