ACE Surrogate Model–Based Uncertainty and Sensitivity Analysis Methods for Severe Accident Codes

Abstract

This paper explores the alternating conditional expectation (ACE) algorithm-based surrogate model to advance the state-of-practice in uncertainty and sensitivity analysis methodologies for severe accident codes. For engineering purposes, the ACE algorithm has been used as an alternative means to find the optimal functional forms of the multiple input variables and response variables of interest. Analysis results here demonstrate that compared with the reference cases the proposed surrogate model provides much higher performance in terms of the coefficient of determination (R2) and normalized root mean square error (NRMSE), thus giving more robust insights into the relationship and correlation between the input parameters and figures of merit (FOMs) of interest. Relevant results and insights are summarized in terms of points of interest.