On the width and mean value of bubble size distributions under subcooled flow boiling

Conde-Fontenla, Marcos; Paz, Carlos; Concheiro, M.; Ribatski, Gherhardt

doi:10.1016/j.expthermflusci.2021.110368

Cited by 11 publications

(1 citation statement)

References 49 publications

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“…1(a) was used to obtain the data utilized for training the machine learning algorithms. This facility, extensively described in previous studies [23], [24], comprises two clearly distinct parts: a study section outlined by a dashed line in Fig. 1(a), and additional subsystems that enable the proper adjustment and measurement of bulk temperature, pressure, and volumetric flow rate.…”

Section: Experimental Setup and Methodologymentioning

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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Section: Experimental Setup and Methodologymentioning

confidence: 99%