A Bayesian ensemble of sensitivity measures for severe accident modeling

In this work, a sensitivity analysis framework is presented to identify the relevant input variables of a severe accident code, based on an incremental Bayesian ensemble updating method. The proposed methodology entails: (i) the propagation of the uncertainty in the input variables through the severe accident code; (ii) the collection of bootstrap replicates of the input and output of limited number of simulations for building a set of finite mixture models (FMMs) for approximating the probability density function (pdf) of the severe accident code output of the replicates; (iii) for each FMM, the calculation of an ensemble of sensitivity measures (i.e., input saliency, Hellinger distance and Kullback-Leibler divergence) and the updating when a new piece of evidence arrives, by a Bayesian scheme, based on the Bradley-Terry model for ranking the most relevant input model variables. An application is given with respect to a limited number of simulations of a MELCOR severe accident model describing the fission products release in the LP-FP-2 experiment of the loss of fluid test (LOFT) facility, which is a scaled-down facility of a pressurized water reactor (PWR).