Estimation of the failure probability of a thermal-hydraulic passive system by means of Artificial Neural Networks and quadratic Response Surfaces

In this paper, Artificial Neural Network (ANN) and quadratic Response Surface (RS) empirical regression models are used as fast-running surrogates of a thermal-hydraulic (T-H) system code to reduce the computational burden associated with the estimation of the functional failure probability of a T-H passive system. The ANN and quadratic RS models are constructed on a limited-size set of input/output data examples of the nonlinear relationships underlying the original T-H code; once built, these models are used for performing, in an acceptable computational time, the numerous system response calculations needed for an accurate uncertainty propagation and failure probability estimation. An application to the functional failure analysis of an emergency passive decay heat removal system in a simple steady-state model of a Gas-cooled Fast Reactor (GFR) is presented.