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Research Papers: Design and Analysis

Sensitivity Analysis of Fitness-for-Service Assessment Based on Reliability for Cylindrical Pressure Vessels With Local Metal Loss

[+] Author and Article Information
Takuyo Kaida

Sumitomo Chemical Co., Ltd.,
5-l, Sobiraki-cho,
Niihama City,
Ehime 792-8521, Japan
e-mail: kaidat@sc.sumitomo-chem.co.jp

Satoshi Izumi

e-mail: izumi@fml.t.u-tokyo.ac.jp

Shinsuke Sakai

e-mail: sakai@fml.t.u-tokyo.ac.jp
The University of Tokyo,
7-3-1, Hongo,
Bunkyo-ku,
Tokyo 113-8656, Japan

Contributed by the Pressure Vessel and Piping Division of ASME for publication in the JOURNAL OF PRESSURE VESSEL TECHNOLOGY. Manuscript received January 7, 2013; final manuscript received March 19, 2013; published online September 18, 2013. Assoc. Editor: David L. Rudland.

J. Pressure Vessel Technol 135(6), 061202 (Sep 18, 2013) (8 pages) Paper No: PVT-13-1008; doi: 10.1115/1.4024455 History: Received January 07, 2013; Revised March 19, 2013

Concern about fitness-for-service (FFS) assessments using stochastic analyses for aged pressure equipment with local metal loss has been growing. When a decision must be made regarding whether to run or repair equipment with local metal loss, a structural integrity assessment based on reliability helps. In analyses of failure probability, it is important to identify which variables strongly affect the structural integrity. The stochastic properties of influential parameters must be clarified, but few data have been published regarding the quantitative analysis of the sensitivity of the parameters in FFS assessments of components with local metal loss. Here, we investigated the effects of parameters on the plastic collapse of a damaged cylindrical pressure vessel with local metal loss, in an evaluation of parameter sensitivity. We also analyzed sensitivity indices for the component with several shapes of local metal loss. We found that the corrosion rate has a major influence on the probability of failure. We propose a practical stochastic analysis procedure for components with local metal loss. In this procedure, the parameter that has consistently low sensitivity to the limit state is used as a constant value.

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References

Figures

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Fig. 1

Flow chart for development of practical limit state model

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Fig. 3

Longitudinal section at the region of metal loss

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Fig. 2

Sensitivity indices in standard normal space

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Fig. 4

Probability of failure assessment curve in case in which all variables are treated as random variables

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Fig. 5

Calculation results of sensitivity indices: Pf = 1 × 10−4

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Fig. 10

Cumulative density function of Type I and Weibull distribution for CV

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Fig. 11

Exceedance probabilities of Type I and Weibull distributions for CV

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Fig. 6

Calculation results of sensitivity indices: Pf = 1 × 10−5

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Fig. 7

Calculation results of sensitivity indices: Pf = 1 × 10−6

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Fig. 8

Assessment curve of probability of failure in case that CV and tmm are treated as random variables

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Fig. 9

Probability of failure assessment curve utilizing the modified limit state model, Xm = 1.2

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