Thermofluid Dynamics of Boiling in Microchannels

Saha, Sujoy Kumar; Celata, Gian Piero; Kandlikar, Satish G.

doi:10.1016/b978-0-12-381529-3.00002-5

Cited by 14 publications

(1 citation statement)

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“…The prediction of wall temperature and heat flux during boiling phenomena is a critical aspect in numerous engineering applications, such as in the design and operation of heat transfer systems like heat exchangers or water-cooled nuclear reactors [1][2][3]. This prediction is particularly significant at the Onset of Nucleate Boiling (ONB), which occurs when vapor bubbles start to form and grow on a heated surface, resulting in a complex heat transfer phenomenon that has been extensively studied but is still not fully understood [4][5][6]. These bubbles, as they initiate and expand, greatly enhance the heat transfer capabilities, effectively extracting heat from the surface [7].…”

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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