Development of the Boated Length to Diameter Correction Factor on Critical Heat Flux Using the Artificial Neural Networks

With using artificial neural networks (ANNs), an analytical study related to the heated length effect on critical heat flux(CHF) has been carried out to make an improvement of the CHF prediction accuracy based on local condition correlations or table. It has been carried out to suggest a feasible criterion of the threshold length-to-diameter (L/D) value in which heated length could affect CHF. And within the criterion, a L/D correction factor has been developed through conventional regression. In order to validate the developed L/D correction factor, CHF experiment for various heated lengths have been carried out under low and intermediate pressure conditions. The developed threshold L/D correlation provides a new feasible criterion of L/D threshold value. The developed correction factor gives a reasonable accuracy fur the original database, showing the error of -2.18% for average and 27.75% for RMS, and promising results for new experimental data.