Tu Duong LE DUY, Dominique Vasseur, Mathieu Couplet, Laurence Dieulle, Christophe Bérenguer

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A study on updating belief functions for parameter uncertainty representation in Nuclear Probabilistic Risk Assessment

Abstract

Probabilistic Risk Assessments (PRA) are used to achieve a safe design and operation of Nuclear Power Plants. The impact of uncertainties which may affect PRA results must thus be taken into account in the decision making process. These uncertainties due to the lack of data have been recently seen as mainly epistemic ones and it has been recommended to characterize them by the belief functions of Dempster-Shafer Theory rather than a presumed single probability distribution. The current construction of these functions is based on the data provided by PRA data handbooks using traditional statistical tools like Maximum Likelihood Estimation (MLE). However, this approach is only appropriate when data coming from the operating feedback observations are sufficiently large as required in the MLE approach. Furthermore, when wishing to incorporate other sources of information, such as expert’s opinions, the pooling data of MLE has limits to account for these kinds of information. Therefore, in order to overcome this problem, two alternative perspectives based on the Dempster’s rule of combination and the Generalized Bayesian Theorem for constructing and updating the belief functions in a more effective way will be presented in this paper. These two approaches will be studied for the use in the context of PRA. The comparison of these two approaches with the current method is carried out through a practical example. Some conclusions about the application of these approaches will be drawn.

Keywords

Parameter uncertainty, belief functions, generalized Bayesian theorem, nuclear risk assessment

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Authors’ addresses

Tu Duong LE DUY
1, avenue de Général de Gaulle,
92141 Clamart cedex.

Dominique Vasseur
Risk Management Department RDI
Clamart cedex

Mathieu Couplet
Risk Management Department RDI
Clamart cedex

Laurence Dieulle
Institut Charles Delaunay/LM2S, UMR STMR
Troyes Cedex

Christophe Bérenguer
Institut Charles Delaunay/LM2S, UMR STMR
Troyes Cedex

E-mail addresses

Tu Duong LE DUY		tu_duong.le_duy@utt.fr
Dominique Vasseur		dominique.vasseur@edf.fr
Mathieu Couplet		mathieu.couplet@edf.fr
Laurence Dieulle		laurence.dieulle@utt.fr
Christophe Bérenguer		christophe.berenguer@utt.fr