3,595 research outputs found
DETAM for accident sequence analysis
Includes bibliographical references (pages 133-138)Final reportSupported by the United States Nuclear Regulatory Commission. NRC-04-88-14
Integrated Scenario-Based Methodology for Project Risk Management
Project risk management is currently used in several industries and mandated by government acquisition agencies around the world to manage uncertainty in an effort to improve a project's probability of success. Common practice involves developing a list of risk items scored with probability and consequence ordinal scales by committee usually focusing on cost and schedule issues. A scenario based process modeling construct is introduced using a hybrid Probabilistic Risk Assessment and Decision Analysis framework integrating project development risks with operational system risks. Project management's decisions are explicitly modeled and ranked based on risk importance to the project. Multiple consequence attributes are unified providing a basis for computing total project risk. This study shows that such an approach leads to an analysis system where scenarios tracing risk items to many possible consequences are explicitly understood; the interaction between cost, schedule, and performance models drive the analysis; probabilities for overruns, delays, increased system hazards are determined directly; and state-of-the-art quantification techniques are directly applicable. All these enhance project management's capability to respond with more effective decisions
Dynamic HRA in outage from literature and outage personnel interview perspectives
In 2021, the goal of the SAFIR2022 project NAPRA task T3.2 was to provide an overview of an outage of a nuclear power plant from the perspective of human reliability analysis (HRA). The general features of the outage as well as the specific matters related to human reliability and dynamism in the outage context were studied from literature and outage personnel interview perspectives.The safety-critical nature of an outage is well recognized, and there is a wealth of literature on the specifics of outage and the challenges associated with the successful completion of work. HRA methods have mostly been developed for full power conditions where the operator’s actions are well trained and laid down in procedures, in time frames typically less than 60 minutes. In the planned shutdown the work concentrates outside the control room, is less in procedures and less trained and the time frames may be much longer. The environment is continuously changing, there are huge number of workers, large variety of work activities, tight schedule and the requirements are high concerning both safety and productivity. The key issues that should be considered in the HRA are errors of commission (EOCs), dependencies between human actions and the dynamism of the operating environment.One practical objective of this report was to identify a scenario to focus on in further work related to dynamic modelling. Based on interviews, heavy loads were identified as critical but also mentally and physically loaded. They also include features identified safety critical in scientific literature. This scenario will be studied in more detail in 2022. Work analysis will be performed with special emphasis on applying a combination of methods to elicit the key dynamic features from the HRA perspective
Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners (Second Edition)
Probabilistic Risk Assessment (PRA) is a comprehensive, structured, and logical analysis method aimed at identifying and assessing risks in complex technological systems for the purpose of cost-effectively improving their safety and performance. NASA's objective is to better understand and effectively manage risk, and thus more effectively ensure mission and programmatic success, and to achieve and maintain high safety standards at NASA. NASA intends to use risk assessment in its programs and projects to support optimal management decision making for the improvement of safety and program performance. In addition to using quantitative/probabilistic risk assessment to improve safety and enhance the safety decision process, NASA has incorporated quantitative risk assessment into its system safety assessment process, which until now has relied primarily on a qualitative representation of risk. Also, NASA has recently adopted the Risk-Informed Decision Making (RIDM) process [1-1] as a valuable addition to supplement existing deterministic and experience-based engineering methods and tools. Over the years, NASA has been a leader in most of the technologies it has employed in its programs. One would think that PRA should be no exception. In fact, it would be natural for NASA to be a leader in PRA because, as a technology pioneer, NASA uses risk assessment and management implicitly or explicitly on a daily basis. NASA has probabilistic safety requirements (thresholds and goals) for crew transportation system missions to the International Space Station (ISS) [1-2]. NASA intends to have probabilistic requirements for any new human spaceflight transportation system acquisition. Methods to perform risk and reliability assessment in the early 1960s originated in U.S. aerospace and missile programs. Fault tree analysis (FTA) is an example. It would have been a reasonable extrapolation to expect that NASA would also become the world leader in the application of PRA. That was, however, not to happen. Early in the Apollo program, estimates of the probability for a successful roundtrip human mission to the moon yielded disappointingly low (and suspect) values and NASA became discouraged from further performing quantitative risk analyses until some two decades later when the methods were more refined, rigorous, and repeatable. Instead, NASA decided to rely primarily on the Hazard Analysis (HA) and Failure Modes and Effects Analysis (FMEA) methods for system safety assessment
A HYBRID METHODOLOGY FOR MODELING RISK OF ADVERSE EVENTS IN COMPLEX HEALTHCARE SETTINGS
