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Kelly, Dana

Publications and source records attributed to Kelly, Dana.

8 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Risk Assessment in Event Evaluation P-302

Incidents at locations such as nuclear power plants, waste repositories, and medical treatment facilities occur at many different times and under a variety of situations. Even the most modern, well run organizations may experience events that are precursors to more serious situations. Currently, the U.S. Nuclear Regulatory Commission evaluates a variety of such events at commercial nuclear plants via the Significance Determination Process (SDP), Management Directive 8.3, and the Accident Sequence Precursor (ASP) program. In these programs, the analyst calculates a conditional risk measure (e.g., conditional core damage probability) for an initiating event situation or an unplanned equipment outage. This calculation is called an “event evaluation.” An “event evaluation” uses a probabilistic risk assessment model to obtain a risk measure that is conditional on the situation which existed during an incident. The Risk Assessment in Event Evaluation material is intended to address how this evaluation is performed using modern risk analysis tools.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2014

A robust Bayesian approach to modeling epistemic uncertainty in common-cause failure models

In a standard Bayesian approach to the alpha-factor model for common-cause failure, a precise Dirichlet prior distribution models epistemic uncertainty in the alpha-factors. This Dirichlet prior is then updated with observed data to obtain a posterior distribution, which forms the basis for further inferences. In this paper, we adapt the imprecise Dirichlet model of Walley to represent epistemic uncertainty in the alpha-factors. In this approach, epistemic uncertainty is expressed more cautiously via lower and upper expectations for each alpha-factor, along with a learning parameter which determines how quickly the model learns from observed data. For this application, we focus on elicitation of the learning parameter, and find that values in the range of 1 to 10 seem reasonable. The approach is compared with Kelly and Atwood's minimally informative Dirichlet prior for the alpha-factor model, which incorporated precise mean values for the alpha-factors, but which was otherwise quite diffuse. Next, we explore the use of a set of Gamma priors to model epistemic uncertainty in the marginal failure rate, expressed via a lower and upper expectation for this rate, again along with a learning parameter. As zero counts are generally less of an issue here, we find that the choice of this learning parameter is less crucial. Finally, we demonstrate how both epistemic uncertainty models can be combined to arrive at lower and upper expectations for all common-cause failure rates. Thereby, we effectively provide a full sensitivity analysis of common-cause failure rates, properly reflecting epistemic uncertainty of the analyst on all levels of the common-cause failure model.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

Effect of Epistemic Uncertainty Modeling Approach on Decision-Making: Example using Equipment Performance Indicator

Quantitative risk assessments are an integral part of risk-informed regulation of current and future nuclear plants in the U.S. The Bayesian approach to uncertainty, in which both stochastic and epistemic uncertainties are represented with precise probability distributions, is the standard approach to modeling uncertainties in such quantitative risk assessments. However, there are long-standing criticisms of the Bayesian approach to epistemic uncertainty from many perspectives, and a number of alternative approaches have been proposed. Among these alternatives, the most promising (and most rapidly developing) would appear to be the concept of imprecise probability. In this paper, we employ a performance indicator example to focus the discussion. We first give a short overview of the traditional Bayesian paradigm and review some its controversial aspects, for example, issues with so-called noninformative prior distributions. We then discuss how the imprecise probability approach treats these issues and compare it with two other approaches: sensitivity analysis and hierarchical Bayes modeling. We conclude with some practical implications for risk-informed decision making.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

Microworlds, Simulators, and Simulation: Framework for a Benchmark of Human Reliability Data Sources

In this paper, we propose a method to improve the data basis of human reliability analysis (HRA) by extending the data sources used to inform HRA methods. Currently, most HRA methods are based on limited empirical data, and efforts to enhance the empirical basis behind HRA methods have not yet yielded significant new data. Part of the reason behind this shortage of quality data is attributable to the data sources used. Data have been derived from unrelated industries, from infrequent risk-significant events, or from costly control room simulator studies. We propose a benchmark of four data sources: a simplified microworld simulator using unskilled student operators, a full-scope control room simulator using skilled student operators, a full-scope control room simulator using licensed commercial operators, and a human performance modeling and simulation system using virtual operators. The goal of this research is to compare findings across the data sources to determine to what extent data may be used and generalized from cost effective sources.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2009

Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis

This document, Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis, is intended to provide guidelines for the collection and evaluation of risk and reliability-related data. It is aimed at scientists and engineers familiar with risk and reliability methods and provides a hands-on approach to the investigation and application of a variety of risk and reliability data assessment methods, tools, and techniques. This document provides both: A broad perspective on data analysis collection and evaluation issues. A narrow focus on the methods to implement a comprehensive information repository. The topics addressed herein cover the fundamentals of how data and information are to be used in risk and reliability analysis models and their potential role in decision making. Understanding these topics is essential to attaining a risk informed decision making environment that is being sought by NASA requirements and procedures such as 8000.4 (Agency Risk Management Procedural Requirements), NPR 8705.05 (Probabilistic Risk Assessment Procedures for NASA Programs and Projects), and the System Safety requirements of NPR 8715.3 (NASA General Safety Program Requirements).

Dezfuli, Homayoon↗