Development of a Generalized Critical Heat Flux Correlation through the Alternating Conditional Expectation Algorithm

Abstract

A new critical heat flux (CHF) correlation has been developed by using the alternating conditional expectation (ACE) algorithm, which yields an optimal relationship between a dependent variable and multiple independent variables. In general, CHF correlation development requires tedious and time-consuming effort because it involves multivariate nonlinear regression analysis. For this reason, existing CHF correlations are usually applicable to specific, and often narrow, ranges of physical parameters. The ACE algorithm is applied to a collection of 12879 CHF data points for forced convective boiling in vertical tubes, and a generalized correlation covering a broad range of flow parameters is obtained. The mean, root mean square, and maximum errors of our new correlation are -0.558, 12.5, and 122.6%, respectively. Our CHF correlation represents the entire set of CHF data with an overall accuracy equivalent to or better than that of three existing correlations. Our results are particularly superior in the high-pressure region covering the rated conditions of pressurized water reactors, as well as in the low-pressure region.