Machine learning-based approach for predicting flow boiling heat transfer coefficient at high saturation temperatures

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24210%2F25%3A00012803" target="_blank" >RIV/46747885:24210/25:00012803 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0735193324013009" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0735193324013009</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.icheatmasstransfer.2024.108538" target="_blank" >10.1016/j.icheatmasstransfer.2024.108538</a>

Result language

angličtina

Original language name

Machine learning-based approach for predicting flow boiling heat transfer coefficient at high saturation temperatures

Original language description

This paper explores the prediction performance of nine (9) machine learning (ML) models at high saturation temperatures for which most empirical models have been reported to fail. Over thousand five hundred data points were carefully compiled from seven different authors utilizing three different working fluids that have been applied or recommended to work well in high temperature applications. Both dimensional and dimensionless features of the dataset were evaluated and compared. Results indicate that machine learning models offer enhanced accuracy compared to empirical models. From the nine machine learning models evaluated, for the case of dimensional features, Gradient Boosting, XGBoost, K-Nearest Neighbor, and Random Forest were the best performing models with Mean Absolute Errors (MAEs) less than 10 % and R-square values over 95 %. In the case of dimensionless features, Gradient Boosting, XGBoost, Random Forest, and Extra Tree were the best-performing models with Mean Absolute Errors (MAEs) less than 10 % and R-square values over 95 %. Overall, XGBoost, Gradient Boosting, and Random Forest were the models that remained resolute in their performance when the data was transformed from dimensional to dimensionless features. Feature importance was also performed to rank the features on how they contributed to the models‘ prediction.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20303 - Thermodynamics

Project

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Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

International Communications in Heat and Mass Transfer

ISSN

0735-1933

e-ISSN

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Volume of the periodical

161

Issue of the periodical within the volume

31 December 2024

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

18

Pages from-to

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UT code for WoS article

001421918300001

EID of the result in the Scopus database

2-s2.0-85213524282