The NUREG-1150 probabilistic risk assessment for the Sequoyah Nuclear Plant

Gregory, J.J.; Breeding, R.J.; Helton, Jon C.; Murfin, W.B.; Higgins, S.J.; Shiver, A.W.

doi:10.1016/0029-5493(92)90303-d

Cited by 19 publications

(5 citation statements)

References 7 publications

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“…This work was then continued in the NRC's MELCOR program to develop software for use in analyses of potential accidents at nuclear power plants. [142][143][144][145][146][147][148][149] Subsequently, the NRC used sampling-based procedures for the propagation of epistemic uncertainty in its reassessment of results obtained in the Reactor Safety Study (i.e., in the NUREG-1150 reactor probabilistic risk assessments) 59,60,[150][151][152][153][154] and also in its following Risk Methods Integration and Evaluation Program (RMIEP). 155 In addition, the NRC has used sampling-based uncertainty and sensitivity analysis procedures in a sequence of studies involving reactor accident consequence models.…”

Section: Calculation Of Expected Dose and Display Of Epistemic Uncertmentioning

confidence: 99%

Illustration of sampling-based approaches to the calculation of expected dose in performance assessments for the proposed high level radioactive waste repository at Yucca Mountain, Nevada.

Helton

Sallaberry

2007

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A deep geologic repository for high level radioactive waste is under development by the U.S. Department of Energy at Yucca Mountain (YM), Nevada. As mandated in the Energy Policy Act of 1992, the U.S. Environmental Protection Agency (EPA) has promulgated public health and safety standards (i.e., 40 CFR Part 197) for the YM repository, and the U.S. Nuclear Regulatory Commission has promulgated licensing standards (i.e., 10 CFR Parts 2, 19, 20, etc.) consistent with 40 CFR Part 197 that the DOE must establish are met in order for the YM repository to be licensed for operation. Important requirements in 40 CFR Part 197 and 10 CFR Parts 2, 19, 20, etc. relate to the determination of expected (i.e., mean) dose to a reasonably maximally exposed individual (RMEI) and the incorporation of uncertainty into this determination. This presentation describes and illustrates how general and typically nonquantitive statements in 40 CFR Part 197 and 10 CFR Parts 2, 19, 20, etc. can be given a formal mathematical structure that facilitates both the calculation of expected dose to the RMEI and the appropriate separation in this calculation of aleatory uncertainty (i.e., randomness in the properties of future occurrences such as igneous and seismic events) and epistemic uncertainty (i.e., lack of knowledge about quantities that are poorly known but assumed to have constant values in the calculation of expected dose to the RMEI).

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Section: Calculation Of Expected Dose and Display Of Epistemic Uncertmentioning

confidence: 99%

Illustration of sampling-based approaches to the calculation of expected dose in performance assessments for the proposed high level radioactive waste repository at Yucca Mountain, Nevada.

Helton

Sallaberry

2007

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“…The NUREG-1150 analyses (see Refs. 82,[120][121][122][123][124] are an example of this approach to the propagation of uncertainty.…”

Section: Generation Of Samplementioning

confidence: 99%

“…Additional information: The NUREG-1150 analyses, 82,[120][121][122][123][124] the analyses carried out in support of the Compliance Certification Application for the Waste Isolation Pilot Plant, [105][106][107] and analyses carried out in support of the Yucca Mountain Project's development of a facility for the deep geologic disposal of high level radioactive waste [140][141][142] provide examples of complex analyses that have used Latin hypercube sampling in the propagation of epistemic uncertainty.…”

Section: Propagation Of Sample Through the Analysismentioning

confidence: 99%

Survey of sampling-based methods for uncertainty and sensitivity analysis.

Johnson¹,

Helton²,

Sallaberry³

et al. 2006

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Sampling-based methods for uncertainty and sensitivity analysis are reviewed. The following topics are considered: (i) Definition of probability distributions to characterize epistemic uncertainty in analysis inputs, (ii) Generation of samples from uncertain analysis inputs, (iii) Propagation of sampled inputs through an analysis, (iv) Presentation of uncertainty analysis results, and (v) Determination of sensitivity analysis results. Special attention is given to the determination of sensitivity analysis results, with brief descriptions and illustrations given for the following procedures/techniques: examination of scatterplots, correlation analysis, regression analysis, partial correlation analysis, rank transformations, statistical tests for patterns based on gridding, entropy tests for patterns based on gridding, nonparametric regression analysis, squared rank differences/rank correlation coefficient test, two dimensional Kolmogorov-Smirnov test, tests for patterns based on distance measures, top down coefficient of concordance, and variance decomposition.

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“…The probabilityprob (TIjl WBj n WD,) is approximated with the assumption that n WD,) = prob (TIjl I WBj), prob (TIjL I WBj with the result that prob ( T I j l WBj n WD,) = l/nlZj Overview of M A W S owing to the original definition of TIjl given in Eq. (12). The number of time intervals nnj associated with weather bin WBj is defined by nnj = min { n77 = 4, n W j 1.…”

Section: Overview Of Maccsmentioning

confidence: 99%

“…As a result, the analyses for prodromal vomiting are quite similar to the already presented analyses for early fatalities, with EIFACAl (early injury hazard function alpha value for prodromal vomiting) playing the same role in the analyses for prodromal vomiting as EFFACAl (early fatality hazard function alpha value for bone marrow exposure) in the analyses for early fatalities. Because of this similarity, Figures 10,11,and 12 and Tables 9 and 10 are presented without discussion. The discussions of the corresponding results in Section 5.1 for early fatalities can be consulted for additional information.…”

Section: Prodromal Vomitingmentioning

confidence: 99%

Uncertainty and sensitivity analysis of early exposure results with the MACCS reactor accident consequence model

Helton¹,

Johnson²,

Shiver³

et al. 1995

Reliability Engineering & System Safety

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This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, make any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. DISCLAIMER Portions of this document may be illegible in electronic image products. Images are produced from the best avaiiable original document.

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The NUREG-1150 probabilistic risk assessment for the Sequoyah Nuclear Plant

Cited by 19 publications

References 7 publications

Illustration of sampling-based approaches to the calculation of expected dose in performance assessments for the proposed high level radioactive waste repository at Yucca Mountain, Nevada.

Illustration of sampling-based approaches to the calculation of expected dose in performance assessments for the proposed high level radioactive waste repository at Yucca Mountain, Nevada.

Survey of sampling-based methods for uncertainty and sensitivity analysis.

Uncertainty and sensitivity analysis of early exposure results with the MACCS reactor accident consequence model

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