An integrated probabilistic risk analysis decision support methodology for systems with multiple state variables

Probabilistic risk analysis (PRA) methods have been proven to be valuable in risk and reliability analysis. However, a weak link seems to exist between methods for analysing risks and those for making rational decisions. The integrated decision support system (IDSS) methodology presented in this paper attempts to address this issue in a practical manner. In consists of three phases: a PRA phase, a risk sensitivity analysis (SA) phase and an optimisation phase, which are implemented through an integrated computer software system. In the risk analysis phase the problem is analysed by the Boolean representation method (BRM), a PRA method that can deal with systems with multiple state variables and feedback loops. In the second phase the results obtained from the BRM are utilised directly to perform importance and risk SA. In the third phase, the problem is formulated as a multiple objective decision making problem in the form of multiple objective reliability optimisation. An industrial example is included. The resultant solutions of a five objective reliability optimisation are presented, on the basis of which rational decision making can be explored.     

Risk-significant functional dependencies in pressurized water reactors

This work provides evidence that functional dependencies among nuclear plant systems, particularly those among frontline safety systems and support systems, are often major contributors to the calculated risks in completed probabilistic risk assessments (PRAs). The study, furthermore, determined how the risk impact of functional dependencies could be reduced in future nuclear power plant designs. The risk reduction insights are summarized by a set of nine generalized design approaches, which we call design principles. These approaches are not new to the nuclear industry nor are the dependencies they address. The contribution made by this study is the use of PRA insights in pointing out the relative importance of the dependencies in terms of their contributions to risk.     

A framework for CAD-based geometric reasoning for robot assembly language†

For automatic robot programming, world modelling of the robot's environment is one of the most important phases of the task planning. World modelling requires that the robot know the environment in which it operates, including the spatial configuration of all objects in the task environment. In robotic assembly planning at the task level, representation of these objects requires symbolic feature and shape identification of the objects to be assembled by the robot. In this paper, we present a framework for reasoning about objects based on their shapes and features and the representation of such objects for robotic assembly planning when the modelling is done on a CAD system. We show the importance of AI languages in the communication of constructive solid geometry (CSG) based information from modellers. Finally, we present the schematic for a formalism, based on Prolog, for expressing object properties and assembly situations.     

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.     

Illustration of integrating mechanistic best-estimate analysis results in Level 1 probabilistic risk assessment

This paper contains an illustrative application of the Ref. 1 methodology for integrating mechanistic best-estimate analysis results in Level 1 probabilistic risk assessment (PRA). Predicated upon that application the paper addresses whether the methodology can be implemented in a cost-effective manner, and whether implementation is important for improving risk quantification.     

Probabilistic risk analysis for the NASA space shuttle: a brief history and current work

While NASA managers have always relied on risk analysis tools for the development and maintenance of space projects, quantitative and especially probabilistic techniques have been gaining acceptance in recent years. In some cases, the studies have been required, for example, to launch the Galileo spacecraft with plutonium fuel, but these successful applications have helped to demonstrate the benefits of these tools. This paper reviews the history of probabilistic risk analysis (PRA) by NASA for the space shuttle program and discusses the status of the on-going development of the Quantitative Risk Assessment System (QRAS) software that performs PRA. The goal is to have within NASA a tool that can be used when needed to update previous risk estimates and to assess the benefits of possible upgrades to the system.     

Retrospective review of experience with using the NUPRA software

The models used in probabilistic risk assessment (PRA) by the nuclear industry are supported by a wide variety of computer software. In fact, prior to the publication of the WASH-1400 in 1974, researchers and practitioners had already initiated the software development process leading to today's advanced and user-friendly PC-based software packages. NUS Corporation initiated its PC code development in the early 1980s. After testing a number of prototypes, a first integrated package supporting all parts of the Level 1 PRA was launched in 1987. This software package, called NUPRA, is an implementation of an approach based on minimal cutset equations and it has been used to install ten PRA studies on a PC. Insights from applications are now used to enhance the current NUPRA package to cover Level 2 and Level 3 analysis needs. In this paper the experience of using the NUPRA software is documented.     

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.     

