The characterization of uncertainty in Probabilistic Risk Assessments of complex systems

Parry, Gareth

doi:10.1016/s0951-8320(96)00069-5

Cited by 177 publications

(104 citation statements)

References 6 publications

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“…This type of uncertainty is termed aleatory (or stochastic, irreducible, type A, see (Helton et al, 2006)). This distinction has been and still is an area of interest and study in the engineering and risk assessment community (see (Apostolakis, 1990;Helton, 1997;Helton et al, 2007;Parry & Winter, 1981;Pate'-Cornell, 1996)). …”

Section: Introductionmentioning

confidence: 99%

A methodology for performing global uncertainty and sensitivity analysis in systems biology

Marino

Hogue

Ray

et al. 2008

Journal of Theoretical Biology

2,217

1,892

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Accuracy of results from mathematical and computer models of biological systems is often complicated by the presence of uncertainties in experimental data that are used to estimate parameter values. Current mathematical modeling approaches typically use either single-parameter or local sensitivity analyses. However, these methods do not accurately assess uncertainty and sensitivity in the system as, by default they hold all other parameters fixed at baseline values. Using techniques described within we demonstrate how a multi-dimensional parameter space can be studied globally so all uncertainties can be identified. Further, uncertainty and sensitivity analysis techniques can help to identify and ultimately control uncertainties. In this work we develop methods for applying existing analytical tools to perform analyses on a variety of mathematical and computer models. We compare two specific types of global sensitivity analysis indexes that have proven to be among the most robust and efficient. Through familiar and new examples of mathematical and computer models, we provide a complete methodology for performing these analyses, both in deterministic and stochastic settings, and propose novel techniques to handle problems encountered during this type of analyses.

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Section: Introductionmentioning

confidence: 99%

A methodology for performing global uncertainty and sensitivity analysis in systems biology

Marino

Hogue

Ray

et al. 2008

Journal of Theoretical Biology

2,217

1,892

View full text Add to dashboard Cite

show abstract

“…Epistemic uncertainty can be reduced whereas aleatory uncertainty is not reducible. As a consequence, many researchers argue that both types of uncertainty should be treated separately (e.g., Hoffman and Hammonds, 1994, Ferson and Ginzburg, 1996, Hora, 1996, Parry, 1996, Haimes, 1998, Cullen and Frey, 1999, Hall, 2003, Helton and Oberkamp, 2004, Merz and Thieken, 2005. The separation between these two kinds of uncertainty is particularly important in risk analyses, where aleatory uncertainty arises from the many possible failure scenarios that may occur, and epistemic uncertainty arises from a lack of knowledge with respect to the quantification of the frequency, evolution or consequences of failures.…”

Section: Introductionmentioning

confidence: 99%

Flood risk curves and uncertainty bounds

2009

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Although flood risk assessments are frequently associated with significant uncertainty, formal uncertainty analyses are the exception rather than the rule. We propose to separate two fundamentally different types of uncertainty in flood risk analyses: aleatory and epistemic uncertainty. Aleatory uncertainty refers to quantities that are inherently variable in time, space or populations of individuals or objects. Epistemic uncertainty results from incomplete knowledge and is related to our inability to understand, measure and describe the system under investigation. The separation between aleatory and epistemic uncertainty is exemplified for the flood risk analysis of the City of Cologne, Germany. This flood risk assessment consists of three modules, (1) flood frequency analysis, (2) inundation estimation, and (3) damage estimation. By the concept of parallel models, the epistemic uncertainty of each module is quantified. The epistemic uncertainty associated with the risk estimate is reduced by introducing additional information into the risk analysis. Finally, the contribution of different modules to the total uncertainty is quantified. The flood risk analysis results in a flood risk curve, representing aleatory uncertainty, and in associated uncertainty bounds, representing epistemic uncertainty. In this way, the separation reveals the uncertainty (epistemic) that can be reduced by more knowledge and the uncertainty (aleatory) that is not reducible.

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“…In the design and implementation of analyses for complex systems, it is useful to distinguish between two types of uncertainty: aleatory uncertainty and epistemic uncertainty [4][5][6][7][8][9][10][11][12][13][14][15][16].…”

Section: Treatment Of Uncertaintymentioning

confidence: 99%

Development, analysis, and evaluation of a commercial software framework for the study of Extremely Low Probability of Rupture (xLPR) events at nuclear power plants.

Kalinich¹,

Helton²,

Sallaberry³

et al. 2010

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Sandia National Laboratories (SNL) participated in a Pilot Study to examine the process and requirements to create a software system to assess the extremely low probability of pipe rupture (xLPR) in nuclear power plants. This project was tasked to develop a prototype xLPR model leveraging existing fracture mechanics models and codes coupled with a commercial software framework to determine the framework, model, and architecture requirements appropriate for building a modular-based code. The xLPR pilot study was conducted to demonstrate the feasibility of the proposed developmental process and framework for a probabilistic code to address degradation mechanisms in piping system safety assessments. The pilot study includes a demonstration problem to assess the probability of rupture of DM pressurizer surge nozzle welds degraded by primary water stress-corrosion cracking (PWSCC). The pilot study was designed to define and develop the framework and model; then construct a prototype software system based on the proposed model. The second phase of the project will be a longer term program and code development effort focusing on the generic, primary piping integrity issues (xLPR code). The results and recommendations presented in this report will be used to help the U.S. Nuclear Regulatory Commission (NRC) define the requirements for the longer term program.iv

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The characterization of uncertainty in Probabilistic Risk Assessments of complex systems

Cited by 177 publications

References 6 publications

A methodology for performing global uncertainty and sensitivity analysis in systems biology

A methodology for performing global uncertainty and sensitivity analysis in systems biology

Flood risk curves and uncertainty bounds

Development, analysis, and evaluation of a commercial software framework for the study of Extremely Low Probability of Rupture (xLPR) events at nuclear power plants.

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