A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
Deep learning predicts boiling heat transfer
2021
Scientific Reports
AbstractBoiling is arguably Nature's most effective thermal management mechanism that cools submersed matter through bubble-induced advective transport. Central to the boiling process is the development of bubbles. Connecting boiling physics with bubble dynamics is an important, yet daunting challenge because of the intrinsically complex and high dimensional of bubble dynamics. Here, we introduce a data-driven learning framework that correlates high-quality imaging on dynamic bubbles with
doi:10.1038/s41598-021-85150-4
pmid:33692489
pmcid:PMC7970936
fatcat:4kza3lp2tjeyzjycwpkncdp5gm