XGBoost algorithm for predicting heat transfer coefficient of saturated flow boiling in mini/micro-channels

•A universal consolidated database of 11,470 pre-dryout data points was constructed from 41 sources covering 23 working fluids and wide operating ranges.•The XGBoost algorithm was applied to predict saturated flow boiling heat transfer coefficients in mini/micro-channels.•PFI and SHAP analyses were...

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Vydáno v:	International journal of heat and mass transfer Ročník 256; s. 128095
Hlavní autoři:	Noh, Hyeonseok, Kim, Jihyeok, Lee, Seunghyun, Kim, Sung-Min, Mudawar, Issam
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.03.2026
Témata:	Extreme gradient boosting Heat transfer coefficient Machine learning Mini/micro-channels Saturated flow boiling XGBoost Mini/micro-channels Extreme gradient boosting Heat transfer coefficient XGBoost Saturated flow boiling Machine learning
ISSN:	0017-9310
On-line přístup:	Získat plný text
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