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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Vydané v:	International journal of heat and mass transfer Ročník 162; s. 120351
Hlavní autori:	Zhou, Liwei, Garg, Deepak, Qiu, Yue, Kim, Sung-Min, Mudawar, Issam, Kharangate, Chirag R.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Elsevier Ltd 01.12.2020 Elsevier BV
Predmet:	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
On-line prístup:	Získať plný text
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