Robustness of an uncertainty and sensitivity analysis of early exposure results with the MACCS reactor accident consequence model

Helton, Jon C.; Johnson, Jay; McKay, Michael D.; Shiver, A.W.; Sprung, J.L.

doi:10.1016/0951-8320(95)00001-i

Cited by 43 publications

(16 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such a replacement might be reasonable for uncertain numbers having a core. * This approach of pinching inputs and recalculating the assessment is familiar to Monte Carlo analysts (e.g., Iman 1987;Iman and Hora 1990;Helton et al 1995). …”

Section: Sensitivity Analyses Within the New Methodsmentioning

confidence: 99%

“…Although they know that it has pitfalls * , many analysts routinely conduct sensitivity studies of the proportional contribution of variability in each variable to the overall variability in the calculated risk distribution (e.g., Helton et al 1995;1997a,b). To determine the effects of variability in a Monte Carlo simulation using this method, each variable containing variability (i.e., expressed as a probability distribution) is reduced in turn to its mean or other appropriate point value, and the simulation is repeated.…”

Section: Sensitivity Analyses Within the New Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Sensitivity in risk analyses with uncertain numbers.

Tucker

Ferson

2006

View full text Add to dashboard Cite

Sensitivity analysis is a study of how changes in the inputs to a model influence the results of the model. Many techniques have recently been proposed for use when the model is probabilistic. This report considers the related problem of sensitivity analysis when the model includes uncertain numbers that can involve both aleatory and epistemic uncertainty and the method of calculation is Dempster-Shafer evidence theory or probability bounds analysis. Some traditional methods for sensitivity analysis generalize directly for use with uncertain numbers, but, in some respects, sensitivity analysis for these analyses differs from traditional deterministic or probabilistic sensitivity analyses. A case study of a dike reliability assessment illustrates several methods of sensitivity analysis, including traditional probabilistic assessment, local derivatives, and a "pinching" strategy that hypothetically reduces the epistemic uncertainty or aleatory uncertainty, or both, in an input variable to estimate the reduction of uncertainty in the outputs. The prospects for applying the methods to black box models are also considered.4

show abstract

Section: Sensitivity Analyses Within the New Methodsmentioning

confidence: 99%

Section: Sensitivity Analyses Within the New Methodsmentioning

confidence: 99%

Sensitivity in risk analyses with uncertain numbers.

Tucker

Ferson

2006

View full text Add to dashboard Cite

show abstract

“…Under these conditions, Latin hypercube sampling is a very effective sampling strategy because its dense stratification across the range of each uncertain input results in a good representation of model behavior regardless of which predicted variable is under consideration and which elements of x actually affect the uncertainty in this variable. [124][125][126][127] Step 3. Propagate the sample generated in Step 2 through the model to obtain values for all model results of interest.…”

Section: Ccpf For Ymentioning

confidence: 99%

A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory

Helton

Johnson²,

Oberkampf

et al. 2007

Computer Methods in Applied Mechanics and Engineering

Self Cite

127

View full text Add to dashboard Cite

Prepared by Sandia Nat ional Labor at ori es Albu querque , Ne w Mexico 87185 and Livermore , Ca liforn ia 94550Sandia is a mult iprogr am laborat or y operated by Sandia Corpo ratio n, a Lockh eed Mar tin Compa ny, for th e United States Depa rt ment of Energy's National Nuclear Securi ty Admi nistration under Contract DE-A C04-94AL 85000 .App rov ed for public release; furth er dissemination unlimited . (rI1) Sandia National LaboratoriesIssued by Sandia National Laboratories, operated for the United States Department of Energy by Sandia Corporation. NOTICE: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, nor any of their contractors, subcontractors, or their employees, make any warranty, express or implied, or assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represent 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, any agency thereof, or any of their contractors or subcontractors. The views and opinions expressed herein do not necessarily state or reflect those of the United States Government, any agency thereof, or any of their contractors.Printed in the United States of America. This report has been reproduced directly from the best available copy. AbstractEvidence theory provides an alternative to probability theory for the representation of epistemic uncertainty in model predictions that derives from epistemic uncertainty in model inputs, where the descriptor epistemic is used to indicate uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. The potential benefit, and hence appeal, of evidence theory is that it allows a less restrictive specification of uncertainty than is possible within the axiomatic structure on which probability theory is based. Unfortunately, the propagation of an evidence theory representation for uncertainty through a model is more computationally demanding than the propagation of a probabilistic representation for uncertainty, with this difficulty constituting a serious obstacle to the use of evidence theory in the representation of uncertainty in predictions obtained from computationally intensive models. This presentation describes and illustrates a samplingbased computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory. Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impr...

show abstract

“…For example, the totality of the model calculations (Le., BRAGFLO, NUTS, PANEL, GRASP-INV, SECOFL2D, SECOTP2D, CUTTINGSS, BRAGFLO-DBR) in the 1996 WIPP PA is too extensive to permit the generation of 1000's of CCDFs in an uncertainty/sensitivity study to assess the effects of subjective uncertainty on compliance Helton et al 1995b). …”

Section: -6mentioning

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

Self Cite

View full text Add to dashboard Cite

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...

show abstract

Robustness of an uncertainty and sensitivity analysis of early exposure results with the MACCS reactor accident consequence model

Cited by 43 publications

References 17 publications

Sensitivity in risk analyses with uncertain numbers.

Sensitivity in risk analyses with uncertain numbers.

A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory

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

Contact Info

Product

Resources

About