Risk-informed design of IRIS using a level-1 probabilistic risk assessment from its conceptual design phase

In this study, a probabilistic risk assessment (PRA) for the International Reactor Innovative and Secure (IRIS) has been generated to address two key areas as a part of the effort for the pre-application licensing of the IRIS design.First, the IRIS PRA is supporting the evaluation of IRIS design by providing design insights as well as a solid risk basis for the pre-licensing evaluation of the IRIS design. Second, the current PRA task is beginning the preparation of the more complete PRA analyses and documentation that will be required for Design Certification. The initial IRIS PRA is an at-power, Level-1 PRA for internal events that focuses on the evaluation of the IRIS design features to support the risk-informed design of IRIS by application of the PRA insights and the risk information to the design. To accomplish the evaluation, a reasonably complete Level-1 PRA model has been developed.The use of PRA in the early stages of the design has allowed a selection of design and performance features and an optimization of the design of several systems to reduce the potential for events that could lead to core damage via both enhanced prevention and mitigation of challenges. As a result, the total core damage frequency for internal events for the IRIS design has been calculated as 1.2×10⁻⁸ per year.     

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 mathematical formulation for the event sequence diagram framework

The Event Sequence Diagram (ESD) framework allows modeling of dynamic situations of interest to PRA analysts. A qualitative presentation of the framework was given in an earlier article. The mathematical formulation for the components of the ESD framework is described in this article. The formulation was derived from the basic probabilistic dynamics equations. For tackling certain dynamic non-Markovian situations, the probabilistic dynamics framework was extended. The mathematical treatment of dependencies among fault trees in a multi layered ESD framework is also presented.     

Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents: Part 5: Dynamic probabilistic simulation of the IDAC model

This is the last in a series of five papers that discuss the Information Decision and Action in Crew (IDAC) context for human reliability analysis (HRA) and example application. The model is developed to probabilistically predict the responses of the control room operating crew in nuclear power plants during an accident, for use in probabilistic risk assessments (PRA). The operator response spectrum includes cognitive, emotional, and physical activities during the course of an accident. This paper describes a dynamic PRA computer simulation program, accident dynamics simulator (ADS), developed in part to implement the IDAC model. This paper also provides a detailed example of implementing a simpler version of IDAC, compared with the IDAC model discussed in the first four papers of this series, to demonstrate the practicality of integrating a detailed cognitive HRA model within a dynamic PRA framework.     

On comparing PRA results with operating experience

A procedure is presented for quantifying the consistency between probabilistic risk assessment (PRA) results and corresponding plant-specific operating data not considered in the PRA. The method, which is easily implemented in practice, is based on the use of Bayes p-values for the predictive probability that the observed data would have been produced from the PRA results in conjunction with an assumed binomial or Poisson sampling distribution. Uncertainties in both the PRA results and the operating data are considered. The method is used to quantify the consistency between PRA results and operating data for high-pressure coolant injection system unreliabilities at 11 US commercial boiling water reactors.     

Construction of event-tree/fault-tree models from a Markov approach to dynamic system reliability

While the event-tree (ET)/fault-tree (FT) methodology is the most popular approach to probability risk assessment (PRA), concerns have been raised in the literature regarding its potential limitations in the reliability modeling of dynamic systems. Markov reliability models have the ability to capture the statistical dependencies between failure events that can arise in complex dynamic systems. A methodology is presented that combines Markov modeling with the cell-to-cell mapping technique (CCMT) to construct dynamic ETs/FTs and addresses the concerns with the traditional ET/FT methodology. The approach is demonstrated using a simple water level control system. It is also shown how the generated ETs/FTs can be incorporated into an existing PRA so that only the (sub)systems requiring dynamic methods need to be analyzed using this approach while still leveraging the static model of the rest of the system.     

Developing a risk/cost framework for routing truck movements of hazardous materials

As the United States continues to increase its dependence on industrial technologies which require hazardous materials and generate hazardous wastes, concern is mounting over the safe transport of hazardous cargo. It is estimated that 1.5 billion tons of hazardous cargo are moved through the nation's transportation systems (excluding pipeline), with truck as the primary mode of transport. Because of the dynamic nature of exposure to the population and environment associated with the transport of hazardous cargo, it is important to develop an accurate representation of this type of transport risk, and to structure a framework for designating a permanent set of shipping routes based on optimizing across risks and costs. This paper describes a methodology which has been developed that incorporates risk and cost into a framework for optimizing the routing of truck movements of hazardous materials. Considerable attention is focused on the risk estimation part of this process, as this is a subject of much uncertainty and of considerable significance to policymakers. The resulting methodology is applied in a regional setting to illustrate its use as an analysis tool. Enhancements to the model structure and extensions beyond the truck routing problem are also discussed.     

