Multi-unit Probabilistic Safety Assessment Research Articles

Multi-unit PSA Based Risk Evaluation Framework for Utilizing Cross-Tie Systems for Nuclear Power Plants

The Fukushima accident showed that the safety of multiple nuclear power plants (NPPs) at the same site could be jeopardized simultaneously. Since then, many studies have focused on developing strategies to prevent the spread of multi-unit accidents, with numerous countries establishing strategies to use mobile equipment. However, mobile equipment strategies are inherently accompanied by a high degree of uncertainty regarding operation success and duration because multiple organizations and personnel interact in various ways during multi-unit accident situations. Furthermore, supplementing current fixed equipment with additional mobile equipment requires extra resources. Therefore, cross-tie strategies that use currently installed fixed equipment can provide additional means to manage site risk with relatively few additional costs. This study proposes a multi-unit probabilistic safety assessment–based risk evaluation framework for utilizing cross-tie systems in NPPs and a modeling methodology to quantify the effectiveness of the cross-tie strategies. A case study was conducted to evaluate the risk reduction from using cross-tie strategies for emergency diesel generators and alternate AC diesel generators, which are power systems utilized in multi-unit loss of offsite power initiating events. It is expected that the developed framework and methodology can be utilized for other types of cross-tie strategies as well.

Estimation of uncertainty in computation of importance factors and probability of undesirable events for safety-critical multi-unit systems, based on Binary Decisions Diagrams

This paper addresses current needs of the Multi-Unit Probabilistic Safety Assessment (MUPSA) methodology, which consists of a series of eight essential steps for conducting Probabilistic Safety Assessment (PSA) in the context of a multi-unit Nuclear Power Plant (NPP), co-located on a single site. The focus of this study revolves around one of the steps of the MUPSA methodology, which includes the examination of the importance factors and uncertainty analysis. Emphasizing the role of this step within MUPSA, the paper highlights the hypothesis and estimations made, along with the determination of system importance factors and failure frequency. To illustrate the practical application of these concepts, the paper presents three illustrative examples. The first example involves a 3-terminal reliability network, while the second example features an illustrative calculation applied to a system designed for potential use in a real NPP, compared to a Monte-Carlo simulation. Moreover, the second example involves the evaluation of a medium voltage distribution system (MVDS) designed to meet the auxiliary supply requirements of a nuclear power plant. This analysis is performed using a binary decision diagram (BDD), and the results derived from the BDD evaluation are then verified with those obtained through a Monte Carlo simulation for a comprehensive comparative analysis. Finally, an uncertainty analysis is achieved using a BDD reliability model, using the results of fault trees developed by National Company SN Nuclearelectrica S.A. (SNN), Department of Nuclear Safety, in the context of the Coordinated Research Project (CRP- I31031).

ELSMOR – towards European Licensing of Small Modular Reactors: Methodology recommendations for light-water small modular reactors safety assessment

Decarbonization of energy production is key in today’s societies and nuclear energy holds an essential place in this prospect. Besides heavy-duty electricity production, other industrial and communal needs could be served by integrating novel nuclear energy production systems, among which are low-power nuclear devices, like small modular reactors (SMRs). The ELSMOR (towards European Licensing of Small Modular Reactors) European project addresses this topic as an answer to the Horizon 2020 Euratom NFRP-2018-3 call. The consortium includes 15 partners from eight European countries, involving research institutes, major European nuclear companies and technical support organizations. The 3.5-year project, launched in September 2019, investigates selected safety features of light-water (LW) SMRs with focus on licensing aspects. Providing a comprehensive compliance framework that regulators can adopt and operate, the licensing process of such SMRs could be optimized, helping their deployment. In this prospect, as a result of ELSMOR’s work, this article gives an overview of the specific issues that LW-SMRs may bring about in the different domains of nuclear safety, in terms of: Methodological standpoints: safety goals, safety requirements, safety principles (defence-in-depth implementation);Main safety functions of reactivity control, decay heat removal and confinement management;Severe accident management;Other safety issues particular to SMRs: use of shared systems; performing of multi-unit probabilistic safety assessment (PSA); refuelling, spent fuel management, transport and disposal management. In this article, adequate methodologies are developed to deal with these issues and to help assess the safety of LW-SMRs. This work gives a precious synthesis of the safety assessment issues of LW-SMRs and of the associated methodologies developed in the context of the ELSMOR European project.

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

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.

