Probabilistic safety assessment support for the maintenance rule at Duke Power Company

The Nuclear Regulatory Commission (NRC) published the Maintenance Rule on July 10, 1991 with an implementation date of July 10, 1996 [1]. Maintenance rule implementation at the Duke Power Company has used probabilistic safety assessment (PSA) insights to help focus the monitoring of structures, systems and components (SSC) performance and to ensure that maintenance is effectively performed. This paper describes how the probabilistic risk assessment (PRA)¹ group at the Duke Power Company provides support for the maintenance rule by performing the following tasks: (1) providing a member of the expert panel; (2) determining the risk-significant SSCs; (3) establishing SSC performance criteria for availability and reliability; (4) evaluating past performance and its impact on core damage risk as part of the periodic assessment; (5) providing input to the PRA matrix; (6) providing risk analyses of combinations of SSCs out of service; (7) providing support for the SENTINEL program; and (8) providing support for PSA training. These tasks are not simply tied to the initial implementation of the rule. The maintenance rule must be kept consistent with the current design and operation of the plant. This will require that the PRA models and the many PSA calculations performed to support the maintenance rule are kept up-to-date. Therefore, support of the maintenance rule will be one of the primary roles of the PSA group for the remainder of the life of the plant.     

Seismic probabilistic risk assessment and seismic margins studies for nuclear power plants

This paper reviews the seismic probabilistic risk assessment and seismic margins studies for nuclear power plants in the United States. The techniques employed in these studies are briefly described. A few comments on the evaluation of the fragility of structures and equipment are discussed. Seismic PRA is a systematic process to evaluate the safety of nuclear power plants. In the process, it integrates all the elements such as seismic hazard, component fragility and plant system. Thus, it provides the overall view of the safety of an entire plant under a seismic event.

The major tasks of a seismic PRA such as the evaluation of hazard curves, component fragility and plant system are also present in probabilistic analyses of nonnuclear facilities. The concept and technique embodied in seismic PRA for nuclear power plants can be applied to other types of engineering facilities.     

The impact of assessing lower head integrity on boiling water reactor probabilistic risk assessments

Over the last several decades, much effort has been directed at estimating the likelihood of a large early release of radioactivity during a nuclear accident. This effort has culminated in the Individual Plant Examinations (IPEs) for the over 100 US nuclear power plants and the NUREG 1150 study. The large early release of radioactivity requires core damage with loss of primary containment integrity during the accident. Given a successful reactor scram, early containment failure coupled with a large release of radioactivity will only occur if the reactor core vessel is breached by core debris. Most IPE/PRA studies performed to date have not considered the possibility of quenching core debris in the lower plenum. Consequently, lower head failure is presumed to closely follow the onset of core damage. Therefore, these assessments did not address the role that in-vessel debris retention plays in preserving primary containment integrity, nor do they propose a criterion for evaluating the integrity of the vessel lower head given that core damage has occurred. Yet preserving the vessel lower head integrity is a necessary condition for satisfying the plant design and licensing basis. Therefore, a more complete treatment of the risk associated with nuclear plant operation includes an evaluation of the ability to retain the core debris in-vessel. This paper presents a performance requirement for vessel integrity to be used in probabilistic risk assessments; evaluates the impact the core damage progression and lower plenum quenching models have on the likelihood of terminating the damage progression in-vessel; documents the significant reduction in BWR containment failure probability that can occur when appropriate core damage and lower head quenching models are used; reviews the implications of core debris quenching in the lower head on BWR PRA modeling; argues why crediting the capability to maintain vessel integrity is necessary from a safety point of view. These results and conclusions are derived from consideration of a BWR 4 plant with a 251 inch vessel. However, the concepts are generally applicable and results specific to other BWR designs can be developed using the methodology presented in this paper.     

Use of risk assessment in the nuclear industry with specific reference to the Australian situation

The use of risk assessment in the nuclear industry began in the 1970s as a complementary approach to the deterministic methods used to assess the safety of nuclear facilities. As experience with the theory and application of probabilistic methods has grown, so too has its application. In the last decade, the use of probabilistic safety assessment has become commonplace for all phases of the life of a plant, including siting, design, construction, operation and decommissioning. In the particular case of operation of plant, the use of a ‘living’ safety case or probabilistic safety assessment, building upon operational experience, is becoming more widespread, both as an operational tool and as a basis for communication with the regulator. In the case of deciding upon a site for a proposed reactor, use is also being made of probabilistic methods in defining the effect of design parameters. Going hand in hand with this increased use of risk based methods has been the development of assessment criteria against which to judge the results being obtained from the risk analyses. This paper reviews the use of risk assessment in the light of the need for acceptability criteria and shows how these tools are applied in the Australian nuclear industry, with specific reference to the probabilistic safety assessment (PSA) performed of HIFAR.     