Critical success factor framework for the implementation of integrated-enterprise systems in the manufacturing environment

The business environment today is a collaborative business environment. These include inter- and intra-enterprise business process collaboration in the areas of supply chain management, customer relationship management, supplier relationship management, e-business and employee–business integration. To achieve this, enterprises also realize the need to implement integrated-enterprise systems, which integrate tightly their intra- and inter-enterprise business processes. With new technologies like web services, wireless applications and advanced software applications that enable collaboration, the enterprise today needs implementation frameworks that consider the requirements of collaborative business scenarios in a holistic manner. Consideration of the various critical success factors in the life cycle of the integrated-enterprise systems implementation reduces the risk of failures. A critical success factor framework is introduced for an integrated-enterprise systems implementation framework in the collaborative manufacturing environment. This framework ensures that the various critical success factors are considered at a very early stage of the project so that the integrated-enterprise system can be designed and implemented. Risks and potential problem areas are identified and mitigation plans can be put in place. A case study of an integrated-enterprise system implementation using the critical success factor framework is also presented. This framework was used successfully to design and implement a collaborative integrated-enterprise system for a manufacturing enterprise.     

GOALDS—Goal Based Damage Ship Stability and safety standards

The new probabilistic damaged stability regulations for dry cargo and passenger ships (SOLAS 2009), which entered into force on January 1, 2009, represent a major step forward in achieving an improved safety standard through the rationalisation and harmonization of damaged stability requirements. There are, however, serious concerns regarding the adopted formulation for the calculation of the survival probability of passenger ships, particularly for ROPAX and large cruise vessels. The present paper outlines the objectives, the methodology of work and main results of the EU-funded FP7 project GOALDS (Goal Based Damaged Stability, 2009–2012), which aims to address the above shortcomings by state-of-the-art scientific methods and by formulating a rational, goal-based regulatory framework, properly accounting for the damage stability properties of passenger ships and the risk of people onboard.     

SCAP: a new methodology for safety management based on feedback from credible accident-probabilistic fault tree analysis system

As it is conventionally done, strategies for incorporating accident--prevention measures in any hazardous chemical process industry are developed on the basis of input from risk assessment. However, the two steps-- risk assessment and hazard reduction (or safety) measures--are not linked interactively in the existing methodologies. This prevents a quantitative assessment of the impacts of safety measures on risk control.We have made an attempt to develop a methodology in which risk assessment steps are interactively linked with implementation of safety measures. The resultant system tells us the extent of reduction of risk by each successive safety measure. It also tells based on sophisticated maximum credible accident analysis (MCAA) and probabilistic fault tree analysis (PFTA) whether a given unit can ever be made 'safe'. The application of the methodology has been illustrated with a case study.     

QRAS—the quantitative risk assessment system

This paper presents an overview of QRAS, the Quantitative Risk Assessment System. QRAS is a PC-based software tool for conducting Probabilistic Risk Assessments (PRAs), which was developed to address risk analysis needs held by NASA. QRAS is, however, applicable in a wide range of industries. The philosophy behind the development of QRAS is to bridge communication and skill gaps between managers, engineers, and risk analysts by using representations of the risk model and analysis results that are easy to comprehend by each of those groups. For that purpose, event sequence diagrams (ESD) are being used as a replacement for event trees (ET) to model scenarios, and the quantification of events is possible through a set of quantification models familiar to engineers. An automated common cause failure (CCF) modeling tool further aids the risk modeling. QRAS applies BDD-based algorithms for the accurate and efficient computation of risk results. The paper presents QRAS' modeling and analysis capabilities. The performance of the underlying BDD algorithm is assessed and compared to that of another PRA software tool, using a case study extracted from the International Space Station PRA.     

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.