Performance Assessment in Support of the 1996 Compliance Certification Application for the Waste Isolation Pilot Plant

Helton, Jon C.; Anderson, D.R.; Jow, Hong-Nian; Marietta, M.G.; Basabilvazo, G.

doi:10.1111/j.1539-6924.1999.tb00453.x

Cited by 34 publications

(12 citation statements)

References 43 publications

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“…Often, event trees and fault trees are used to represent the effects of stochastic uncertainty, and sampling‐based procedures of the type illustrated in this presentation are used to represent the effects of subjective uncertainty. Many examples of analyses involving both stochastic and subjective uncertainty exist, including analyses related to reactor safety, ( 87–90 ) radioactive waste disposal, ( 91–93 ) environmental risk assessment, ( 94–100 ) and petroleum exploration. ( 101 )…”

Section: Discussionmentioning

confidence: 99%

Illustration of Sampling‐Based Methods for Uncertainty and Sensitivity Analysis

Helton¹,

Davis²

2002

Risk Analysis

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252

152

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A sequence of linear, monotonic, and nonmonotonic test problems is used to illustrate sampling-based uncertainty and sensitivity analysis procedures. Uncertainty results obtained with replicated random and Latin hypercube samples are compared, with the Latin hypercube samples tending to produce more stable results than the random samples. Sensitivity results obtained with the following procedures and/or measures are illustrated and compared: correlation coefficients (CCs), rank correlation coefficients (RCCs), common means (CMNs), common locations (CLs), common medians (CMDs), statistical independence (SI), standardized regression coefficients (SRCs), partial correlation coefficients (PCCs), standardized rank regression coefficients (SRRCs), partial rank correlation coefficients (PRCCs), stepwise regression analysis with raw and rank-transformed data, and examination of scatter plots. The effectiveness of a given procedure and/or measure depends on the characteristics of the individual test problems, with (1) linear measures (i.e., CCs, PCCs, SRCs) performing well on the linear test problems, (2) measures based on rank transforms (i.e., RCCs, PRCCs, SRRCs) performing well on the monotonic test problems, and (3) measures predicated on searches for nonrandom patterns (i.e., CMNs, CLs, CMDs, SI) performing well on the nonmonotonic test problems.

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Section: Discussionmentioning

confidence: 99%

Illustration of Sampling‐Based Methods for Uncertainty and Sensitivity Analysis

Helton¹,

Davis²

2002

Risk Analysis

Self Cite

252

152

View full text Add to dashboard Cite

show abstract

“…Helton et al 1999), there is reason to be optimistic that the required computations will not be prohibitive. Indeed, depending on the specific properties of a particular analysis, it could be possible to forgo the clustering procedure indicated in conjunction with Equations (44) and (45) and also Equations (48) and (49) and calculate patch failure properties for every node.…”

Section: % 46)mentioning

confidence: 99%

Incorporation of Uncertainty and Variability of Drip Shield and Waste Package Degradation in WAPDEG Analysis

Helton¹

2000

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“…However, not all high-consequence issues have requirements that explicitly state a confidence that includes uncertainties. Some high-consequence issues of national importance (e.g., nuclear power safety [Helton and Breeding 1993]; geologic disposal of transuranic radioactive wastes, WIPP [Helton et al 1999]; and geologic disposal of highlevel radioactive wastes, Yucca Mountain [Helton and Sallaberry 2007]) involve decisions that are tied to some measure of central tendency (mean or median) and are simply "informed" by the required treatment of uncertainties.…”

Section: C2 Requirements Languagementioning

confidence: 99%

Toward a more rigorous application of margins and uncertainties within the nuclear weapons life cycle : a Sandia perspective.

Klenke¹,

Novotny²,

A.³

et al. 2007

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This paper presents the conceptual framework that is being used to define quantification of margins and uncertainties (QMU) for application in the nuclear weapons (NW) work conducted at Sandia National Laboratories. The conceptual framework addresses the margins and uncertainties throughout the NW life cycle and includes the definition of terms related to QMU and to figures of merit. Potential applications of QMU consist of analyses based on physical data and on modeling and simulation. Appendix A provides general guidelines for addressing cases in which significant and relevant physical data are available for QMU analysis. Appendix B gives the specific guidance that was used to conduct QMU analyses in cycle 12 of the annual assessment process. Appendix C offers general guidelines for addressing cases in which appropriate models are available for use in QMU analysis. Appendix D contains an example that highlights the consequences of different treatments of uncertainty in model-based QMU analyses.4

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Performance Assessment in Support of the 1996 Compliance Certification Application for the Waste Isolation Pilot Plant

Cited by 34 publications

References 43 publications

Illustration of Sampling‐Based Methods for Uncertainty and Sensitivity Analysis

Illustration of Sampling‐Based Methods for Uncertainty and Sensitivity Analysis

Incorporation of Uncertainty and Variability of Drip Shield and Waste Package Degradation in WAPDEG Analysis

Toward a more rigorous application of margins and uncertainties within the nuclear weapons life cycle : a Sandia perspective.

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