A Pragmatic Approach to Modeling Combinations of Plant Operational States in Multi-Unit Nuclear Power Plant Probabilistic Safety Assessment

One of the technical challenges in multi-unit probabilistic safety assessment (MUPSA) is dealing with numerous combinations of plant operational states (POSs) for each nuclear power plant unit. Since the number of possible combinations of POSs increases exponentially with the number of units, it is impractical to develop separate MUPSA models and assess the risk for each POS combination. This paper proposes a pragmatic approach to modeling combinations of POSs for each reactor unit in MUPSA involving up to 10 reactor units. This approach does not focus on selecting representative POS combinations but rather on screening out non-risk-significant accident sequences in a stepwise manner according to the model quantification results. The effectiveness of the approach is demonstrated by application to cases with four different numbers of units. As a result, in the 2-, 4-, and 6-unit cases, the site and multi-unit core damage frequency (CDF) due to a multi-unit loss of offsite power initiating event are successfully calculated without screening out any accident sequences for each unit. In the 10-unit case, the quantification fails without screening, but it succeeds after reducing the model size by about 43% via the exclusion of the accident sequences in each integrated single-unit model with a CDF contribution of less than 0.1%. The results show that the minimal cut sets obtained in each case cover many POS combinations and that most non-risk-significant POS combinations can be truncated by the cutoff value of 1E-14/yr. In addition, comparing the quantification results according to the stepwise screening criteria shows that the proposed approach can effectively reduce the computational burden in MUPSA without losing much accuracy or realism.

Application of Dynamic Fault Tree Analysis to Prioritize Electric Power Systems in Nuclear Power Plants

Because the scope of risk assessments at nuclear power plants (NPPs) is being extended both spatially and temporally, conventional, or static fault trees might not be able to express failure mechanisms, or they could be unnecessarily conservative in their expression. Therefore, realistic assessment techniques are needed to adequately capture accident scenarios. In multi-unit probabilistic safety assessment (PSA), fault trees naturally become more complex as the number of units increases. In particular, when considering a shared facility between units of the electric power system (EPS), static fault trees (SFTs) that prioritize a specific unit are limited in implementing interactions between units. However, dynamic fault trees (DFTs) can be available without this limitation by using dynamic gates. Therefore, this study implements SFTs and DFTs for an EPS of two virtual NPPs and compares their results. In addition, to demonstrate the dynamic characteristics of the shared facilities, a station blackout (SBO), which causes the power system to lose its function, is assumed—especially with an inter-unit shared facility, AAC DG (Alternate AC Diesel Generator). To properly model the dynamic characteristics of the shared EPS in DFTs, a modified dynamic gate and algorithm are introduced, and a Monte Carlo simulation is adopted to quantify the DFT models. Through the analysis of the DFT, it is possible to confirm the actual connection priority of AAC DG according to the situation of units in a site. In addition, it is confirmed that some conservative results presented by the SFT can be evaluated from a more realistic perspective by reflecting this.

Sensitivity Study on the Correlation Level of Seismic Failures in Seismic Probabilistic Safety Assessments

It is popular that correlated seismic failures spread over the fault tree of a seismic probabilistic safety assessment (PSA) for a nuclear power plant (NPP). To avoid the calculational difficulty of core damage frequency (CDF), the fault tree has been simplified by replacing correlated seismic failures with one typical seismic failure by assuming a full correlation among the correlated seismic failures. Then, the approximate seismic CDF of a seismic single-unit PSA (SUPSA) has been calculated for decades with this simplified SUPSA fault tree. Furthermore, current seismic multi-unit PSAs (MUPSAs) have been performed with imperfect seismic MUPSA models that were generated by combining such imperfect seismic SUPSA fault trees. The authors of this study recently developed a method that can calculate an accurate seismic CDF by converting correlated seismic failures into seismic common cause failures (CCFs). In this study, accurate and imperfect MUPSA models were created and their seismic CDFs were compared. The results of this study show that the seismic CDFs in SUPSA and MUPSA are drastically distorted and safety margins are accordingly distorted when the full correlation assumption is employed. Thus, this study shows that very careful attention should be paid to calculating and interpreting seismic CDFs for the single-unit and multi-unit NPP regulations.

