Prediction of Critical Heat Flux by Using Artificial Neural Network

Three artificial neural networks（ANNs） are trained based on three types of databases to predict critical heat flux（CHF） in the present paper.The input parameters of the ANNs are the system pressure,mass flow rate and equilibrium quality/inlet subcooling,and the output is CHF.The detail effects of system pressure,mass flow rate,equilibrium quality and inlet subcooling on CHF are analyzed based on the trained ANNs.The ANNs are applied successfully for the predicting of CHF.The predicted results agree very well with experimental data.The analyzed results show that the ANN with the highest accuracy for predicting CHF is the one based on the type II database in the three types: inlet,local and outlet conditions.