Sensitivity analysis of local uncertainties in large break loss-of-coolant accident (LB-LOCA) thermo-mechanical simulations

Arkoma, Asko; Ikonen, Timo

doi:10.1016/j.nucengdes.2016.06.002

Cited by 8 publications

(2 citation statements)

References 13 publications

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“…In literature, global methods have been used (a) to conduct “SA” for the input parameters at the level of failure mechanisms with respect to the physical performance outputs 32,44–65 or the component failure probabilities 66,67 and (b) to conduct “IM analysis” for the PRA basic events (i.e. at the level of components) with respect to the system risk.…”

Section: Review Of Existing Sensitivity and Im Analysis Methods In Pramentioning

confidence: 99%

Global importance measure methodology for integrated probabilistic risk assessment

Sakurahara

Reihani

Kee

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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An integrated probabilistic risk assessment framework combines spatio-temporal probabilistic simulations of underlying failure mechanisms with classical probabilistic risk assessment logic consisting of event trees and fault trees. The importance measure methods commonly used in classical probabilistic risk assessment, for example, Fussell–Vesely importance measure and Risk Achievement Worth, generate the ranking of components at the basic event level utilizing the one-at-a-time and local methods. These classical importance measure methods, however, are not sufficient for integrated probabilistic risk assessment where, in addition to the component-level risk importance ranking, the ranking of risk-contributing factors (e.g. physical design parameters) associated with the underlying failure mechanisms is desired. In this research, the global importance measure method is suggested and implemented for the integrated probabilistic risk assessment framework. The global importance measure can account for four key aspects of integrated probabilistic risk assessment, including (a) ranking of input parameters at the failure mechanism level based on the contribution to the system risk metrics, (b) uncertainty of input parameters, (c) non-linearity and interactions among input parameters inside the model, and (d) uncertainty associated with the system risk estimates; therefore, it enhances the accuracy of risk importance ranking when the risk model has a high level of non-linearity, interactions, and uncertainties. This article shows (a) qualitative justifications for the selection of the global importance measure method for the integrated probabilistic risk assessment framework and (b) quantitative proof of concept using three case studies, including two illustrative fault tree examples and one practical application of the integrated probabilistic risk assessment framework for Generic Safety Issue 191 at nuclear power plants (a sump blockage issue following a loss-of-coolant accident).

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Section: Review Of Existing Sensitivity and Im Analysis Methods In Pramentioning

confidence: 99%

Global importance measure methodology for integrated probabilistic risk assessment

Sakurahara

Reihani

Kee

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

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“…Moreover, the phenomena related to the sequence were identified through the LBLOCA PIRT, see [38,39]. Afterwards, and by means of the literature sensitivity analyses see [10,[40][41][42] the parameters and phenomena with a significant relevance were also selected. Finally, 25 uncertain parameters complete the set employed in the analyses.…”

Section: Monte Carlo Sampling and Uncertainty Analysis Of A Lblocamentioning

confidence: 99%

Uncertainty and sensitivity analysis of a PWR LOCA sequence using parametric and non-parametric methods

Zugazagoitia

Queral

Fernández-Cosials

et al. 2020

Reliability Engineering & System Safety

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The Best Estimate Plus Uncertainty (BEPU) approach is being used worldwide for nuclear power plants licensing. This method relies on the use of best estimate models to simulate sequences, evaluating the uncertainties involved. To assess these uncertainties, several methodologies have been developed such as the nonparametric Wilks/Wald method, parametric methods that reconstruct a distribution from the data, or the binomial approach. Additionally, sensitivity analyses can be performed to obtain the correlation of the output-inputs. Finally, a variability analysis of the most influential parameters made to find a combination of parameters that can lead to damage is also useful. In this paper, all previous techniques are described, studied and applied by performing a large Monte Carlo set of simulations of a loss of coolant accident in a pressurized water reactor assessing two figures of merit. The comparison of the different methods show that the most conservative is the Wilks/Wald method; the least conservative is the parametric approach, and in between, the binomial one. The impact of the sample size is also studied for all methods, showing different behaviors for the different approaches.

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Statistical characterization of NPP transients: Application to PWR LBLOCA

Fernández-Cosials

Queral

Zugazagoitia

et al. 2020

Annals of Nuclear Energy

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Sensitivity analysis of local uncertainties in large break loss-of-coolant accident (LB-LOCA) thermo-mechanical simulations

Cited by 8 publications

References 13 publications

Global importance measure methodology for integrated probabilistic risk assessment

Global importance measure methodology for integrated probabilistic risk assessment

Uncertainty and sensitivity analysis of a PWR LOCA sequence using parametric and non-parametric methods

Statistical characterization of NPP transients: Application to PWR LBLOCA

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