An approach for using risk assessment in risk-informed decisions on plant-specific changes to the licensing basis

This paper discusses an acceptable approach that the US Nuclear Regulatory Commission staff has proposed for using Probabilistic Risk Assessment in making decisions on changes to the licensing basis of a nuclear power plant. First, the overall philosophy of risk-informed decision-making, and the process framework are described. The philosophy is encapsulated in five principles, one of which states that, if the proposed change leads to an increase in core damage frequency or risk, the increases must be small and consistent with the intent of the Nuclear Regulatory Commission's Safety Goal Policy Statement. The second part of the paper discusses the use of PRA to demonstrate that this principle has been met. The discussion focuses on the acceptance guidelines, and on comparison of the PRA results with those guidelines. The difficulties that arise because of limitations in scope and analytical uncertainties are discussed and approaches to accommodate these difficulties in the decision-making are described.     

Development of a dynamical systems model of plant programmatic performance on nuclear power plant safety risk

Application of probabilistic risk assessment (PRA) techniques to model nuclear power plant accident sequences has provided a significant contribution to understanding the potential initiating events, equipment failures and operator errors that can lead to core damage accidents. Application of the lessons learned from these analyses has resulted in significant improvements in plant operation and safety. However, this approach has not been nearly as successful in addressing the impact of plant processes and management effectiveness on the risks of plant operation. The research described in this paper presents an alternative approach to addressing this issue. In this paper we propose a dynamical systems model that describes the interaction of important plant processes on nuclear safety risk. We discuss development of the mathematical model including the identification and interpretation of significant inter-process interactions. Next, we review the techniques applicable to analysis of nonlinear dynamical systems that are utilized in the characterization of the model. This is followed by a preliminary analysis of the model that demonstrates that its dynamical evolution displays features that have been observed at commercially operating plants. From this analysis, several significant insights are presented with respect to the effective control of nuclear safety risk. As an important example, analysis of the model dynamics indicates that significant benefits in effectively managing risk are obtained by integrating the plant operation and work management processes such that decisions are made utilizing a multidisciplinary and collaborative approach. We note that although the model was developed specifically to be applicable to nuclear power plants, many of the insights and conclusions obtained are likely applicable to other process industries.     

Calculating and addressing uncertainty for risk-based allowable outage times

This article examines the calculation and treatment of uncertainty in risk-based allowable outage times (AOTs) for operational control at nuclear power plants, where an AOT is defined as the time that a component or system is permitted to be out of service. The US Nuclear Regulatory Commission (NRC) has explored the possibility of using a nuclear power plant's probabilistic risk assessment results to determine component or system AOTs. The analysis and results from previous work prepared for the NRC on determining risk-based AOTs are presented. As part of the discussion, the article examines the inherent uncertainty in calculating risk-based AOTs and presents the difficulties in calculating these risk-based AOTs. It is noted that care should be taken when dealing with uncertainty analysis results where a time-interval is the outcome of the analysis. In addition, potential improvements in the mechanism of calculating risk-based AOTs are suggested.     

Conversion of a mainframe simulation for maintenance performance to a PC environment

This paper presents a brief review of a mainframe version of a computer code for simulating maintenance crew performance crew and introduces advantages realized with the recent implementation of a personal computer (PC) version. The basic computer model—the maintenance personnel performance simulation (MAPPS)—has been developed and validated by the US nuclear Regulatory Commission (NRC) in order to improve maintenance practices and procedures at nuclear power plants. The simulation model is stochastically based, and users are able to model 2 to 15 person crews. Maintenance crew performance is varied as a function of task, environment, and personnel factors. MAPPS produces human error probabilities (HEPs) suitable for use in probabilistic risk assessments. These HEPs are also a potentially important source of information for risk management data bases such as the NRC sponsored Nuclear Computerized Library for Assessing Reactor Reliability (NUCLARR).     

