Probabilistic Risk Assessment Based Guidance for Piping In-Service Inspection

Vo, T.V.; Gore, B.F.; Eschbach, E.J.; Simonen, F.A.

doi:10.13182/nt89-a34332

Cited by 11 publications

(4 citation statements)

References 1 publication

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“…In one of the examples that follows, we use a linear model with five factors and one response variable, and the total number of terms on the right hand side of eq. (2) is 2 5 , or 32.…”

Section: 2mentioning

confidence: 99%

“…In addition to the three factors we already selected in DEX-1 and DEX-2, we need to add two more factors, X4 and X5, to make k = 5. A full factorial DEX for k = 5 requires 2 5 (=32) runs. A 5-factor, 8-run DEX is known as a fractional factorial orthogonal design, which is more economical but not as accurate, because a DATAPLOT-10-Step-Analysis will show in its 7th plot complications due to the confounding of one-way and two-way effects of a first-order model (see Fig.…”

Section: Design Of Experiments: Dex-3 (5 Factors)mentioning

confidence: 99%

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Design of a PYTHON-Based Plug-In for Benchmarking Probabilistic Fracture Mechanics Computer Codes With Failure Event Data

Fong

deWit

Marcal³

et al. 2009

Volume 6: Materials and Fabrication, Parts a and B

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In a 2007 paper entitled “Application of Failure Event Data to Benchmark Probabilistic Fracture Mechanics (PFM) Computer Codes” (Simonen, F. A., Gosselin, S. R., Lydell, B. O. Y., Rudland, D. L., & Wikowski, G. M. Proc. ASME PVP Conf., San Antonio, TX, Paper PVP2007-26373), it was reported that the two benchmarked PFM models, PRO-LOCA and PRAISE, predicted significantly higher failure probabilities of cracking than those derived from field data in three PWR and one BWR cases by a factor ranging from 30 to 10,000. To explain the reasons for having such a large discrepancy, the authors listed ten sources of uncertainties: (1) Welding Residual Stresses. (2) Crack Initiation Predictions. (3) Crack Growth Rates. (4) Circumferential Stress Variation. (5) Operating temperatures different from design temperatures. (6) Temperature factor in actual activation energy vs. assumed. (7) Under reporting of field data due to NDE limitations. (8) Uncertainty in modeling initiation, growth, and linking of multiple cracks around the circumference of a weld. (9) Correlation of crack initiation times and growth rates. (10) Insights from NUREG/CR-6674 (2000) fatigue crack growth models using conservative inputs for cyclic strain rates and environmental parameters such as oxygen content. In this paper we design a Python-based plug-in that allows a user to address those ten sources of uncertainties. This approach is based on the statistical theory of design of experiments with a 2-level factorial design, where a small number of runs is enough to estimate the uncertainties in the predictions of PFM models due to some combination of the source uncertainties listed by Simonen et al (PVP2007-26373).

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“…In one of the examples that follows, we use a linear model with five factors and one response variable, and the total number of terms on the right hand side of eq. (2) is 2 5 , or 32.…”

Section: 2mentioning

confidence: 99%

Section: Design Of Experiments: Dex-3 (5 Factors)mentioning

confidence: 99%

Design of a PYTHON-Based Plug-In for Benchmarking Probabilistic Fracture Mechanics Computer Codes With Failure Event Data

Fong

deWit

Marcal³

et al. 2009

Volume 6: Materials and Fabrication, Parts a and B

View full text Add to dashboard Cite

show abstract

“…it is the sum of the cut sets involving the basic event divided by the sum of all cut sets. [3,4]. In the case of ISI, it will be carried out for piping segments from process systems as well as safety systems.…”

Section: Importance Measuresmentioning

confidence: 99%

“…importance measures, matrix definition, etc. have been evolved [3,4].The various steps in carrying out RI-ISI can be summarized as below: † Identification of systems and boundaries using information from a plant PSA. † Ranking of components applying the risk measures to determine the categories that are then reviewed to add deterministic insights in making final selection of where to focus ISI resources.…”

mentioning

confidence: 99%