Uncertainties in simulation models arise not only from the parameters that are used within the model, but also due to the modeling process itself—specifically the identification of a model that most accurately predicts the true physical response of interest. In risk-analysis studies, it is critical to consider the effect that all forms of uncertainty have on the overall level of uncertainty. This work develops an approach to quantify the effect of both parametric and model-form uncertainties. The developed approach is demonstrated on the assessment of the fatigue-based risk associated with a reactor pressure vessel subjected to a thermal shock event.
Issue Section:
Research Papers
References
1.
Odette
, G. R.
, and Lucas
, G. E.
, 2001
, “Embrittlement of Nuclear Reactor Pressure Vessels
,” JOM
, 53
(7
), pp. 18
–22
.2.
Pate-Cornell
, M. E.
, 1996
, “Uncertainties in Risk Analysis: Six Levels of Treatment
,” Reliab. Eng. Syst. Saf.
, 54
(1
), pp. 95
–111
.3.
Oberkampf
, W. L.
, Helton
, J. C.
, Joslyn
, C. A.
, Wojkiewicz
, S. F.
, and Ferson
, S.
, 2004
, “Challenge Problems: Uncertainty in System Response Given Uncertain Parameters
,” Reliab. Eng. Syst. Saf.
, 85
(1–3
), pp. 11
–19
.4.
Droguett
, E. L.
, and Mosleh
, A.
, 2008
, “Bayesian Methodology for Model Uncertainty Using Model Performance Data
,” Risk Anal.
, 28
(5
), pp. 1457
–1476
.5.
Ching
, J.
, Line
, H. D.
, and Yen
, M. T.
, 2009
, “Model Selection Issue in Calibrating Reliability-Based Resistance Factors Based on Geotechnical In-Situ Test Data
,” Struct. Saf.
, 31
(5
), pp. 420
–431
.6.
Park
, I.
, Amarchinta
, H. K.
, and Grandhi
, R. V.
, 2010
, “A Bayesian Approach for Quantification of Model Uncertainty
,” Reliab. Eng. Syst. Saf.
, 95
(1
), pp. 777
–785
.7.
Riley
, M. E.
, 2015
, “Evidence-Based Quantification of Uncertainties Induced via Simulation-Based Modeling
,” Reliab. Eng. Syst. Saf.
, 133
(1
), pp. 79
–86
.8.
Kline
, S. J.
, 1985
, “The Purpose of Uncertainty Analysis
,” ASME J. Fluids Eng.
, 107
(2
), pp. 153
–160
.9.
Dickson
, T.
, Williams
, P. T.
, and Yin
, S.
, 2012
, “Fracture Analysis of Vessels—Oak Ridge, FAVOR, v12.1, Computer Code: User's Guide
,” Technical Report, Oak Ridge National Laboratory, Oak Ridge, TN, Report No. ORNL/TM-2012/566
.10.
Buckner
, H. F.
, “A Novel Principle for the Computation of Stress Intensity Factors
,” Z. Angew. Math. Mech.
, 50
(1
), pp. 529
–546
.11.
Spencer
, B.
, Backman
, M.
, Hoffman
, W.
, and Chakraborty
, P.
, 2015
, “Reactor Pressure Vessel Integrity Assessments With the Grizzly Simulation Code
,” 23rd Structural Mechanics in Reactor Technology Conference
, Manchester, UK.12.
Kennedy
, M. C.
, and O'Hagan
, A.
, 2001
, “Bayesian Calibration of Computer Models
,” J. R. Stat. Soc.
, 63
(3
), pp. 425
–464
.13.
Riley
, M. E.
, and Grandhi
, R. V.
, 2011
, “Quantification of Model-Form and Predictive Uncertainty for Multi-Physics Simulation
,” Comput. Struct.
, 89
(1
), pp. 1206
–1213
.14.
Park
, I.
, and Grandhi
, R. V.
, 2012
, “Quantification of Model-Form and Parametric Uncertainty Using Evidence Theory
,” Struct. Saf.
, 39
(1
), pp. 44
–51
.15.
Dempster
, A. P.
, 1968
, “A Generalization of Bayesian Inference
,” J. R. Stat. Soc.
, 30
(1
), pp. 205
–247
.16.
Shafer
, G. A.
, 1976
, A Generalization of Bayesian Inference
, Princeton University Press
, Princeton, NJ
.17.
Sentz
, K.
, and Ferson
, S.
, 2002
, “Combination of Evidence in Dempster-Shafer Theory
,” Sandia National Laboratories, Albuquerque, NM, Report No. SAND 2002-0835
.18.
Smets
, P.
, 1993
, “Belief Functions: The Disjunctive Rule of Combination and the Generalized Bayesian Theorem
,” Int. J. Approximate Reasoning
, 9
(1
), pp. 1
–35
.19.
Teferra
, K.
, Shields
, M.
, Hapij
, A.
, and Daddazio
, R.
, 2014
, “Mapping Model Validation Metrics to Subject Matter Expert Scores for Model Adequacy Assessment
,” Reliab. Eng. Syst. Saf.
, 132
(1
), pp. 9
–19
.20.
Wasserman
, L.
, 2000
, “Bayesian Model Selection and Model Averaging
,” J. Math. Psychol.
, 44
(1
), pp. 92
–107
.21.
Eldred
, M. S.
, Swiler
, L. P.
, and Tang
, G.
, 2011
, “Mixed Aleatory-Epistemic Uncertainty Quantification With Stochastic Expansions and Optimization-Based Interval Estimation
,” Reliab. Eng. Syst. Saf.
, 96
(9
), pp. 1092
–1113
.22.
Shah
, H.
, Hosder
, S.
, and Winter
, T.
, 2015
, “A Mixed Uncertainty Quantification Approach Using Evidence Theory and Stochastic Expansions
,” Int. J. Uncertainty Quantif.
, 5
(1
), pp. 21
–48
.Copyright © 2016 by ASME
You do not currently have access to this content.