Machine learning algorithms to predict flow condensation heat transfer coefficient in mini/micro-channel utilizing universal data

By Liwei Zhou, Deepak Garg, Yue Qiu, Sung-Min Kim, I. Mudawar, Chirag R. Kharangate. International Journal of Heat and Mass Transfer, 2020

165 citations recorded by the source corpus.

Abstract Miniature condensers utilizing mini/micro-channel has been recognized as one effective technique for designing a compact heat rejection device. However, because of the complex behaviors in phase-change systems like flow condensation, accurately predicting heat transfer coefficients can be a challenging task. In this study, a large database is utilized to develop machine-learning based models for predicting condensation heat transfer coefficients in mini/micro-channels. A consolidated database of 4,882 data points for flow condensation heat transfer in mini/micro-channels is amassed from 37 sources that includes 17 working fluid, reduced pressures of 0.039 – 0.916, hydraulic diameters of 0.424 mm – 6.52 mm, mass velocities of 50

Subfields: Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/0af423d7daa14e26e4e6f6d2eff1b9bffdf678fe

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In Mechanical Engineering

1. Motong Li
2. Zhibin Yu
3. Bing-yang Cao

Citations in the corpus

• A comprehensive review on microchannel heat sinks for electronics cooling (2023, methodology): “The commonly used ML methods contain artificial neural networks [54], random forests, Gaussian process regression [55], extreme gradient boosting [56] and so on.”