The aim of this work is to perform an uncertainty analysis on the expected frequency calculated for a selection of reference accident sequences, defined through an ET model built for the International Thermonuclear Experimental Reactor (ITER) Primary Heat Transfer Systems (PHTS). This is in order to add credibility to the event tree (ET) model quantification, to calculate frequency distributions for the occurrence of events and, consequently, to assess if representative sequences have been correctly selected on the probability point of view. The analysis has been performed through Monte Carlo simulations using the RISK SPECTRUM software. The present probabilistic approach to uncertainty quantification consists of treating the input parameters like failure rate or unavailability on demand as random variables with a specified probability distribution and propagating the uncertainty using Monte Carlo simulations. Uncertainty parameters and distribution type of single event frequency have been set. End event frequency has been calculated 10000 times, each time with a Monte Carlo simulated set of reliability parameters: the results obtained from the calculations give the 90% confidence interval of the frequency and therefore an estimation of the correctness of the sequence categorisation.

Uncertainty Analysis on Selection of Representative Accident Sequences

Pinna, T.;Burgazzi, L.
1999

Abstract

The aim of this work is to perform an uncertainty analysis on the expected frequency calculated for a selection of reference accident sequences, defined through an ET model built for the International Thermonuclear Experimental Reactor (ITER) Primary Heat Transfer Systems (PHTS). This is in order to add credibility to the event tree (ET) model quantification, to calculate frequency distributions for the occurrence of events and, consequently, to assess if representative sequences have been correctly selected on the probability point of view. The analysis has been performed through Monte Carlo simulations using the RISK SPECTRUM software. The present probabilistic approach to uncertainty quantification consists of treating the input parameters like failure rate or unavailability on demand as random variables with a specified probability distribution and propagating the uncertainty using Monte Carlo simulations. Uncertainty parameters and distribution type of single event frequency have been set. End event frequency has been calculated 10000 times, each time with a Monte Carlo simulated set of reliability parameters: the results obtained from the calculations give the 90% confidence interval of the frequency and therefore an estimation of the correctness of the sequence categorisation.
Analisi sistemi e di sicurezza
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12079/3684
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