Risk-Informed Safety Margin Characterization Research Articles

An advanced probability safety margin analysis approach combined deterministic and probabilistic safety assessment

The conventional deterministic safety margin lacks the capability for real safety margin estimation in nuclear power plants (NPPs). To improve the accuracy of safety margin analysis, the risk-informed safety margin characterization (RISMC) approach has been proposed to capture the real safety margin in NPPs. However, existing literature lacks detailed application of the RISMC approach to improving the efficiency of safety margin analysis. To solve this problem, an advanced probability safety margin (APSM) analysis approach is proposed to improve safety margin analysis efficiency. The proposed APSM combines, the best estimate plus uncertainty (BEPU) analysis approach and the probability safety assessment (PSA) approach to screen key sequences. To reduce the number of calculation samples of Monte Carlo approach, the small sample estimation method is used to obtain the conditional exceedance probability (CEP). The proposed approach is subsequently used to estimate the reactor core damage frequency (CDF) of a 900 MW pressurized water reactor (PWR) under a small break lack of coolant accident, and for two reactor power levels. Evaluation results show that the proposed APSM analysis approach efficiently and accurately quantifies the core damage frequency under different reactor statuses. Moreover, the total number of calculation sequences is reduced from 64 to 8 and the total number of iterations is reduced from 8,320 to 520. In addition, the proposed APSM approach is compared with the PSA-only approach under 105% power condition. The comparison result shows that, the CDF obtained by APSM is 41% lower than that of the PSA. Moreover, for the APSM, the core damage frequency increased by 152.98% with core power uprates at 5%.

RISMC industry application #1 (ECCS/LOCA): Core characterization automation: Lattice codes interface for PHISICS/RELAP5-3D

The United States Nuclear Regulatory Commission (U.S. NRC) is currently proposing rule-making “10 CFR 50.46c” to revise the loss-of-coolant-accident (LOCA) and emergency core cooling system (ECCS) acceptance criteria to include the effects of higher burn-up on cladding performance. The key implications of this proposition are that the cladding performance cannot be evaluated anymore as separated from other disciplines, but needs to be evaluated in a coupled manner with other physics. The Risk-Informed Safety Margin Characterization (RISMC) Pathway initiated several programs at Idaho National Laboratory (INL) to support the industry in the transition to the new rule. One of these programs is the Industry Application #1 (IA1). IA1’s goal is to develop an Integrated Evaluation Model (IEM) called LOCA Toolkit for the US (LOTUS). This tool connects five major disciplines involved in LOCA analysis, among them is (automated) core design (CD-A). This paper focuses solely on the design of LOTUS' CD-A module as well as a demonstration application of the CD-A module to a generic Pressurized Water Reactor (PWR). The demonstration of the other LOTUS disciplines together with a demonstration example of the final risk-informed safety margin analysis is published in a separate paper. LOTUS is meant to provide the methodology framework into which the user can plug his established tools and codes. With the CD-A, the plant owner/operator will characterize his core design with the tools he has. He will then integrate his tools/methods into the LOTUS framework for the downstream safety analysis. For the current CD-A demonstration, the tools used are the already coupled codes PHISICS and RELAP5-3D, while cross section generation is done using the Studsvik lattice code HELIOS-2.

Modeling upgrade of probabilistically significant SBO scenarios with uncertainty analysis for the evaluation of system power uprate of limited scope

