Boiling

Faghri, Amir; Zhang, Kun

doi:10.1016/b978-0-12-370610-2.50015-5

Cited by 6 publications

(1 citation statement)

References 67 publications

(63 reference statements)

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“…These bubbles, as they initiate and expand, greatly enhance the heat transfer capabilities, effectively extracting heat from the surface [7]. Consequently, nucleate boiling is widely used in various engineering fields due to its high heat transfer coefficients [8], [9]. However, the presence of bubbles can also lead to undesirable effects, such as the formation of vapor film, bubble coalescence, or a significant decrease in heat transfer efficiency once the Critical Heat Flux (CHF) is reached [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Approaches for Boiling ONB Prediction

Cabarcos,

Paz,

Concheiro

et al. 2023

International Conference on Fluid Flow and Thermal Science

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This study investigates the use of Machine Learning models for predicting both wall temperature and heat flux at the Onset of Nucleate Boiling (ONB). The dataset used in this work was obtained from an experimental test bench using Joule heating for boiling generation. Furthermore, five models, including Artificial Neural Networks (ANN), XGBoost, Support Vector Regression, AdaBoost, and Random Forest, were trained and evaluated. Results reveal that AdaBoost performed the worst in both wall temperature and heat flux predictions, indicating limitations in its ability to accurately forecast the ONB parameters. Conversely, the Random Forest model showed signs of overfitting in both predictions, suggesting that it may struggle to generalize to unseen data. In contrast, ANN demonstrated superior performance in predicting wall temperature (with mean square errors of 3.79 °C² and 3.84 °C² for training and testing), while XGBoost outperformed other models in heat flux prediction. Both models successfully captured the complex relationships between inputs (bulk temperature, pressure, channel inclination and velocity) and ONB parameters, leading to accurate predictions.

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