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Higher-order sensitivity analysis of a final repository model with discontinuous behaviour using the RS-HDMR meta-modeling approach

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  • Spiessl, Sabine M.
  • Kucherenko, Sergei
  • Becker, Dirk-A.
  • Zaccheus, Oluyemi

Abstract

Sensitivity analysis is considered a useful tool for determining sensitivities and assessing uncertainties of computational models, which is critical for the performance assessment of final repository models. One group of methods of sensitivity analysis is variance-based methods, which can identify sensitivities of individual parameters and parameter interactions. This is done via computation of Sobol’ sensitivity indices of first and higher orders.

Suggested Citation

  • Spiessl, Sabine M. & Kucherenko, Sergei & Becker, Dirk-A. & Zaccheus, Oluyemi, 2019. "Higher-order sensitivity analysis of a final repository model with discontinuous behaviour using the RS-HDMR meta-modeling approach," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 149-158.
    • Handle: RePEc:eee:reensy:v:187:y:2019:i:c:p:149-158
    DOI: 10.1016/j.ress.2018.12.004
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    References listed on IDEAS

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    1. Spiessl, Sabine M. & Becker, Dirk-A., 2015. "Sensitivity analysis of a final repository model with quasi-discrete behaviour using quasi-random sampling and a metamodel approach in comparison to other variance-based techniques," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 287-296.
    2. Tarantola, S. & Gatelli, D. & Mara, T.A., 2006. "Random balance designs for the estimation of first order global sensitivity indices," Reliability Engineering and System Safety, Elsevier, vol. 91(6), pages 717-727.
    3. Lambert, Romain S.C. & Lemke, Frank & Kucherenko, Sergei S. & Song, Shufang & Shah, Nilay, 2016. "Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 128(C), pages 42-54.
    4. Kucherenko, Sergei & Feil, Balazs & Shah, Nilay & Mauntz, Wolfgang, 2011. "The identification of model effective dimensions using global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 440-449.
    5. Plischke, Elmar, 2010. "An effective algorithm for computing global sensitivity indices (EASI)," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 354-360.
    6. Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
    7. Kucherenko, S. & Song, S., 2017. "Different numerical estimators for main effect global sensitivity indices," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 222-238.
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    Cited by:

    1. Saveleva, Elena & Svitelman, Valentina & Blinov, Petr & Valetov, Dmitry, 2021. "Sensitivity analysis and model calibration as a part of the model development process in radioactive waste disposal safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 210(C).

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