With the application of risk-informed safety margin characterization methodology, the computational risk assessment approach has become a useful measure to integrate both the traditional PSA and BEPU approaches to perform a risk-informed safety margin evaluation. In the traditional calculation of the event sequence success criteria in a classical PSA modeling, without proper uncertainty analysis a sequence can only be either success (zero) or failure (unity). Consequently, the traditional PSA modeling technique can be insensitive to a design change of limited scope. To make the traditional PSA model sensitive to a minor system impact, the uncertainties of associated stochastic and mechanistic parameters of related probabilistically significant sequences (PSS) need to be analyzed. In this paper, two probabilistically significant SBO sequences of a typical three-loop PWR, namely the loss of heat sink dominant group and the seal failure dominant group, were identified and revised with the consideration of both stochastic and mechanistic uncertainties for further ΔCDF evaluation when system power uprate by 5%.Particularly, to make the revised modeling of dominant SBO sequences more sensitive to a system impact of limited scope, the effective sampling range of the stochastic parameters, such as system power recover time, needs to be narrowed down reasonably. By applying the limit surface method, the effective sampling range of system power recover time lies only within a conditional success time interval defined by the tmin and tmax; when power recover time is early than tmin, the associated PSS will surely success, and vice versa for the time later than tmax. In the future application of the revised PSA computation risk assessment model to evaluate a power uprate of 5%, it was expected that both the tmin and tmax will be quantitatively reduced. Besides, the conditional exceedance probability (Pce) derived from the distribution of PCTs for the conditional success evaluation also will be recalculated. With new branching probabilities and Pce, a new conditional CDF of a SBO PSS can be evaluated for a power rate of limited scope.In our study, it was also demonstrated that with proper uncertainty analysis, the revised CDF of a SBO PSS can even be reduced by a factor of 8.75 to 3.10 compared to traditional PSA approach.

Uncertainty Analysis of the Conditional Exceedance Probability Calculation for a Probabilistically Significant SBO Sequence

The Risk Informed Safety Margin Characterization methodology combines traditional probabilistic safety assessment (PSA) and the best-estimate plus uncertainty approach. Consequently, both stochastic uncertainty and epistemic uncertainty can be taken into overall consideration to evaluate the risk-informed safety margin. Generally, in calculation of the event sequence success criteria in traditional PSA, the result can only be either success (zero) or failure (unity), which is because uncertainties are not properly taken into consideration. In this paper, the conditional exceedance probability (CEP) of a probabilistically significant station blackout sequence of a typical three-loop pressurized water reactor was calculated with the consideration of both stochastic and epistemic uncertainties by using RELAP5. To get the probability density function of the peak cladding temperature (PCT) of a particular sequence and corresponding CEP, random sampling analysis of major plant status parameters and stochastic parameters was performed. It is assumed that the core is damaged when the PCT reaches 1477.6 K. Through the calculation of CEP of this specific sequence, it can be found that core damage will take place in a certain possibility between zero and unity when taking plant status uncertainties and stochastic uncertainties into consideration. Therefore, the core damage frequency (CDF) of any probabilistically significant sequence can be recalculated to get a more precise CEP. With the application of the computational risk assessment method, not only can the conditional CDF be reasonably reduced, but also the revised model can be made sensitive to a system design change of limited scope. Compared to the traditional PSA evaluation without uncertainty analysis, the CDF of the loss–of–heat sink dominant group can be reduced by a factor of 8.75 ( / ).

Efficient Monte Carlo methods for estimating failure probabilities

We develop efficient Monte Carlo methods for estimating the failure probability of a system. An example of the problem comes from an approach for probabilistic safety assessment of nuclear power plants known as risk-informed safety-margin characterization, but it also arises in other contexts, e.g., structural reliability, catastrophe modeling, and finance. We estimate the failure probability using different combinations of simulation methodologies, including stratified sampling (SS), (replicated) Latin hypercube sampling (LHS), and conditional Monte Carlo (CMC). We prove theorems establishing that the combination SS+LHS (resp., SS+CMC+LHS) has smaller asymptotic variance than SS (resp., SS+LHS). We also devise asymptotically valid (as the overall sample size grows large) upper confidence bounds for the failure probability for the methods considered. The confidence bounds may be employed to perform an asymptotically valid probabilistic safety assessment. We present numerical results demonstrating that the combination SS+CMC+LHS can result in substantial variance reductions compared to stratified sampling alone.

