Polynomial chaos expansion for global sensitivity analysis applied to a model of radionuclide migration in a randomly heterogeneous aquifer

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• 作者：Valentina Ciriello (1)
  Vittorio Di Federico (1)
  Monica Riva (2)
  Francesco Cadini (3)
  Jacopo De Sanctis (3)
  Enrico Zio (3) (4)
  Alberto Guadagnini (2)
  
• 关键词：Performance assessment ; Radionuclide migration ; Heterogeneous aquifers ; Global sensitivity analysis ; Sobol indices ; Polynomial chaos expansion
• 刊名：Stochastic Environmental Research and Risk Assessment (SERRA)
• 出版年：2013
• 出版时间：May 2013
• 年：2013
• 卷：27
• 期：4
• 页码：945-954
• 全文大小：506 KB
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• 作者单位：Valentina Ciriello (1)
  Vittorio Di Federico (1)
  Monica Riva (2)
  Francesco Cadini (3)
  Jacopo De Sanctis (3)
  Enrico Zio (3) (4)
  Alberto Guadagnini (2)
  
  1. Dipartimento di Ingegneria Civile, Ambientale e dei Materiali, Universit脿 di Bologna, Bologna, Italy
  2. Dipartimento di Ingegneria Idraulica, Ambientale, Infrastrutture Viarie, Rilevamento, Politecnico di Milano, Milan, Italy
  3. Dipartimento di Energia, Politecnico di Milano, Milan, Italy
  4. Chair on Systems Science and the Energetic Challenge, European Foundation for New Energy鈥撁塴ectricit茅 de France, Ecole Centrale Paris and Supelec, Paris, France
  
• ISSN：1436-3259

文摘

We perform global sensitivity analysis (GSA) through polynomial chaos expansion (PCE) on a contaminant transport model for the assessment of radionuclide concentration at a given control location in a heterogeneous aquifer, following a release from a near surface repository of radioactive waste. The aquifer hydraulic conductivity is modeled as a stationary stochastic process in space. We examine the uncertainty in the first two (ensemble) moments of the peak concentration, as a consequence of incomplete knowledge of (a) the parameters characterizing the variogram of hydraulic conductivity, (b) the partition coefficient associated with the migrating radionuclide, and (c) dispersivity parameters at the scale of interest. These quantities are treated as random variables and a variance-based GSA is performed in a numerical Monte Carlo framework. This entails solving groundwater flow and transport processes within an ensemble of hydraulic conductivity realizations generated upon sampling the space of the considered random variables. The Sobol indices are adopted as sensitivity measures to provide an estimate of the role of uncertain parameters on the (ensemble) target moments. Calculation of the indices is performed by employing PCE as a surrogate model of the migration process to reduce the computational burden. We show that the proposed methodology (a) allows identifying the influence of uncertain parameters on key statistical moments of the peak concentration (b) enables extending the number of Monte Carlo iterations to attain convergence of the (ensemble) target moments, and (c) leads to considerable saving of computational time while keeping acceptable accuracy.