A systematic approach to estimate an inter-unit common-cause failure probability

Soga, Shota; Higo, Eishiro; Miura, Hiromichi

doi:10.1016/j.ress.2021.107802

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…One of the technical difculties for a probabilistic safety assessment for multi-unit sites is the quantifcation of the inter-unit CCF. Te symmetric assumption of intra-unit CCF analysis becomes invalid when quantifying the inter-unit CCF, and a more systemic approach is needed to deal with the dynamics and the asymmetry that inter-unit CCFs present in PSA analysis [15]. Te approach for estimating the inter-unit CCF utilized in this work is from Jang and Jae [12], which proposed two methods of estimating the inter-unit CCF using Swain's dependency model and the CCF data in the SUPSA.…”

Section: Estimation Of the Inter-unit Ccfmentioning

confidence: 99%

Assessing the Impact of Common Cause Failures on Site Risk within Level 1 Multi-Unit PSA

Coleman

Boafo

Yamoah

et al. 2023

Science and Technology of Nuclear Installations

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Common cause failures (CCFs) may lead to the simultaneous unavailability or failure of numerous components in the nuclear power plant because of the existence of a shared cause when an initiating event disrupts the normal functioning of nuclear power plants. The presence of common cause failures (intra-unit and inter-unit) can be recognized in a multi-unit probabilistic safety assessment (MUPSA) as a crucial dependency factor that can influence accident scenarios and the core damage frequency (CDF), as CCF may affect the availability and proper operation of mitigating systems. Since such failures are likely to significantly undermine the benefits of the concept of redundancy in nuclear power plant systems, it is necessary to identify the CCFs that contribute to the core damage in a multi-unit site and analyse their overall quantitative magnitude and qualitative proportions. In this study, a twin-unit generic pressurized water reactor (PWR) nuclear plant is modeled using the AIMS-PSA software. For the loss-of-offsite-power (LOOP) and station blackout (SBO) events, the site CDF was calculated, and the cut-sets produced by this quantification were examined for the modeled CCF basic events in the fault trees. The quantitative and qualitative contributions of the CCFs to the frequency of site core damage were examined. CCFs in the modeled fault trees contributed to 4.58% to the site CDF of the combined LOOP followed by SBO event. In the LOOP event alone that leads to core damage, the CCF contributed 4.58% to the site CDF while CCFs contributed 17.19% to the site CDF in the SBO event alone that leads to core damage. With CCF events considered in the modeling process, the site CDF estimated with CCF events increased by 7.53% in the combined LOOP followed by SBO event. In the LOOP event alone that leads to core damage, inclusion of CCF events in the modeling increased the site CDF by 7.42%. A 15.66% increase in site CDF was recorded in the SBO event alone that leads to core damage as compared to modeling without CCF events. The results show how crucial the common cause failure contribution is to site CDF. The safety of the nuclear plant at a site is impacted by an increase in site CDF when common cause failures are considered. The various CCF fundamental event compositions and their percentage contributions were explicitly examined by the minimal cut-sets which leads to core damage in the units. In conclusion, this study’s findings can help us better understand how CCFs increase multi-unit site risk and can also act as a starting point for future studies on the qualitative and quantitative categorizations of CCF effects within MUPSA.

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Section: Estimation Of the Inter-unit Ccfmentioning

confidence: 99%

Assessing the Impact of Common Cause Failures on Site Risk within Level 1 Multi-Unit PSA

Coleman

Boafo

Yamoah

et al. 2023

Science and Technology of Nuclear Installations

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“…In 2020, Higo et al developed a method to account for the combined influence of asymmetric and symmetric CCF probabilities by assessing the degree of shared coupling factors [86]. This work was later refined by combining with a gamma factor model to express inter-unit CCF probability [87].…”

Section: Overviewmentioning

confidence: 99%

An Integrated Framework for Risk Assessment of High Safety Significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology and Demonstration

Bao

Svetlana

Park

et al. 2022

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This report documents the activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2022 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a technical basis to support effective and secure DI&C technologies for digital upgrades/designs. A framework was proposed for this strategy, which aims to (1) provide a best-estimate, risk-informed capability to quantitatively and accurately estimate the risk impact of plant modernization, considering the introduction of high safety-significant safety-related (HSSSR) DI&C systems, (2) support and supplement existing risk-informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems, (4) assure the long-term safety and reliability of HSSSR DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant.

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“…[15][16][17][18][19][20]. Recently, Soga et al 21 proposed a systematic approach to approximate an inter-unit CCF probability. Imperfect coverage or unrecovered faults owing to a lack of fault detection or switching failure are prevalent problems in redundant machining systems.…”

Section: Introductionmentioning

confidence: 99%

“…). Recently, Soga et al 21 . proposed a systematic approach to approximate an inter‐unit CCF probability.…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis of standby provision multi‐unit machining systems with varied failures, degradations, imperfections, and delays

Shekhar

Devanda

Kaswan

2023

Quality & Reliability Eng

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This prospective study focuses on analyzing the reliability characteristics of multi‐unit systems with standby provisioning, accounting for failures, degradation, random delays, and probabilistic imperfections. The investigation sheds light on how these unreliable attributes hinder the performance and availability of machining systems. Given the frequent occurrence of these negative attributes within machining systems, their impact on production flow, performance, and resource utilization is significant. Moreover, these unfavorable attributes hinder the adoption of advanced technologies that rely on the continuous availability of machining systems. Thorough research on operational traits serves as a foundation for developing solutions to enhance machining system efficiency and elucidates the underlying causes of machining system failures throughout their performance lifecycle. The reliability analysis employs a state‐of‐the‐art queue‐theoretic approach. In order to model the stochastic behavior of the investigated machine repair problem in a systematic manner, we consider various statistically independent failure modes, including active/standby unit failure, degraded failure, switching failure, and common‐cause failure. The unreliable characteristics of machining systems are further compounded by factors such as imperfect fault coverage, reboot delay, and imperfect repair, necessitating a comprehensive examination to strategically implement preventive, corrective, and predictive measures. The seamless operation of multi‐unit machining systems is essential for the successful integration of advanced technologies such as cloud computing, industry 4.0, and IoT. Failures, delays, degradation, and imperfections within machining systems have detrimental effects on their efficiency and availability, demanding in‐depth investigation. To facilitate numerical experimentation and sensitivity analysis of the reliability aspects of the proposed machining system, we develop performance indices such as system reliability, mean‐time‐to‐failure, and failure frequency. These metrics provide valuable insights for decision‐makers seeking to implement measures that ensure uninterrupted availability of the machining system.

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A systematic approach to estimate an inter-unit common-cause failure probability

Cited by 7 publications

References 17 publications

Assessing the Impact of Common Cause Failures on Site Risk within Level 1 Multi-Unit PSA

Assessing the Impact of Common Cause Failures on Site Risk within Level 1 Multi-Unit PSA

An Integrated Framework for Risk Assessment of High Safety Significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology and Demonstration

Reliability analysis of standby provision multi‐unit machining systems with varied failures, degradations, imperfections, and delays

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