Deep learning predicts boiling heat transfer

Abstract Boiling 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 challe...

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Autores principales:	Youngjoon Suh, Ramin Bostanabad, Yoonjin Won
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/619782d4fc9644759b6eb386b03e90c7
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id	oai:doaj.org-article:619782d4fc9644759b6eb386b03e90c7
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spelling	oai:doaj.org-article:619782d4fc9644759b6eb386b03e90c72021-12-02T15:54:06ZDeep learning predicts boiling heat transfer10.1038/s41598-021-85150-42045-2322https://doaj.org/article/619782d4fc9644759b6eb386b03e90c72021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85150-4https://doaj.org/toc/2045-2322Abstract Boiling 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 associated boiling curves. The framework leverages cutting-edge deep learning models including convolutional neural networks and object detection algorithms to automatically extract both hierarchical and physics-based features. By training on these features, our model learns physical boiling laws that statistically describe the manner in which bubbles nucleate, coalesce, and depart under boiling conditions, enabling in situ boiling curve prediction with a mean error of 6%. Our framework offers an automated, learning-based, alternative to conventional boiling heat transfer metrology.Youngjoon SuhRamin BostanabadYoonjin WonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Youngjoon Suh Ramin Bostanabad Yoonjin Won Deep learning predicts boiling heat transfer
description	Abstract Boiling 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 associated boiling curves. The framework leverages cutting-edge deep learning models including convolutional neural networks and object detection algorithms to automatically extract both hierarchical and physics-based features. By training on these features, our model learns physical boiling laws that statistically describe the manner in which bubbles nucleate, coalesce, and depart under boiling conditions, enabling in situ boiling curve prediction with a mean error of 6%. Our framework offers an automated, learning-based, alternative to conventional boiling heat transfer metrology.
format	article
author	Youngjoon Suh Ramin Bostanabad Yoonjin Won
author_facet	Youngjoon Suh Ramin Bostanabad Yoonjin Won
author_sort	Youngjoon Suh
title	Deep learning predicts boiling heat transfer
title_short	Deep learning predicts boiling heat transfer
title_full	Deep learning predicts boiling heat transfer
title_fullStr	Deep learning predicts boiling heat transfer
title_full_unstemmed	Deep learning predicts boiling heat transfer
title_sort	deep learning predicts boiling heat transfer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/619782d4fc9644759b6eb386b03e90c7
work_keys_str_mv	AT youngjoonsuh deeplearningpredictsboilingheattransfer AT raminbostanabad deeplearningpredictsboilingheattransfer AT yoonjinwon deeplearningpredictsboilingheattransfer
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Deep learning predicts boiling heat transfer

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