IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v107y2012icp190-204.html
   My bibliography  Save this article

EFAST analysis applied to a PA model for a generic HLW repository in clay

Author

Listed:
  • Spiessl, S.M.
  • Becker, D.-A.
  • Rübel, A.

Abstract

This paper contributes to the investigation whether sophisticated methods, i.e., the EFAST method are suitable for use in long-term safety assessments of geological repositories for radioactive wastes and also whether they provide deeper insight into a repository system than less sophisticated methods such as rank-based regression or correlation or non-parametric methods, scatterplots or CSM plots. A generic test case for a HLW repository in a clay formation was selected. Despite the skewed and heavily tailed distribution of the model output, the EFAST results were surprisingly good compared to results previously obtained for a repository in rock salt. The important difference between the PA models in clay and salt is that the former produces a less skewed and less scattered distribution of the annual dose than the latter. As a result, the variance of the model output and the sensitivity indices are more stable. For the considered model, the EFAST method yields quantitative information about the parameter importance that cannot be obtained using linear regression, correlation or graphical methods. It is therefore a valuable tool, especially in combination with other sensitivity analysis methods.

Suggested Citation

  • Spiessl, S.M. & Becker, D.-A. & Rübel, A., 2012. "EFAST analysis applied to a PA model for a generic HLW repository in clay," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 190-204.
    • Handle: RePEc:eee:reensy:v:107:y:2012:i:c:p:190-204
    DOI: 10.1016/j.ress.2012.04.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832012001056
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2012.04.012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tarantola, S. & Kopustinskas, V. & Bolado-Lavin, R. & Kaliatka, A. & Ušpuras, E. & Vaišnoras, M., 2012. "Sensitivity analysis using contribution to sample variance plot: Application to a water hammer model," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 62-73.
    2. Bolado-Lavin, R. & Castaings, W. & Tarantola, S., 2009. "Contribution to the sample mean plot for graphical and numerical sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(6), pages 1041-1049.
    3. Saltelli, Andrea & Bolado, Ricardo, 1998. "An alternative way to compute Fourier amplitude sensitivity test (FAST)," Computational Statistics & Data Analysis, Elsevier, vol. 26(4), pages 445-460, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Soha Saad & Florence Ossart & Jean Bigeon & Etienne Sourdille & Harold Gance, 2021. "Global Sensitivity Analysis Applied to Train Traffic Rescheduling: A Comparative Study," Energies, MDPI, vol. 14(19), pages 1-29, October.
    2. Wei, Pengfei & Lu, Zhenzhou & Ruan, Wenbin & Song, Jingwen, 2014. "Regional sensitivity analysis using revised mean and variance ratio functions," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 121-135.
    3. Cheng, Lei & Lu, Zhenzhou & Zhang, Leigang, 2015. "Application of Rejection Sampling based methodology to variance based parametric sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 9-18.
    4. Mirko Ginocchi & Ferdinanda Ponci & Antonello Monti, 2021. "Sensitivity Analysis and Power Systems: Can We Bridge the Gap? A Review and a Guide to Getting Started," Energies, MDPI, vol. 14(24), pages 1-59, December.
    5. Wei, Pengfei & Lu, Zhenzhou & Song, Jingwen, 2015. "Variable importance analysis: A comprehensive review," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 399-432.
    6. Di Maio, Francesco & Bandini, Alessandro & Zio, Enrico & Alberola, Sofia Carlos & Sanchez-Saez, Francisco & Martorell, Sebastián, 2016. "Bootstrapped-ensemble-based Sensitivity Analysis of a trace thermal-hydraulic model based on a limited number of PWR large break loca simulations," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 122-134.
    7. Pengfei Wei & Zhenzhou Lu & Jingwen Song, 2014. "Uncertainty Importance Analysis Using Parametric Moment Ratio Functions," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 223-234, February.
    8. Li, Luyi & Lu, Zhenzhou & Hu, JiXiang, 2014. "A new kind of regional importance measure of the input variable and its state dependent parameter solution," Reliability Engineering and System Safety, Elsevier, vol. 128(C), pages 1-16.
    9. Yeratapally, Saikumar R. & Glavicic, Michael G. & Argyrakis, Christos & Sangid, Michael D., 2017. "Bayesian uncertainty quantification and propagation for validation of a microstructure sensitive model for prediction of fatigue crack initiation," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 110-123.
    10. Plischke, Elmar, 2012. "An adaptive correlation ratio method using the cumulative sum of the reordered output," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 149-156.
    11. Wei, Pengfei & Lu, Zhenzhou & Song, Jingwen, 2015. "Regional and parametric sensitivity analysis of Sobol׳ indices," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 87-100.
    12. H. Christopher Frey & Sumeet R. Patil, 2002. "Identification and Review of Sensitivity Analysis Methods," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 553-578, June.
    13. Yun, Wanying & Lu, Zhenzhou & Feng, Kaixuan & Li, Luyi, 2019. "An elaborate algorithm for analyzing the Borgonovo moment-independent sensitivity by replacing the probability density function estimation with the probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 99-108.
    14. Brown, S. & Beck, J. & Mahgerefteh, H. & Fraga, E.S., 2013. "Global sensitivity analysis of the impact of impurities on CO2 pipeline failure," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 43-54.
    15. Tarantola, S. & Kopustinskas, V. & Bolado-Lavin, R. & Kaliatka, A. & Ušpuras, E. & Vaišnoras, M., 2012. "Sensitivity analysis using contribution to sample variance plot: Application to a water hammer model," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 62-73.
    16. Messan, Komi & Rodriguez Messan, Marisabel & Chen, Jun & DeGrandi-Hoffman, Gloria & Kang, Yun, 2021. "Population dynamics of Varroa mite and honeybee: Effects of parasitism with age structure and seasonality," Ecological Modelling, Elsevier, vol. 440(C).
    17. Kieran Alden & Mark Read & Jon Timmis & Paul S Andrews & Henrique Veiga-Fernandes & Mark Coles, 2013. "Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems," PLOS Computational Biology, Public Library of Science, vol. 9(2), pages 1-9, February.
    18. Pannier, S. & Graf, W., 2015. "Sectional global sensitivity measures," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 110-117.
    19. Xing Liu & Enrico Zio & Emanuele Borgonovo & Elmar Plischke, 2024. "A Systematic Approach of Global Sensitivity Analysis and Its Application to a Model for the Quantification of Resilience of Interconnected Critical Infrastructures," Energies, MDPI, vol. 17(8), pages 1-24, April.
    20. Wang, Fugui & Mladenoff, David J. & Forrester, Jodi A. & Keough, Cindy & Parton, William J., 2013. "Global sensitivity analysis of a modified CENTURY model for simulating impacts of harvesting fine woody biomass for bioenergy," Ecological Modelling, Elsevier, vol. 259(C), pages 16-23.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:107:y:2012:i:c:p:190-204. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.