Historical perspective on failure rates for US commercial reactor components

To support the development of probabilistic risk assessments of US commercial nuclear power plants, significant effort has been expended to develop generic failure rates for components. Generic failure rates indicate industry-average performance of components, rather than component performance at a specific plant. Most publicly available, generic failure rate databases are typically based on data collected in the 1970s and 1980s for US nuclear power plants. Recent data analysis programs sponsored by the US Nuclear Regulatory Commission and data collection programs sponsored by the Institute of Nuclear Power Operations provide an opportunity to compare more recent failure rate estimates with those obtained in the 1970s and 1980s. These recent results indicate that many component generic failure rates are now lower than observed in the 1970s and 1980s. Suggestions for up-to-date failure rates are presented. Also, failure to run rates for standby components are presented for both short- and longer-term run times.     

A framework to integrate software behavior into dynamic probabilistic risk assessment

Software plays an increasingly important role in modern safety-critical systems. Although, research has been done to integrate software into the classical probabilistic risk assessment (PRA) framework, current PRA practice overwhelmingly neglects the contribution of software to system risk. Dynamic probabilistic risk assessment (DPRA) is considered to be the next generation of PRA techniques. DPRA is a set of methods and techniques in which simulation models that represent the behavior of the elements of a system are exercised in order to identify risks and vulnerabilities of the system. The fact remains, however, that modeling software for use in the DPRA framework is also quite complex and very little has been done to address the question directly and comprehensively. This paper develops a methodology to integrate software contributions in the DPRA environment. The framework includes a software representation, and an approach to incorporate the software representation into the DPRA environment SimPRA. The software representation is based on multi-level objects and the paper also proposes a framework to simulate the multi-level objects in the simulation-based DPRA environment. This is a new methodology to address the state explosion problem in the DPRA environment. This study is the first systematic effort to integrate software risk contributions into DPRA environments.     

Human reliability analysis—why not turn to the human factors community?

In response to the Dougherty thesis that contemporary human reliability analysis (HRA) methods are inadequate, this paper proposes that credible assessments of these HRA methods adequacy can be obtained only by means of their full exploitation by human factors specialists as part of the probabilistic risk assessment (PRA) process. The paper traces the history of human factors in PRA. It concludes that regarding PRA, only peripheral attention has been given to human factors; further that there has been almost a total absence of human factors specialists involved in the PRA process. The paper introduces and discusses a recent US Nuclear Regulatory Commission research initiative on a task analysis-linked evaluation technique (TALENT) concept for integrating human factors expertise into the PRA process, and fully exploiting state-of-knowledge HRA methods and data. The paper concludes that by means of TALENT implementation: (1) more credible assessments of HRA methods adequacy can be made, and (2) more realistic estimates of the overall impact of human error on complex high reliability systems' reliability and risk can be achieved.     

Issues related to structural aging in probabilistic risk assessment of nuclear power plants

Structural components and systems have an important safety function in nuclear power plants. Although they are essentially passive under normal operating conditions, they play a key role in mitigating the impact of extreme environmental events such as earthquakes, winds, fire and floods on plant safety. Moreover, the importance of structural components and systems in accident mitigation is amplified by common-cause effects. Reinforced concrete structural components and systems in NPPs are subject to a phenomenon known as aging, leading to time-dependent changes in strength and stiffness that may impact their ability to withstand various challenges during their service lives from operation, the environment and accidents. Time-dependent changes in structural properties as well as challenges to the system are random in nature. Accordingly, condition assessment of existing structures should be performed within a probabilistic framework. The mathematical formalism of a probabilistic risk assessment (PRA) provides a means for identifying aging structural components that may play a significant role in mitigating plant risk. Structural condition assessments supporting a decision regarding continued service can be rendered more efficient if guided by the logic of a PRA.     

The development and application of the accident dynamic simulator for dynamic probabilistic risk assessment of nuclear power plants

This paper describes the principal modelling concepts, practical aspects, and an application of the Accident Dynamic Simulator (ADS) developed for full scale dynamic probabilistic risk assessment (DPRA) of nuclear power plants. Full scale refers not only to the size of the models, but also to the number of potential sequences which should be studied. Plant thermal-hydraulics behaviour, safety systems response, and operator interactions are explicitly accounted for as integrated active parts in the development of accident scenarios. ADS uses discrete dynamic event trees (D-DET) as the main accident scenario modelling approach, and introduces computational techniques to minimize the computer memory requirement and expedite the simulation. An operator model (including procedure-based behaviour and several types of omission and commission errors) and a thermal-hydraulic model with a PC run time more than 300 times faster than real accident time are among the main modules of ADS. To demonstrate the capabilities of ADS, a dynamic PRA of the Steam Generator Tube Rupture event of a US nuclear power plant is analyzed.     

