Abstract
In this study, a wavelet neural network (WNN) model for predicting critical heat flux (CHF) is set up. The WNN mode combining the properties of the wavelet transform and the advantages of Artificial Neural Networks (ANN) has some advantages of its globe optimal searching, quick convergence speed and solving non-linear problem. The database used in the analysis is from the 1960’s, including 126 data points which cover these parameter ranges: pressure P=100–1,000 kPa, mass flow rate G=40–500 kgm-2s-1, inlet subcooling ΔTsub=0–35◦C and heat flux Q=20–8,000 kWm-2. The WNN prediction results have a good agreement with experimental data. Simulation and analysis results show that the network model can effectively predict CHF.
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