Despite efforts to provide safe, effective medical care, adverse events still occur with some regularity. While risk cannot be entirely eliminated from healthcare activities, an important goal is to develop effective and durable mitigation strategies to render the system `safer'. In order to do this, though, we must develop models that comprehensively and realistically characterize the risk. In the healthcare domain, this can be extremely challenging due to the wide variability in the way that healthcare processes and interventions are executed and also due to the dynamic nature of risk in this particular domain. In this study we have developed a generic methodology for evaluating dynamic changes in adverse event risk in acute care hospitals as a function of organizational and non-organizational factors, using a combination of modeling formalisms. First, a system dynamics (SD) framework is used to demonstrate how organizational level and policy level contributions to risk evolve over time, and how policies and decisions may affect the general system-level contribution to adverse event risk. It also captures the feedback of organizational factors and decisions over time and the non-linearities in these feedback effects. Second, Bayesian Belief Network (BBN) framework is used to represent patient-level factors and also physician level decisions and factors in the management of an individual patient, which contribute to the risk of hospital-acquired adverse event. The model is intended to support hospital decisions with regards to staffing, length of stay, and investment in safeties, which evolve dynamically over time. The methodology has been applied in modeling the two types of common adverse events; pressure ulcers and vascular catheter-associated infection, and has been validated with eight years of clinical data
Development of Approaches to Common Cause Dependencies with Applications to Multi-Unit Nuclear Power Plant
The term “common cause dependencies” encompasses the possible mechanisms that directly compromise components performances and ultimately cause degradation or failure of multiple components, referred to as common cause failure (CCF) events. The CCF events have been a major contributor to the risk posed by the nuclear power plants and considerable research efforts have been devoted to model the impacts of CCF based on historical observations and engineering judgment, referred to as CCF models. However, most current probabilistic risk assessment (PRA) studies are restricted to single reactor units and could not appropriately consider the common cause dependencies across reactor units. Recently, the common cause dependencies across reactor units have attracted a lot of attention, especially following the 2011 Fukushima accident in Japan that involved multiple reactor unit damages and radioactive source term releases. To gain an accurate view of a site's risk profile, a site-based risk metric representing the entire site rather than single reactor unit should be considered and evaluated through a multi-unit PRA (MUPRA). However, the multi-unit risk is neither formally nor adequately addressed in either the regulatory or the commercial nuclear environments and there are still gaps in the PRA methods to model such multi-unit events. In particular, external events, especially seismic events, are expected to be very important in the assessment of risks related to multi-unit nuclear plant sites.
The objective of this dissertation is to develop three inter-related approaches to address important issues in both external events and internal events in the MUPRA.
1) Develop a general MUPRA framework to identify and characterize the multi-unit events, and ultimately to assess the risk profile of multi-unit sites.
2) Develop an improved approach to seismic MUPRA through identifying and addressing the issues in the current methods for seismic dependency modeling. The proposed approach can also be extended to address other external events involved in the MUPRA.
3) Develop a novel CCF model for components undergoing age-related degradation by superimposing the maintenance impacts on the component degradation evolutions inferred from condition monitoring data. This approach advances the state-of-the-art CCF analysis in general and assists in the studies of internal events of the MUPRA
Identification of Causal Paths and Prediction of Runway Incursion Risk using Bayesian Belief Networks
In the U.S. and worldwide, runway incursions are widely acknowledged as a critical concern for aviation safety. However, despite widespread attempts to reduce the frequency of runway incursions, the rate at which these events occur in the U.S. has steadily risen over the past several years. Attempts to analyze runway incursion causation have been made, but these methods are often limited to investigations of discrete events and do not address the dynamic interactions that lead to breaches of runway safety. While the generally static nature of runway incursion research is understandable given that data are often sparsely available, the unmitigated rate at which runway incursions take place indicates a need for more comprehensive risk models that extend currently available research.
This dissertation summarizes the existing literature, emphasizing the need for cross-domain methods of causation analysis applied to runway incursions in the U.S. and reviewing probabilistic methodologies for reasoning under uncertainty. A holistic modeling technique using Bayesian Belief Networks as a means of interpreting causation even in the presence of sparse data is outlined in three phases: causal factor identification, model development, and expert elicitation, with intended application at the systems or regulatory agency level. Further, the importance of investigating runway incursions probabilistically and incorporating information from human factors, technological, and organizational perspectives is supported. A method for structuring a Bayesian network using quantitative and qualitative event analysis in conjunction with structured expert probability estimation is outlined and results are presented for propagation of evidence through the model as well as for causal analysis.
In this research, advances in the aggregation of runway incursion data are outlined, and a means of combining quantitative and qualitative information is developed. Building upon these data, a method for developing and validating a Bayesian network while maintaining operational transferability is also presented. Further, the body of knowledge is extended with respect to structured expert judgment, as operationalization is combined with elicitation of expert data to create a technique for gathering expert assessments of probability in a computationally compact manner while preserving mathematical accuracy in rank correlation and dependence structure.
The model developed in this study is shown to produce accurate results within the U.S. aviation system, and to provide a dynamic, inferential platform for future evaluation of runway incursion causation. These results in part confirm what is known about runway incursion causation, but more importantly they shed more light on multifaceted causal interactions and do so in a modeling space that allows for causal inference and evaluation of changes to the system in a dynamic setting. Suggestions for future research are also discussed, most prominent of which is that this model allows for robust and flexible assessment of mitigation strategies within a holistic model of runway safety
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