Evolution of licensing methodology for large break loss of coolant accident analysis and associated PCT margin quantification

The evaluation of methodology for large break loss of coolant accident (LBLOCA) licensing analysis involves two kinds of methodologies, namely deterministic methodology and risk-informed methodology. According to the existing 10 CFR50.46, LBLOCA is one of the most essential design-basis accidents and a deterministic methodology shall be applied to perform LBLOCA analysis based on a so-called surrogate sequence. Without considering how low this sequence occurrence probability is, this surrogate sequence satisfies all the required licensing assumptions. However, in the to-be-issued 10 CFR 50.46a, the LBLOCA can be categorized as an accident beyond design basis and the margin of peak cladding temperature (PCT) shall be evaluated in a risk-informed manner. Regarding the deterministic LBLOCA methodologies, the PCT margin quantified by the traditional appendix K methodology with conservative appendix K models and bounding plant parameters can be enlarged by 41.5K with the deterministic-realistic hybrid methodology (DRHM), in which realistic plant parameters and statistical uncertainty analysis are utilized. It was noted that the PCT margin released by realistic plant parameters can be about 1/4–1/5 of the margin generated by a full-scoped BEPU methodology with both best-estimate models and realistic plant parameters. Furthermore, by relaxing the licensing sequence assumptions, an additional LBLOCA PCT margin of 44.3–62.2K can be released from the deterministic approaches by applying the risk-informed safety margin characterization (RISMC) methodology. It was also observed from the risk-informed LBLOCA analysis, to have a cumulated occurrence probability over 99% in the load spectrum, the third sequence by order of ascending PCT was referred and the associated sequence occurrence probability of this sequence is 5.07*10−3. While in the deterministic methodology, the occurrence probability of the traditional surrogate or licensing sequence is only 5.46*10−5.

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.

Performance-based ECCS cladding acceptance criteria: A new simulation approach

The U.S. Nuclear Regulatory Commission is currently proposing rulemaking to revise the Loss Of Coolant Accident (LOCA) and therefore the Emergency Core Cooling System (ECCS) acceptance criteria, to include the effects of higher burnup on cladding performance as well as to address other technical issues. As motivated by the new rule, the need to use advanced cladding designs may be a result. A loss of operational margin may result due to the more restrictive cladding embrittlement criteria. Initial and future compliance with the rule may significantly increase vendor workload and licensee cost, as a spectrum of fuel rod initial burnup states may need to be analyzed to demonstrate compliance. Consequently, there will be an increased focus on licensee decision making related to LOCA analysis to minimize cost and impact, and to manage margin.The study here presented has been part of a big project used to investigate technical issues and approaches for future industrial applications within the Risk-Informed Safety Margin Characterization (RISMC) Pathway. Specifically, the primary aim of this study is to lay out a roadmap to demonstrate the application of the new methodology. The present analysis shows a simplified version of the methodology of an industrial application on the Core Design and the Multi-Cycle Analysis.

10 CFR 50.46c Rulemaking: A Novel Approach in Restating the LOCA Problem for PWRs

The U.S. Nuclear Regulatory Commission (NRC) is considering a rulemaking that would revise requirements in 10 CFR 50.46 [also known as the emergency core cooling system (ECCS) rule]. Experimental work sponsored by the NRC suggested that the current regulatory acceptance criteria on ECCS performance during design-basis accidents are actually nonconservative for higher-burnup fuel, that embrittlement mechanisms not contemplated in the original criteria exist, and that the 17% limit on oxidation is not adequate to preserve the level of ductility that the NRC originally deemed to be warranted for adequate protection. The new rule imposes new acceptance criteria and is expected to be in effect within this decade. An implementation plan was developed that will give individual plants up to 7 years with which to comply once the rule is amended, depending on the status of each plant's analysis of record, the effort involved, and existing analytical margin to the limits.The proposed rule may challenge U.S. light water reactor fleet operational flexibility and economics. Within the U.S. Department of Energy Light Water Reactor Sustainability Program, the Idaho National Laboratory is pursuing an initiative that is focused on industry applications using Risk-Informed Safety Margin Characterization (RISMC) tools and methods applied to issues that are of current interest to the operating fleet. The mission of RISMC is to provide cost-beneficial approaches to safety analysis by leveraging modern methods, augmented tools (a combination of existing and new), and repurposed data (existing, but used in a new way).

