Nuclear Power Plant Probabilistic Safety Research Articles

Nuclear Power Plant Probabilistic Safety Assessment by Fuzzy Approach: Current States, Future Research Directions and Challenges

Nuclear Power Plant Probabilistic Safety Assessment by Fuzzy Approach: Current States, Future Research Directions and Challenges

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.

Digital technologies in nuclear power plant probabilistic safety assessment

The ability of systems, structures and components that provide ultimate heat sink, to perform their functions and to ensure the safety of the nuclear power plant with VVER-1200 reactor in the events of external influences that entail the loss of ultimate heat sink is subject of the study. Necessity of consideration of mentioned events was demonstrated by last events occurred on NPPs worldwide in particular in Fukushima-Daiichi accident. Probabilistic model was developed implementing modern methods for assessment of probabilistic safety values using digital software RiskSpectrumPSA. Calculated probabilistic safety values confirm chosen design solutions and possibility of probabilistic safety assessment application for similar tasks.

Fuzzy probability based event tree analysis for calculating core damage frequency in nuclear power plant probabilistic safety assessment

Probabilistic Safety Assessment (PSA) is an established approach to evaluate safety and performances of nuclear power plants (NPPs). It consists of coupled fault tree analysis (FTA) and event tree analysis (ETA). FTA is used to graphically model possible failure scenarios of the evaluated safety systems and quantify their occurence likelihoods. Meanwhile, ETA is used to graphically show event sequences leading to core damage and quantify their frequencies. Fuzzy concept has been introduced and integrated into FTA to deal with uncertainties raised in basic event reliability evaluation when related historical failure data is limited, such as fuzzy probability – based fault tree analysis (FPFTA). To be coupled with ETA, uncertainties modeled in FPFTA need to be propagated into the quantification of core damage frequency (CDF). The motivation of this study is how to propagate uncertainties raised in FPFTA into the quantification of CDF in NPP PSA. Therefore, the objective of this study is to develop a fuzzy probability – based event tree analysis (FPETA) to assess CDF in NPP PSA. An illustrative case study is provided to demonstrate the feasibility and the applicability of the proposed FPETA. The results have confirmed that proposed FPETA could be used as a reasonable alternative for conventional ETA to quantify CDF when probability distributions are not available. Furthermore, the results also demonstrate that the proposed FPETA can be coupled with FPFTA in NPP PSA.

Analysis of time-dependent failure rate and probability of nuclear component

Reliability data of nuclear component are the input parameter and important to the nuclear power plant probabilistic safety assessment. If the failure is weariness in origin, the failure rate and probability will be not constant and have a time trend. Failure rate and probability have been proved to be increasing with time by reliability mathematics. The generalized linear model is built for Poisson and Binomial distribution and applied to study the time-dependent failure rate and probability of nuclear component. The model is proved to be reasonable by the qualitative graph and Bayesian chi-square statistic methods, which can predict the real time trend of nuclear component failure rate and probability correctly. Only analyzing the time dependent failure rate and probability of nuclear component, we will build more accurate general reliability data to analyze the uncertainty of system reliability.

α-Cut method based importance measure for criticality analysis in fuzzy probability – Based fault tree analysis

Fuzzy probability – based fault tree analysis (FPFTA) has been recently developed and proposed to deal with the limitations of conventional fault tree analysis. In FPFTA, reliabilities of basic events, intermediate events and top event are characterized by fuzzy probabilities. Furthermore, the quantification of the FPFTA is based on fuzzy multiplication rule and fuzzy complementation rule to propagate uncertainties from basic event to the top event. Since the objective of the fault tree analysis is to improve the reliability of the system being evaluated, it is necessary to find the weakest path in the system. For this purpose, criticality analysis can be implemented. Various importance measures, which are based on conventional probabilities, have been developed and proposed for criticality analysis in fault tree analysis. However, not one of those importance measures can be applied for criticality analysis in FPFTA, which is based on fuzzy probability. To be fully applied in nuclear power plant probabilistic safety assessment, FPFTA needs to have its corresponding importance measure. The objective of this study is to develop an α-cut method based importance measure to evaluate and rank the importance of basic events for criticality analysis in FPFTA. To demonstrate the applicability of the proposed measure, a case study is performed and its results are then benchmarked to the results generated by the four well known importance measures in conventional fault tree analysis. The results confirm that the proposed α-cut method based importance measure is feasible to evaluate critical basic events in FPFTA.

Safety margin sensitivity analysis for model selection in nuclear power plant probabilistic safety assessment

The safety assessment of Nuclear Power Plants makes use of Thermal-Hydraulic codes for the quantification of the safety margins with respect to upper/lower safety thresholds, when postulated accidental scenarios occur. To explicitly treat uncertainties in the safety margins estimates within the Risk-Informed Safety Margin Characterization (RISMC) framework, we resort to the concept of Dynamic Probabilistic Safety Margin (DPSM). We propose to add to the framework a sensitivity analysis that calculates how much the Thermal-Hydraulic (TH) code inputs affect the DPSM, in support to the selection of the most proper probabilistic safety assessment method to be used for the problem at hand, between static or dynamic methods (e.g., Event Trees (ETs) or Dynamic ETs (DETs), respectively). Two case studies are considered: firstly a Station Black Out followed by a Seal Loss Of Coolant Accident (LOCA) for a 3-loops Pressurized Water Reactor (PWR), whose dynamics is simulated by a MAAP5 model and, secondly, the accidental scenarios that can occur in a U-Tube Steam Generator, whose dynamics is simulated by a SIMULINK model. The results show that the sensitivity analysis performed on the DPSM points out that an ET-based analysis is sufficient in one case, whereas a DET-based analysis is needed for the other case.

