Calculation of reactor accident safety goals

Helton, Jon C.; Breeding, R.J.

doi:10.1016/0951-8320(93)90038-z

Cited by 85 publications

(39 citation statements)

References 29 publications

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“…In contrast, stochastic components vary randomly from moment to moment within a single ABM simulation. Some studies have applied a stochastic component to otherwise deterministic models by sampling a random sequence of values prior to running a model simulation (Helton, 1999;Helton & Breeding, 1993;Helton et al, 1995). However, this approach cannot be easily applied to ABMs as presented here: stochastic decisions made by each agent at each time step are based on conditional probabilities that depend on both random chance and the state of other interacting agents.…”

Section: Uncertainty and Sensitivity Analysis In Agent-based Modelsmentioning

confidence: 99%

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

Marino

Hogue

Ray

et al. 2008

Journal of Theoretical Biology

2,272

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: Uncertainty and Sensitivity Analysis In Agent-based Modelsmentioning

confidence: 99%

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

Marino

Hogue

Ray

et al. 2008

Journal of Theoretical Biology

2,272

1,892

View full text Add to dashboard Cite

show abstract

“…Actually, this construction is an example of the Farmer limit line approach to the definition of acceptable risk (Farmer 1967, Cox and Baybutt 1982, Munera and Yadigaroglu 1986. A similar construction was used in the NUREG-1150 analyses (U.S. NRC 1990, Breeding et al 1992 to implement the proposed large release safety goal for reactor accidents (U.S. NRC 1986, Helton andBreeding 1993). Thus, again, the 1996 WIPP PA involves widely used ideas, although the actual scale of the analysis is much larger than that of a typical PA. …”

Section: -13mentioning

confidence: 99%

Uncertainty and sensitivity analysis results obtained in the 1992 performance assessment for the waste isolation pilot plant

Helton

Anderson

Baker

et al. 1996

Reliability Engineering & System Safety

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The Waste Isolation Pilot Plant (WPP) is located in southeastern New Mexico and is being developed by the U.S. Department of Energy (DOE) for the geologic (deep underground) disposal of transuranic waste. A detailed performance assessment (PA) for the WIPP was carried out in 1996 and supports an application by the DOE to the U.S. Environmental Protection Agency (EPA) for the certification of the WIPP for the disposal of TRU waste. The 1996 WIPP PA uses a computational structure that maintains a separation between stochastic (i.e., aleatory) and subjective (Le., epistemic) uncertainty, with stochastic uncertainty arising from the many possible disruptions that could occur over the 10,000 yr regulatory period that applies to the WIPP and subjective uncertainty arising from the imprecision with which many of the quantities required in the PA are known. Important parts of this structure are (1) the use of Latin hypercube sampling to incorporate the effects of subjective uncertainty, (2) the use of Monte Carlo (Le., random) sampling to incorporate the effects of stochastic uncertainty, and (3) the efficient use of the necessarily limited number of mechanistic calculations that can be performed to support the analysis. The use of Latin hypercube sampling generates a mapping from imprecisely known analysis inputs to analysis outcomes of interest that provides both a display of the uncertainty in analysis outcomes (i.e., uncertainty analysis) and a basis for investigating the effects of individual inputs on these outcomes (i.e., sensitivity analysis). The sensitivity analysis procedures used in the PA include examination of scatterplots, stepwise regression analysis, and partial correlation analysis. Uncertainty and sensitivity analysis results obtained as part of the 1996 WIPP PA are presented and discussed. Specific topics considered include two phase flow in the vicinity of the repository, radionuclide release from the repository, fluid flow and radionuclide transport in formations overlying the repository, and complementary cumulative distribution functions used in comparisons with regulatory standards (i.e., 40 CFR 191, Subpart B). 12-6Transport . . .... A-24Time-dependent radionuclide inventories expressed in EPA units (i.e., the normalized units used in showing compliance with 191.13(a)) for entire repository (Sanchez et al. 1997). Left frame shows radionuclides included in groundwater transport calculations; right frame shows radionculides not included in groundwater transport because of low inventory or short half-life. All radionuclides shown are included in estimates of cuttings and cavings and spallings; direct brine releases included all except . 4-75Flowing bottom hole pressure (FBHP) (Le., p,,in Eq. (4.7.22)) as a function of brine well index (Le., J b in Eq. (4.7.5)) and panel pressure for a system.with only mobile brine (i.e., krg = 0) ( . 4-78Flowing bottom hole pressure (FBHP) (Le., p,,in Eq. (4.7.22)) as a function of relative permeabilities and panel pressure for a brine dominated sys...

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“…We distinguish risk-informed decisions from risk-based decisions. The latter implies that any decision is solely dependent on absolute results of the study, whereas most high-consequence decisions are ultimately assertion-based (Helton and Breeding, 1993;. Assertion-based decisions can be risk-informed while allowing the decision maker to subjectively weigh technical and programmatic "intangibles."…”

Section: Introductionmentioning

confidence: 99%

The method of belief scales as a means for dealing with uncertainty in tough regulatory decisions.

Pilch¹

2005

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Modeling and simulation is playing an increasing role in supporting tough regulatory decisions, which are typically characterized by variabilities and uncertainties in the scenarios, input conditions, failure criteria, model parameters, and even model form. Variability exists when there is a statistically significant database that is fully relevant to the application. Uncertainty, on the other hand, is characterized by some degree of ignorance. A simple algebraic problem was used to illustrate how various risk methodologies address variability and uncertainty in a regulatory context. These traditional risk methodologies include probabilistic methods (including frequensic and Bayesian perspectives) and second-order methods where variabilities and uncertainties are treated separately. Representing uncertainties with (subjective) probability distributions and using probabilistic methods to propagate subjective distributions can lead to results that are not logically consistent with available knowledge and that may not be conservative. The Method of Belief Scales (MBS) is developed as a means to logically aggregate uncertain input information and to propagate that information through the model to a set of results that are scrutable, easily interpretable by the nonexpert, and logically consistent with the available input information. The MBS, particularly in conjunction with sensitivity analyses, has the potential to be more computationally efficient than other risk methodologies. The regulatory language must be tailored to the specific risk methodology if ambiguity and conflict are to be avoided. 4Intentionally Left Blank 5

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Calculation of reactor accident safety goals

Cited by 85 publications

References 29 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

Uncertainty and sensitivity analysis results obtained in the 1992 performance assessment for the waste isolation pilot plant

The method of belief scales as a means for dealing with uncertainty in tough regulatory decisions.

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