Risk-informed analysis of the large break loss of coolant accident and PCT margin evaluation with the RISMC methodology

For general design basis accidents, such as SBLOCA and LBLOCA, the traditional deterministic safety analysis methodologies are always applied to analyze events based on a so called surrogate or licensing sequence, without considering how low this sequence occurrence probability is. In the to-be-issued 10 CFR 50.46a, the LBLOCA will be categorized as accidents beyond design basis and the PCT margin shall be evaluated in a risk-informed manner. According to the risk-informed safety margin characterization (RISMC) methodology, a process has been suggested to evaluate the risk-informed PCT margin. Following the RISMC methodology, a load spectrum of PCT for LBLOCA has been generated for the Taiwan’s Maanshan Nuclear Power plant and 14 probabilistic significant sequences have been identified. It was observed in the load spectrum that the conditional PCT generally ascends with the descending sequence occurrence probability. With the load spectrum covering both aleatory and epistemic uncertainties, the risk-informed PCT margin can be evaluated by either expecting value estimation method or sequence probability coverage method. It was found that by comparing with the traditional deterministic methodology, the PCT margin evaluated by the RISMC methodology can be greater by 44–62K. Besides, to have a cumulated occurrence probability over 99% in the load spectrum, the occurrence probability of the sequence referred is about 5.07*10−3, whereas for the traditional surrogate or licensing sequence generally applied in the deterministic methodology, the occurrence probability is only about 5.46*10−5.

BWR Station Blackout: A RISMC Analysis Using RAVEN and RELAP5-3D

The existing fleet of nuclear power plants is in the process of having its lifetime extended and having the power generated from these plants increased via power uprates and improved operations. In order to evaluate the impact of these factors on the safety of the plant, the Risk-Informed Safety Margin Characterization (RISMC) pathway aims to provide insights to decision makers through a series of simulations of the plant dynamics for different initial conditions and accident scenarios. This paper presents a case study in order to show the capabilities of the RISMC methodology to assess the impact of power uprate of a boiling water reactor system during a station blackout accident scenario. We employ a system simulator code, RELAP5-3D, coupled with RAVEN, which performs the stochastic analysis. Our analysis is performed by (a) sampling values from a set of parameters from the uncertainty space of interest, (b) simulating the system behavior for that specific set of parameter values, and (c) analyzing the outcomes from the set of simulation runs.

A dynamic probabilistic safety margin characterization approach in support of Integrated Deterministic and Probabilistic Safety Analysis

The challenge of Risk-Informed Safety Margin Characterization (RISMC) is to develop a methodology for estimating system safety margins in the presence of stochastic and epistemic uncertainties affecting the system dynamic behavior. This is useful to support decision-making for licensing purposes. In the present work, safety margin uncertainties are handled by Order Statistics (OS) (with both Bracketing and Coverage approaches) to jointly estimate percentiles of the distributions of the safety parameter and of the time required for it to reach these percentiles values during its dynamic evolution.The novelty of the proposed approach consists in the integration of dynamic aspects (i.e., timing of events) into the definition of a dynamic safety margin for a probabilistic Quantification of Margin and Uncertainties (QMU).The system here considered for demonstration purposes is the Lead–Bismuth Eutectic- eXperimental Accelerator Driven System (LBE-XADS).