A Method to Avoid Underestimated Risks in Seismic SUPSA and MUPSA for Nuclear Power Plants Caused by Partitioning Events

Seismic probabilistic safety assessment (PSA) models for nuclear power plants (NPPs) have many non-rare events whose failure probabilities are proportional to the seismic ground acceleration. It has been widely accepted that minimal cut sets (MCSs) that are calculated from the seismic PSA fault tree should be converted into exact solutions, such as binary decision diagrams (BDDs), and that the accurate seismic core damage frequency (CDF) should be calculated from the exact solutions. If the seismic CDF is calculated directly from seismic MCSs, it is drastically overestimated. Seismic single-unit PSA (SUPSA) models have random failures of alternating operation systems that are combined with seismic failures of components and structures. Similarly, seismic multi-unit PSA (MUPSA) models have failures of NPPs that undergo alternating operations between full power and low power and shutdown (LPSD). Their failures for alternating operations are modeled using fraction or partitioning events in seismic SUPSA and MUPSA fault trees. Since partitioning events for one system are mutually exclusive, their combinations should be excluded in exact solutions. However, it is difficult to eliminate the combinations of mutually exclusive events without modifying PSA tools for generating MCSs from a fault tree and converting MCSs into exact solutions. If the combinations of mutually exclusive events are not deleted, seismic CDF is underestimated. To avoid CDF underestimation in seismic SUPSAs and MUPSAs, this paper introduces a process of converting partitioning events into conditional events, and conditional events are then inserted explicitly inside a fault tree. With this conversion, accurate CDF can be calculated without modifying PSA tools. That is, this process does not require any other special operations or tools. It is strongly recommended that the method in this paper be employed for avoiding CDF underestimation in seismic SUPSAs and MUPSAs.

A preliminary site risk assessment

ABSTRACT Since the Fukushima accident, interest in the site probabilistic safety assessment (PSA) has increased worldwide. However, the standard process for the site risk assessment (SRA) has not yet been established. In addition, most of these studies have focused on the level 1 and 2 PSA. The offsite consequences of the multi-unit accidents may differ from that of the single-unit accidents, so that the full-scope site PSA is essential. In this paper, the preliminary site risk assessment has been performed. The level 1 multi-unit probabilistic safety assessment (MU-PSA) methodology considering the inter-unit dependency was established and carried out. And the mapping table method was adopted in the level 2 MU-PSA. For the level 3 MU-PSA, the grouping method was adopted to reduce the number of the source-term category combinations. Then, the early fatality risk and the latent cancer risk from the accidents in a site were estimated and the tendency of the site risk was analyzed. The process of performing a full-scope site PSA for a site with more than 2 units of varied reactor types presented through this application could contribute to developing the SRA methodology, risk metrics, and safety goals.

Development of a multi-unit seismic conditional core damage probability model with uncertainty analysis

Various studies about multi-unit probabilistic safety assessment (PSA) have been conducted following the Fukushima accident. In this paper, an intuitive and simple model is developed to supplement the disadvantages of huge models that integrate all units in the same site. Through this framework, pre-assessment can be made for multi-unit events, the results of which can assist risk-informed decision making in a relatively short time. The model deducts multi-unit seismic conditional core damage probability (MUSCCDP) with a Bayesian belief network, and by including seismic inter-unit correlation factors, the model can also represent the extent of simultaneous and identical core damage in multiple units at the same site due to an earthquake. The outcome of the model confirms the impact that seismic intensity and seismic correlation have on MUSCCDP. Moreover, it provides an importance analysis of the components in the units and represents which components are the most vulnerable in multiple units. As the uncertainty analysis of the input data for the model is conducted by changing the distribution of the variables, the results raise the necessity for further uncertainty analyses in multi-unit seismic PSA before accepting the PSA results.

Probability subtraction method for accurate quantification of seismic multi-unit probabilistic safety assessment

Single-unit probabilistic safety assessment (SUPSA) has complex Boolean logic equations for accident sequences. Multi-unit probabilistic safety assessment (MUPSA) model is developed by revising and combining SUPSA models in order to reflect plant state combinations (PSCs). These PSCs represent combinations of core damage and non-core damage states of nuclear power plants (NPPs). Since all these Boolean logic equations have complemented gates (not gates), it is not easy to generate exact Boolean solutions.Delete-term approximation method (DTAM) has been widely applied for generating approximate minimal cut sets (MCSs) from the complex Boolean logic equations with complemented gates. By applying DTAM, approximate conditional core damage probability (CCDP) has been calculated in SUPSA and MUPSA. It was found that CCDP calculated by DTAM was overestimated when complemented gates have non-rare events. Especially, the CCDP overestimation drastically increases if seismic SUPSA or MUPSA has complemented gates with many non-rare events.The objective of this study is to suggest a new quantification method named probability subtraction method (PSM) that replaces DTAM. The PSM calculates accurate CCDP even when SUPSA or MUPSA has complemented gates with many non-rare events. In this paper, the PSM is explained, and the accuracy of the PSM is validated by its applications to a few MUPSAs.

