October 1, 2025

Prediction of Boiling Heat Transfer Coefficients for Horizontal Tubes Using Machine Learning Models

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Machine learning models showed improved prediction of boiling heat transfer coefficients compared to traditional correlations.
The random forest model achieved the highest accuracy with an R2 value of 0.894 and a mean relative deviation of 5.65%.
A comprehensive database of 1532 experimental conditions informed the study's machine learning predictions.
Traditional models underperformed notably under variable operating conditions, indicating a need for machine learning approaches.

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Implication

This analysis reveals that machine learning models outperform traditional methods for predicting boiling heat transfer coefficients in various refrigerants.

Cite This Study

Zhu et al. (2025) studied this question.

synapsesocial.com/papers/68dd91cffe798ba2fc498d4a https://doi.org/10.1115/ht2025-156203

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Implication

This analysis reveals that machine learning models outperform traditional methods for predicting boiling heat transfer coefficients in various refrigerants.

Prediction of Boiling Heat Transfer Coefficients for Horizontal Tubes Using Machine Learning Models

Key Points

Discussion

Implication

Cite This Study

Prediction of Boiling Heat Transfer Coefficients for Horizontal Tubes Using Machine Learning Models

Key Points

Discussion

Implication

Cite This Study