Simulation-Informed Probabilistic Methodology for Common Cause Failure Analysis

Sakurahara, Tatsuya; Schumock, Grant; Reihani, Seyed; Kee, Ernie; Mohaghegh, Zahra

doi:10.1016/j.ress.2018.12.007

Cited by 41 publications

(19 citation statements)

References 42 publications

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“…In previous studies by the SoTeRiA laboratory at UIUC [31], Fire I-PRA was applied to a critical fireinduced scenario (i.e., a small-break loss-of-coolant accident due to stuck-open pressurizer valves induced by a switchgear room fire) in an NPP, which improved the realism of PRA, leading to a 50% reduction in the core damage frequency associated with the selected scenario. In the I-PRA framework, the Interface Module can treat multi-level dependencies by using the simulation-informed approach for dependency analysis developed in the previous research by the SoTeRiA Laboratory at UIUC [33].…”

Section: Integrated Pra (I-pra) Methodological Frameworkmentioning

confidence: 99%

“…The equipment thermal damage threshold is also randomly sampled from its distribution, and by comparing the KPM of interest against the damage threshold, the fire-induced equipment damage probability can be computed. Key steps of this calculation process are summarized below, while more details can be found in Sakurahara et al [28,33].…”

Section: Computational Procedures For Computation Of Fire-induced Equimentioning

confidence: 99%

“…In this report, this joint damage probability is calculated based on the simulation outputs considering the impact of the shared fire-induced conditions on the damage state of the two cable trays. More details on the implementation can be found in Sakurahara et al [33].…”

Section: Importance Ranking At the System Risk Levelmentioning

confidence: 99%

“…o The unified computational platform, developed in this research, contributes to more explicit and accurate treatment of dependencies at multiple levels of Fire PRA, such as those at the levels of physical inputs, multiple targets, multiple spurious actuations, and PRA basic events. In the I-PRA framework, the Interface Module can treat multi-level dependencies by using the simulation-informed approach for dependency analysis developed in the previous research by the SoTeRiA Laboratory at UIUC [33].…”

Section: Concluding Remarks: Contributions and Benefitsmentioning

confidence: 99%

See 3 more Smart Citations

Risk Importance Ranking of Fire Data Parameters to Enhance Fire PRA Model Realism

Biersdorf

Bui

Sakurahara

et al. 2020

Self Cite

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Fire is historically and analytically a significant contributor to nuclear power plant risk. The level of fire risk and the methods, tools, and data for modeling this risk are highly debated by experts. One area of debate is the input data used in fire modeling and how to deal with this data's high rate of uncertainty. This report outlines work performed for determining the key parameters causing this uncertainty and how it propagates into nuclear power plant PRA models. This research used the Integrated Probabilistic Risk Assessment (I-PRA) method and paves the way to correctly assess and reduce data uncertainty in fire modeling.

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Section: Integrated Pra (I-pra) Methodological Frameworkmentioning

confidence: 99%

Section: Computational Procedures For Computation Of Fire-induced Equimentioning

confidence: 99%

Section: Importance Ranking At the System Risk Levelmentioning

confidence: 99%

Section: Concluding Remarks: Contributions and Benefitsmentioning

confidence: 99%

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Risk Importance Ranking of Fire Data Parameters to Enhance Fire PRA Model Realism

Biersdorf

Bui

Sakurahara

et al. 2020

Self Cite

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“…11 Therefore, as a more feasible short-term alternative for practical risk-informed applications, the integrated probabilistic risk assessment (I-PRA) framework ( Figure 1) has been developed. [12][13][14][15][16][17][18][19][20][21][22][23] The I-PRA framework (Figure 1) incorporates the underlying science of accident causation (i.e. physical and social failure mechanisms) into classical PRA (plant-specific PRA module in Figure 1) associated with certain areas of safety concern, for example, internal fire 14,15,17,20,22 and Generic Safety Issue 191 (GSI-191), i.e., a sump blockage issue following a loss-of-coolant accident at an NPP.…”

Section: Introductionmentioning

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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Analysis of cascading probabilistic common cause failures

Wang

Lie

Xing

et al. 2023

Quality & Reliability Eng

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Common cause failures may contribute significantly to the reliability of a system and has attracted considerable research attentions. However, the existing models often assumed deterministic failures of components affected by the common cause (CC) and no cascading effects. This paper contributes by modeling cascading probabilistic common cause failures (CPCCFs), where a CC may cause multiple system components to fail with different probabilities and some components may fail in a domino chain manner. An explicit, analytical method is proposed for the reliability analysis of systems subject to CPCCFs, which may be triggered by external or internal CCs. The complex cascading effects are modeled using a directed acyclic graph structure. The proposed method has no limitation on the component time‐to‐failure distributions. Applications and advantages of the proposed method are illustrated through a detailed case study of a safety instrumented system for oil and gas transportation. Correctness of the proposed method is verified by Monte Carlo simulations. The time and space complexity of the method is also studied.

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Simulation-Informed Probabilistic Methodology for Common Cause Failure Analysis

Cited by 41 publications

References 42 publications

Risk Importance Ranking of Fire Data Parameters to Enhance Fire PRA Model Realism

Risk Importance Ranking of Fire Data Parameters to Enhance Fire PRA Model Realism

Global importance measure methodology for integrated probabilistic risk assessment

Analysis of cascading probabilistic common cause failures

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