Dependency analysis method for human failure events in multi-unit probabilistic safety assessments

Dependency between human failure events (HFEs) is often analyzed as a part of the conventional human reliability analysis (HRA) process for nuclear power plants (NPPs). None of the existing methods have considered an application to multi-unit (MU) scenarios. In this study, we introduce a novel approach to evaluate the dependencies between HFEs in cases of MU event scenarios. We focus on developing a set of MU HFE dependency evaluation elements and their evaluation criteria based on the framework of the single-unit evaluation elements that have been utilized in the HRA practice for NPPs. The unique MU HFE dependency evaluation elements are defined, and we present a new dependency evaluation tree for qualitative analysis. In addition, important cases are provided to demonstrate the application of the proposed method, including four MU initiating events: MU loss of offsite power, MU loss of condenser vacuum, MU general transient, and MU loss of ultimate heat sink. Quantitative analysis with several illustrative HFEs in MU probabilistic safety assessment (PSA) cutsets are also presented. This method fills the gap of a HFE dependency analysis method in the case of MU PSA.

A source term binning methodology for multi-unit consequence analyses

Public has concerned on a site risk after the Fukushima nuclear accidents. Therefore, multi-unit probabilistic safety assessment (MUPSA) has been researched actively because it is necessary to perform MUPSA for assessing a site risk. Most of the researches performed until now focus on multi-unit Level 1 PSA because it is the most important issue to model dependencies between units. However, multi-unit Level 3 PSA should be researched because many source term category (STC) combinations exist in multi-unit accidents. A binning methodology to group many STCs into fewer groups was developed in this research for considering this problem. First, a qualitative and quantitative logic tree to group similar STCs into a same group were developed, respectively. Second, five methods to designate representative MACCS inputs of each group were developed. Third, a verification procedure for the binning methodology was developed, and the most appropriate method for each logic tree was selected. The scheme of the binning methodology can be applied to any reactor type with several modifications such as the headings of the logic tree. Conclusively, the binning methodology will be an appropriate example of multi-unit Level 3 PSA and important element of a site risk estimation methodology.

A development of methodology for assessing the inter-unit common cause failure in multi-unit PSA model

The inter-unit common cause failure (CCF) is an event that multiple components are failed simultaneously by common factors may existent in nuclear power plants on a site. The inter-unit CCF can be identified as one of the inter-unit dependencies that has an impact on the multi-unit probabilistic safety assessment (MUPSA). For instance, it may adversely affect the accident mitigation in accident scenarios of multiple units. Therefore, the MUPSA model should be developed considering the inter-unit CCF.In this study, we proposed two methods with the Swain's dependency model and the CCF data in the single-unit PSA (SUPSA) to calculate the inter-unit CCF. The Swain's dependency model is used to calculate the conditional probability considering the dependency level between each CCF event in two different units. To perform the case study, the inter-unit CCF events of emergency diesel generators (EDGs) and alternate AC diesel generators (AAC DGs) in 4 units of different pressurized water reactor (PWR) types were analysed in the case of the multi-unit loss of off-site power (MULOOP). It is expected that methodologies in this study will contribute to the multi-unit level 1 PSA model development as well as evaluating the site risk in the future.

A new methodology for modeling explicit seismic common cause failures for seismic multi-unit probabilistic safety assessment

In a seismic PSA, dependency among seismic failures of components has not been explicitly modeled in the fault tree or event tree. This dependency is separately identified and assigned with numbers that range from zero to unity that reflect the level of the mutual correlation among seismic failures.Because of complexity and difficulty in calculating combination probabilities of correlated seismic failures in complex seismic event tree and fault tree, there has been a great need of development to explicitly model seismic correlation in terms of seismic common cause failures (CCFs). If seismic correlations are converted into seismic CCFs, it is possible to calculate an accurate value of a top event probability or frequency of a complex seismic fault tree by using the same procedure as for internal, fire, and flooding PSA.This study first proposes a methodology to explicitly model seismic dependency by converting correlated seismic failures into seismic CCFs. As a result, this methodology will allow systems analysts to quantify seismic risk as what they have done with the CCF method in internal, fire, and flooding PSA.

