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• W4226333978 endingPage "122839" @default.
• W4226333978 startingPage "122839" @default.
• W4226333978 abstract "Minimum film boiling temperature (TMFB) is a crucial parameter in post-critical heat flux (CHF) condition by determining the collapse of stable vapor film on overheated surface. Although many correlations have been suggested with a wide range of experimental data to predict the TMFB considering various parameters, they have failed to accommodate the universal quenching data due to limited regression capability addressing effects of multivariate parameters. Even though several data-driven machine learning models (DDML) showed improved prediction performance, their performance are dramatically degraded in extrapolation conditions, which are unincluded in the training process. To overcome the inherent problems of conventional correlations and DDMLs, physics-informed machine learning-aided frameworks (PIMLAF) for TMFB were developed in this study by combining the conventional correlations (Henry model and Groeneveld-Stewart correlation) and machine learning techniques (multi-layer perceptron and random forest) to overcome their limited regression capability and ‘black-box’ characteristics. For the interpolation condition, it was observed that the Groeneveld-Stewart correlation provides better baseline for TMFB than Henry model, and random forest (RF) model has advantage discovering the pattern between TMFB and input variables with tuning capability. Through benchmark studies corresponding to extrapolation condition of the training dataset, it was concluded that hybrid approach of RF model and Groeneveld-Stewart correlation exhibits the best performance on TMFB prediction for both interpolation and extrapolation conditions compared to standalone machine learning models, Groeneveld-Stewart correlation, and MLP-based PIMLAF. The suggested RF-based PIMLAF is expected to enhance the TMFB prediction performance during reflood phase of loss of coolant accidents in light water reactors." @default.
• W4226333978 created "2022-05-05" @default.
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• W4226333978 creator A5044024451 @default.
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• W4226333978 date "2022-08-01" @default.
• W4226333978 modified "2023-10-13" @default.
• W4226333978 title "Physics-informed machine learning-aided framework for prediction of minimum film boiling temperature" @default.
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• W4226333978 doi "https://doi.org/10.1016/j.ijheatmasstransfer.2022.122839" @default.
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