Analyzing simulation-based PRA data through traditional and topological clustering: A BWR station blackout case study

Dynamic probabilistic risk assessment (DPRA) methodologies couple system simulator codes (e.g., RELAP and MELCOR) with simulation controller codes (e.g., RAVEN and ADAPT). Whereas system simulator codes model system dynamics deterministically, simulation controller codes introduce both deterministic (e.g., system control logic and operating procedures) and stochastic (e.g., component failures and parameter uncertainties) elements into the simulation. Typically, a DPRA is performed by sampling values of a set of parameters and simulating the system behavior for that specific set of parameter values. For complex systems, a major challenge in using DPRA methodologies is to analyze the large number of scenarios generated, where clustering techniques are typically employed to better organize and interpret the data. In this paper, we focus on the analysis of two nuclear simulation datasets that are part of the risk-informed safety margin characterization (RISMC) boiling water reactor (BWR) station blackout (SBO) case study. We provide the domain experts a software tool that encodes traditional and topological clustering techniques within an interactive analysis and visualization environment, for understanding the structures of such high-dimensional nuclear simulation datasets. We demonstrate through our case study that both types of clustering techniques complement each other for enhanced structural understanding of the data.

The Development of Dynamic Human Reliability Analysis Simulations for Inclusion in Risk Informed Safety Margin Characterization Frameworks

The United States Department of Energy is sponsoring the Light Water Reactor Sustainability program, which has the overall objective of supporting the near-term and the extended operation of commercial nuclear power plants. One key research and development (R&D) area in this program is the Risk-Informed Safety Margin Characterization pathway, which combines probabilistic risk simulation with thermohydraulic simulation codes to define and manage safety margins. The R&D efforts to date, however, have not included robust simulations of human operators, and how the reliability of human performance or lack thereof (i.e., human errors) can affect risk-margins and plant performance. This paper describes current and planned research efforts to address the absence of robust human reliability simulations and thereby increase the fidelity of simulated accident scenarios.

Application of risk informed safety margin characterization to extended power uprate analysis

In this paper we present some initial results of the application of a risk-informed safety margin characterization (RISMC) approach to the analysis of the impact of an extended power uprate (EPU) on plant safety for selected transient and accident sequences. These initial applications were conducted to demonstrate the feasibility and practicality of using the RISMC approach to analyze the safety impact of EPUs at both a pressurized water reactor (PWR) and a boiling water reactor (BWR). For the PWR application, the analysis focused on the loss of main feedwater (LOMFW) event with failure of auxiliary feedwater (AFW) where feed and bleed (F&B) cooling is required to prevent core damage. For the BWR case study, station blackout (SBO) sequences leading to core damage were analyzed. A consistent and repeatable process was developed and applied to identify those key parameters that would be analyzed. Distributions were constructed to represent the uncertainties associated with each of the key parameters. These distributions were sampled using a Latin Hypercube Sampling (LHS) technique to generate sets of sample cases that were used in the physics simulation runs using the MAAP4 code. Simulation results were evaluated to determine the changes to safety margins which would occur due to the uprated power conditions; the results obtained were then compared to those for the current nominal full power. The results obtained indicate, as expected, that safety margins may be reduced with increases in plant power level. However, for most power uprate levels, these safety margin reductions were found to be small. A limited study of margin recovery strategies was performed for the PWR case that indicated that minor to moderate changes in plant operation or design could be used to recover the safety margin reduction that would occur from the power uprate.

Pilot application of risk informed safety margin characterization to a total loss of feedwater event

In this paper we present the results of application of a risk-informed safety margin characterization (RISMC) approach to the analysis of a loss of feedwater (LOFW) event at a pressurized water reactor (PWR). This application considered a LOFW event with the failure of auxiliary feedwater (AFW) for which feed and bleed cooling would be required to prevent core damage. For this analysis the main parameters which impact core damage for the scenario were identified and probability distributions were constructed to represent the uncertainties associated with the parameter values. These distributions were sampled using a Latin Hypercube Sampling (LHS) technique to generate sets of sample cases to simulate using the MAAP4 code. Simulation results were evaluated to determine the safety margins relative to those obtained using typical probabilistic risk assessment (PRA) modeling (success criteria) assumptions.