A pragmatic approach to modeling common cause failures in multi-unit PSA for nuclear power plant sites with a large number of units

One of the major issues in multi-unit probabilistic safety assessment (MUPSA) is how to deal with inter-unit common cause failures (CCFs). Most existing studies on MUPSA have focused on two-unit nuclear power plant (NPP) sites, where it is often not difficult to extend currently available CCF modeling approaches, such as the Alpha Factor and Beta Factor models, to address inter-unit CCFs. However, when considering an NPP site with three or more units, these approaches can be inapplicable or yield overly conservative results. This paper proposes a pragmatic approach to modeling CCFs for application to MUPSA involving a large number of NPP units. Provided here are the criteria for selecting CCF groups for which inter-unit CCFs are considered, as well as the methods for modeling CCF combinations and estimating their probabilities. The effectiveness of the proposed approach is then demonstrated by application to cases with different numbers of identical units as well as to cases where non-identical units are included in MUPSA. Results are also compared with those obtained from existing approaches.

Development of logical structure for multi-unit probabilistic safety assessment

Abstract Site or multi-unit (MU) risk assessment has been a major issue in the field of nuclear safety study since the Fukushima accident in 2011. There have been few methods or experiences for MU risk assessment because the Fukushima accident was the first real MU accident and before the accident, there was little expectation of the possibility that an MU accident will occur. In addition to the lack of experience of MU risk assessment, since an MU nuclear power plant site is usually very complex to analyze as a whole, it was considered that a systematic method such as probabilistic safety assessment (PSA) is difficult to apply to MU risk assessment. This paper proposes a new MU risk assessment methodology by using the conventional PSA methodology which is widely used in nuclear power plant risk assessment. The logical failure structure of a site with multiple units is suggested from the definition of site risk, and a decomposition method is applied to identify specific MU failure scenarios.

Human and organizational factors for multi-unit probabilistic safety assessment: Identification and characterization for the Korean case

Since the Fukushima Daiichi accident, there has been an emphasis on the risk resulting from multi-unit accidents. Human reliability analysis (HRA) is one of the important issues in multi-unit probabilistic safety assessment (MUPSA). Hence, there is a need to properly identify all the human and organizational factors relevant to a multi-unit incident scenario in a nuclear power plant (NPP). This study identifies and categorizes the human and organizational factors relevant to a multi-unit incident scenario of NPPs based on a review of relevant literature. These factors are then analyzed to ascertain all possible unit-to-unit interactions that need to be considered in the multi-unit HRA and the pattern of interactions. The human and organizational factors are classified into five categories: organization, work device, task, performance shaping factors, and environmental factors. The identification and classification of these factors will significantly contribute to the development of adequate strategies and guidelines for managing multi-unit accidents. This study is a necessary initial step in developing an effective HRA method for multiple NPP units in a site.

AIMS-MUPSA software package for multi-unit PSA

The need for a PSA (Probabilistic Safety Assessment) for a multi-unit at a site is growing after the Fukushima accident. Many countries have been studying issues regarding a multi-unit PSA. One of these issues is the problem of many combinations of accident sequences in a multi-unit PSA. This paper deals with the methodology and software to quantify a PSA scenarios for a multi-unit site. Two approaches are developed to quantify a multi-unit PSA. One is to use a minimal cut set approach, and the other is to use a Monte Carlo approach.

Analysis of the technical status of multiunit risk assessment in nuclear power plants

Abstract Since the Fukushima Daiichi nuclear disaster, concern and worry about multiunit accidents have been increasing. Korea has a higher urgency to evaluate its site risk because its number of nuclear power plants (NPPs) and population density are higher than those in other countries. Since the 1980s, technical documents have been published on multiunit probabilistic safety assessment (PSA), but the Fukushima accident accelerated research on multiunit PSA. It is therefore necessary to summarize the present situation and draw implications for further research. This article reviews journal and conference papers on multiunit or site risk evaluation published between 2011 and 2016. The contents of the reviewed literature are classified as research status, initiators, and methodologies representing dependencies, and the insights and conclusions are consolidated. As of 2017, the regulatory authority and nuclear power utility have launched a full-scale project to assess multiunit risk in Korea. This article provides comprehensive reference materials on the necessary enabling technology for subsequent studies of multiunit or site risk assessment.