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

•A large database for flow condensation heat transfer in mini/micro-channels is amassed.•Four machine learning algorithms are developed: ANN, AdaBoost, Random Forest, and XGBoost.•ANN and XGBoost models predict test data with MAE of 6.8% and 9.1%, respectively.•Optimal models performed better than p...

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Bibliographic Details
Published in:	International journal of heat and mass transfer Vol. 162; p. 120351
Main Authors:	Zhou, Liwei, Garg, Deepak, Qiu, Yue, Kim, Sung-Min, Mudawar, Issam, Kharangate, Chirag R.
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier Ltd 01.12.2020 Elsevier BV
Subjects:	Algorithms Computational fluid dynamics Condensation Condensers (liquefiers) Data points Fluid flow Gradient boosted trees Heat transfer Heat transfer coefficients Machine learning Mathematical models Microchannels Model accuracy Neural networks Optimization Reynolds number Working fluids Gradient boosted trees Neural networks Condensation Heat transfer Machine learning
ISSN:	0017-9310, 1879-2189
Online Access:	Get full text
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