Effect of uncertainties in thermodynamic data and model parameters on calculated process performance

Whiting, Wallace B.; Tong, Ting Man; Reed, Michael E.

doi:10.1021/ie00019a011

Cited by 41 publications

(39 citation statements)

References 6 publications

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“…7.5). To keep the example at a convenient size, 3 independent variables (i.e.,~j) will be considered ( Chan 1996, Hamby 1995, Whiting et al 1993.…”

Section: Stability Of Resultsmentioning

confidence: 99%

“…Many additional examples of the use of PCCS in sampling-based sensitivity analysis also exist (e.g., , Hamby 1995, Whiting et al 1993, Breshears et al .1992 is distributed approximately normally with mean O and standard deviation 1 when x and y are uncorrelated, x and y have enough convergent moments (i.e., the tails of their distributions die off sufficiently rapidly), and m is sufficiently large (p. 631, Press et al 1992).…”

Section: Correlation and Partial Correlationmentioning

confidence: 99%

“…The rank transformation has become quite popular in sampling-based sensitivity analyses and many additional examples of its use exist (e.g., Sanchez and Blower 1997;Gwo et al 1996;Helton et al , 1989Hamby 1995;Blower and Dowlatabadi 1994;Whiting et al 1993;MacDonald and Campbell 1986).…”

Section: The Rank Transformationmentioning

confidence: 99%

See 2 more Smart Citations

Sampling-Based Methods for Uncertainty and Sensitivity Analysis

Helton¹,

Davis²

2000

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“…7.5). To keep the example at a convenient size, 3 independent variables (i.e.,~j) will be considered ( Chan 1996, Hamby 1995, Whiting et al 1993.…”

Section: Stability Of Resultsmentioning

confidence: 99%

Section: Correlation and Partial Correlationmentioning

confidence: 99%

See 1 more Smart Citation

Sampling-Based Methods for Uncertainty and Sensitivity Analysis

Helton¹,

Davis²

2000

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“…[21][22][23][24][25][26][40][41][42][70][71][72][73][74][75][76][77][78][79][80][81][82]such analyses are open to the criticism that there may not be enough information available to justify the definition of the distributions indicated in Eq. (2.4).…”

Section: Evidence Theorymentioning

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

127

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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...

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“…Sampling-based approaches to uncertainty and sensitivity analysis are both effective and widely used. [69][70][71][72][73][74][75][76][77][78][79][80][81][82][83] Analyses of this type involve the generation and exploration of a mapping from uncertain analysis inputs to uncertain analysis results. The underlying idea is that analysis results y(x) = [y 1 (x), y 2 (x), …, y nY (x)] are functions of uncertain analysis inputs x = [x 1 , x 2 , …, x nX ].…”

Section: Introductionmentioning

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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Effect of uncertainties in thermodynamic data and model parameters on calculated process performance

Cited by 41 publications

References 6 publications

Sampling-Based Methods for Uncertainty and Sensitivity Analysis

Sampling-Based Methods for Uncertainty and Sensitivity Analysis

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

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

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