Automatic Correlation Generation using the Alternating Conditional Expectation Algorithm

An alternating conditional expectation (ACE) algorithm, a kind of non-parametric regression method, is proposed to generate empirical correlations automatically. The ACE algorithm yields an optimal relationship between a dependent variable and multiple independent variables without any preprocessing and initial assumption on the functional forms. This algorithm is applied to a collection of 12,879 CHF data points for forced convective boiling hi vertical tubes to develop a new critical heat flux (CHF) correlation. The meat 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.