Quenching boiling is widely used in the cooling process of fuel rods after the loss of coolant accident in nuclear reactor.The determination of the minimum film boiling temperature(Tmin)is very important for the safe operation of nuclear reactors.Based on the experimental data in the literature,this paper selects three typical machine learning models:Random Forest(RF),Artificial Neural Network(ANN)and eXtreme Gradient Boosting(XGBoost)to predict Tmin during quenching boiling and conduct a sensitivity analysis of influencing factors.The results show that the machine leaming method can effectively improve the accuracy of Tmin prediction compared to the traditional empirical correlation.Among the models,the RF model exhibits the best predictive performance with a coefficient of determination R2 of 0.9770.By combining the RF model with the Sobol'global sensitivity method,the study identifies the coolant subcooling as the most influential parameter on Tmin,followed by initial wall temperature,while length-diameter ratio,pressure and thermophysical properties have a smaller impact.The findings of this research will provide theoretical guidance for improving the safety of nuclear reactors.
Quenching boilingMinimum film boiling temperatureMachine learningGlobal sensitivity
维普
万方数据
