New correlations for the prediction of incipient nucleate boiling in a one-side heated swirl tube

Lim, Ji Hwan; Park, Minkyu; Shin, Seong Min; Chung, Sukmin

doi:10.1016/j.applthermaleng.2022.118300

Cited by 9 publications

(3 citation statements)

References 72 publications

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“…Similarly, Liu et al [14] formulated an analytical model based on experimental work to predict heat flux and bubble size at the ONB, incorporating various parameters such as fluid inlet conditions, subcooling, contact angle, microchannel dimensions, and fluid exit pressure. More recently, Lim et al [15] explored the onset of nucleate boiling in a swirl tube using dimensional analysis, while Al-Yahia and Jo [16] investigated the influence of mass flux on nucleation site density, bubble departure, and heat flux, proposing a new correlation with an error of ±16.5%.…”

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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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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“…Zhu [7] proposed a modified Liu and Winterton correlation for the entire subcooled boiling region to predict the heat transfer coefficients in swirl flow under high and non-uniform heat fluxes. Ji [8] explored the onset of nucleate boiling (ONB) of a one-side heated swirl tube and evaluated the prediction accuracy of the existing ONB correlations according to the experimental results. Tao [9] proposed a thermal dynamic simulation method based on the Eulerian multiphase method for swirl tubes under the subcooled boiling condition, considering the factors of the interphase transfer of energy and mass and the wall heat flux partitioning model.…”

Section: Introductionmentioning

confidence: 99%

“…Tao [9] proposed a thermal dynamic simulation method based on the Eulerian multiphase method for swirl tubes under the subcooled boiling condition, considering the factors of the interphase transfer of energy and mass and the wall heat flux partitioning model. These studies show that whether the design parameters are reasonable or not has an important effect on the heat transfer performance of the HHF components of the fusion reactor [3][4][5][6][7][8][9][10][11][12][13]. The enhanced heat transfer performance optimization of HHF components of fusion devices is a multi-variable and multi-constraint optimization process, which has many design parameters, high CFD simulation cost, and low efficiency.…”

Section: Introductionmentioning

confidence: 99%

Efficient Sensitivity Analysis for Enhanced Heat Transfer Performance of Heat Sink with Swirl Flow Structure under One-Side Heating

Lei

Xie

2022

Energies

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Excellent heat transfer performance has increasingly become a key issue that needs to be solved urgently in the development process of large-scale fusion equipment. The study of heat transfer performance improvement to scientifically and reasonably determine the design parameters of the high heat flow (HHF) components of fusion reactors based on the efficient in-depth analysis of the heat transfer mechanism and its sensitive factors is of great significance. In this paper, a liquid-vapor two-phase flow model with subcooled boiling for a large length-diameter ratio swirl tube structure in the HHF calorimeter component is proposed to analyze the effects of key design parameters (such as inlet temperature of cooling water flow, swirl tube structure parameters, etc.) on its heat transfer performance. Then, considering the high computational cost of the liquid-vapor two-phase flow model, and in order to improve the efficiency of the sensitivity analysis of these design parameters, the polynomial response surface surrogate model of heat transfer performance function was constructed based on Latin hypercube sampling. On this basis, by combining the proposed surrogate model, the sensitivity index of each design parameter could be obtained efficiently using the Sobol global sensitivity analysis method. This method could greatly improve the calculation efficiency of the design parameter sensitivity analysis of HHF components in the fusion reactor, which provides vital guidance for the subsequent rapid design optimization of related components.

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