A Bayesian ensemble of sensitivity measures for severe accident modeling

Hoseyni Seyed Mohsen (1) , Francesco Di Maio (1) , Matteo Vagnoli (1) , Enrico Zio (2) , Mohammad Pourgol-Mohammad (3)

1 Dipartimento di Energia [Milano]
2 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec
3 Department of Mechanical Engineering

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).

Mots clés: Nuclear Safety; Severe accident modelling; Sensitivity Analysis; Finite Mixture Models; Bayesian Ensemble; Bootstrap Method

Domaines: Energie électrique

Hoseyni Seyed Mohsen, Francesco Di Maio, Matteo Vagnoli, Enrico Zio, Mohammad Pourgol-Mohammad. A Bayesian ensemble of sensitivity measures for severe accident modeling. Nuclear Engineering and Design, 2015, 295, pp.182-191. ⟨10.1016/j.nucengdes.2015.09.021⟩. ⟨hal-01265901⟩