A correction method for heated length effect in critical heat flux prediction

A new correction method is developed for the effect of the length-to-diameter (L/D) ratio on critical heat flux (CHF) by applying artificial neural networks and conventional regression techniques to the KAIST CHF data base for water flow in uniformly-heated, vertical round tubes. It consists of two parts: (a) a threshold L/D over which the length effect becomes negligible; and (b) a L/D correction factor for channels with L/D less than the threshold L/D. The proposed correction method is validated with the experimental data in the original database and a new data set obtained from the KAIST intermediate pressure loop. The proposed method will be useful in the following applications: (a) to predict the CHF for short tubes using CHF models which are based on the data for sufficiently long channels; (b) to define the experimental data which can be used for development of local-condition type CHF correlations; and (c) to convert the CHF data from short channels into CHF data for standard long channels for utilization in correlation development.