Dynamic modeling of the tradeoff between productivity and safety in critical engineering systems

Short-term tradeoffs between productivity and safety often exist in the operation of critical facilities such as nuclear power plants, offshore oil platforms, or simply individual cars. For example, interruption of operations for maintenance on demand can decrease short-term productivity but may be needed to ensure safety. Operations are interrupted for several reasons: scheduled maintenance, maintenance on demand, response to warnings, subsystem failure, or a catastrophic accident. The choice of operational procedures (e.g. timing and extent of scheduled maintenance) generally affects the probabilities of both production interruptions and catastrophic failures. In this paper, we present and illustrate a dynamic probabilistic model designed to describe the long-term evolution of such a system through the different phases of operation, shutdown, and possibly accident. The model's parameters represent explicitly the effects of different components' performance on the system's safety and reliability through an engineering probabilistic risk assessment (PRA). In addition to PRA, a Markov model is used to track the evolution of the system and its components through different performance phases. The model parameters are then linked to different operations strategies, to allow computation of the effects of each management strategy on the system's long-term productivity and safety. Decision analysis is then used to support the management of the short-term trade-offs between productivity and safety in order to maximize long-term performance. The value function is that of plant managers, within the constraints set by local utility commissions and national (e.g. energy) agencies. This model is illustrated by the case of outages (planned and unplanned) in nuclear power plants to show how it can be used to guide policy decisions regarding outage frequency and plant lifetime, and more specifically, the choice of a reactor tripping policy as a function of the state of the emergency core cooling subsystem.     

Organizational processes and nuclear power plant safety

This paper describes an integrated effort to define and measure organizational factors related to nuclear power plant safety. The research began by reviewing previously conducted studies looking at nuclear power operations and operations in other high reliability industries for indications of common safety-related, performance dimensions. Having established a list of 20 common dimensions, the project went on to fully define these dimensions and develop methods for their assessment. The methods of assessment developed for this application were employee survey—a series of self report questions answered by employees, behavioral checklist—sets of statements about the plant and its operations that observers respond to by answering yes or no, structured interview—a set of questions and interviewer asks of an employee and built around the 20 dimensions identified, and behaviorally anchored rating scales—an extension of the methodology used for assessing the performance of individuals to the process of assessing a nuclear power plant. Each of the methodologies is described as it applies to the assessment within the nuclear power environment and examples of each method are presented. Pilot tests of the feasibility of using these assessment methods were conducted in two nuclear power plants and the results are encouraging both in terms of the immediate identification of potential safety issues and as valuable additions to probabilistic risk assessment. Implications of this work for the future assessment of organizational factors related to nuclear power safety are discussed.     

An overview of PSA importance measures

The present paper deals with the use of probabilistic safety assessment (PSA) importance measures to optimise the performance of a nuclear power plant. This article is intended to give an overview on the subject for PSA practitioners. The most frequently used importance measures are shortly addressed. It is shown that two importance measures are sufficient to describe the character of the coredamage-equation. The most often used are the risk achievement and Fussell–Vesely importance, in combination with each other. In the field of nuclear power plant test and maintenance activities the Birnbaum importance is advocated.     

Expert elicitation approach for performing ATHEANA quantification

An expert elicitation approach has been developed to estimate probabilities for unsafe human actions (UAs) based on error-forcing contexts (EFCs) identified through the ATHEANA (A Technique for Human Event Analysis) search process. The expert elicitation approach integrates the knowledge of informed analysts to quantify UAs and treat uncertainty (‘quantification-including-uncertainty’). The analysis focuses on (a) the probabilistic risk assessment (PRA) sequence EFCs for which the UAs are being assessed, (b) the knowledge and experience of analysts (who should include trainers, operations staff, and PRA/human reliability analysis experts), and (c) facilitated translation of information into probabilities useful for PRA purposes. Rather than simply asking the analysts their opinion about failure probabilities, the approach emphasizes asking the analysts what experience and information they have that is relevant to the probability of failure. The facilitator then leads the group in combining the different kinds of information into a consensus probability distribution. This paper describes the expert elicitation process, presents its technical basis, and discusses the controls that are exercised to use it appropriately. The paper also points out the strengths and weaknesses of the approach and how it can be improved. Specifically, it describes how generalized contextually anchored probabilities (GCAPs) can be developed to serve as reference points for estimates of the likelihood of UAs and their distributions.