AN INTELLIGENT SYSTEM BY FUZZY RELIABILITY ALGORITHM IN FAULT TREE ANALYSIS FOR NUCLEAR POWER PLANT PROBABILISTIC SAFETY ASSESSMENT

Fault tree analysis for nuclear power plant probabilistic safety assessment is an intricate process. Personal computer-based software systems have therefore been developed to conduct this analysis. However, all existing fault tree analysis software systems only accept quantitative data to characterized basic event reliabilities. In real-world applications, basic event reliabilities may not be represented by quantitative data but by qualitative justifications. The motivation of this work is to develop an intelligent system by fuzzy reliability algorithm in fault tree analysis, which can accept not only quantitative data but also qualitative information to characterized reliabilities of basic events. In this paper, a newly-developed system called InFaTAS-NuSA is presented and its main features and capabilities are discussed. To benchmark the applicability of the intelligent concept implemented in InFaTAS-NuSA, a case study is performed and the analysis results are compared to the results obtained from a well-known fault tree analysis software package. The results confirm that the intelligent concept implemented in InFaTAS-NuSA can be very useful to complement conventional fault tree analysis software systems.

A fuzzy-based reliability approach to evaluate basic events of fault tree analysis for nuclear power plant probabilistic safety assessment

Fault tree analysis has been widely utilized as a tool for nuclear power plant probabilistic safety assessment. This analysis can be completed only if all basic events of the system fault tree have their quantitative failure rates or failure probabilities. However, it is difficult to obtain those failure data due to insufficient data, environment changing or new components. This study proposes a fuzzy-based reliability approach to evaluate basic events of system fault trees whose failure precise probability distributions of their lifetime to failures are not available. It applies the concept of failure possibilities to qualitatively evaluate basic events and the concept of fuzzy sets to quantitatively represent the corresponding failure possibilities. To demonstrate the feasibility and the effectiveness of the proposed approach, the actual basic event failure probabilities collected from the operational experiences of the David–Besse design of the Babcock and Wilcox reactor protection system fault tree are used to benchmark the failure probabilities generated by the proposed approach. The results confirm that the proposed fuzzy-based reliability approach arises as a suitable alternative for the conventional probabilistic reliability approach when basic events do not have the corresponding quantitative historical failure data for determining their reliability characteristics. Hence, it overcomes the limitation of the conventional fault tree analysis for nuclear power plant probabilistic safety assessment.

AN AREA DEFUZZIFICATION TECHNIQUE TO ASSESS NUCLEAR EVENT RELIABILITY DATA FROM FAILURE POSSIBILITIES

Reliability data is essential for a nuclear power plant probabilistic safety assessment by fault tree analysis to assess the performance of the safety-related systems. The limitation of conventional reliability data arises from insufficient historical data for probabilistic calculation. This study describes a new approach to calculate nuclear event reliability data by utilizing the concept of failure possibilities, which are expressed in qualitative natural languages, mathematically represented by membership functions of fuzzy numbers, and subjectively justified by a group of experts based on their working experience and expertise. We also propose an area defuzzification technique to convert the membership function into nuclear event reliability data. The actual event reliability data, which are collected from the operational experiences of the reactor protection system in Babcock & Wilcox pressurized water reactor between 1984 and 1998, are then compared with the reliability data calculated from the new approach. The results show that fuzzy failure rates can be used as alternatives for probabilistic failure rates when nuclear event historical data are insufficient or unavailable for probabilistic calculation. This study also confirms that our proposed area defuzzification technique is a suitable technique to defuzzify failure possibilities into nuclear event reliability data.

A review of the links between nuclear power plant probabilistic safety analysis and operational research

The original concept of operational research (OR) was applied to RAF operations at Strike (née Bomber) Command during and after the Second World War and came under the leadership of the likes of Watson-Watt and Blackett. The tools used in nuclear power plant (NPP) probabilistic safety analysis (PSA) have their origin in OR as developed in the 1950s and 1960s. OR was initially successful because of the use of independent scientists and mathematicians who cut-through the military hierarchy. PSA as proposed by the Nuclear Installations Inspectorate (NII) as part of fault analysis of NPP can also be successful in improving the design and proposed operation, but it can be limited if only foreseen accident sequences are analysed. It is argued that OR can be applied to fault analysis of NPP in option assessment and hazard identification. In particular, synthesis of the experience from both fields would enable better analyses of the types of unforeseen accident sequences that occurred at Chernobyl and Three Mile Island and near-misses (Davis Besse).

A case study of a human reliability assessment for an existing nuclear power plant

This paper concerns a Human Reliability Assessment (HRA) carried out over a period of two years as part of a Nuclear Power Plant Probabilistic Safety Assessment (PSA). The HRA involved all elements of the HRA process, from problem definition and task analysis, through error representation and quantification, to impact assessment, error reduction, quality assurance and documentation. The aim of this paper is to show the methodology of HRA as applied in a real PSA, highlighting various elements of the HRA, and how the HRA interfaced with the PSA. Comments are also given on practical aspects and impacts of the various approaches, and the usefulness of a hybrid HRA/PSA team approach.

An initial appraisal of the event-tree methodology used in the Angra 1 nuclear power station probabilistic safety assessment

The event-tree analyses of the Angra 1 nuclear power plant probabilistic safety assessment were performed by means of the explicit method of event-tree modelling of shared-equipment dependencies, from which some insights were drawn. At first, we discuss in this paper the effects of collapsing equal impact vectors (from the quantification point of view also). The second feature discussed is related to a critical analysis of the dependency matrix concept in view of a counterpart to it—namely, the GO methodology in the context of modelling intersystem dependencies. Finally, we discuss the possibility of using our computerized event-tree analysis methodology for assessing the unavailability of the involved standby safety systems, most of which are recognized to be noncoherent structures.