Use of global sensitivity analysis and polynomial chaos expansion for interpretation of nonreactive transport experiments in laboratory-scale porous media

Water Resources Research

Volumn 47, Issue 2, 2011, Pages

  Fajraoui, Noura ^a   Ramasomanana, Fanilo ^a   Younes, Anis ^a   Mara, Thierry Alex ^b   Ackerer, Philippe ^a   Guadagnini, Alberto ^c  

^a UNIVERSITÉ DE STRASBOURG   (France)

^b UNIVERSITÉ DE LA RÉUNION   (France)

^c POLITECNICO DI MILANO   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

DISPERSIVE PARAMETERS; GLOBAL SENSITIVITY; GLOBAL SENSITIVITY ANALYSIS; HETEROGENEOUS POROUS MEDIA; IDENTIFIABILITY; IMPORTANCE MEASURE; INFLUENTIAL FACTORS; LABORATORY SCALE; MEASUREMENT POINTS; MODEL-BASED; POLYNOMIAL CHAOS EXPANSION; POROUS MEDIA; POROUS MEDIUM; PROPER DESIGN; PSEUDO STEADY STATE; SPACE TIME DISTRIBUTION; SURROGATE MODEL; TRANSPORT EXPERIMENTS; TRANSPORT MODELS; UNCERTAIN PARAMETERS;

EXPERIMENTS; GROUND STATE; POROUS MATERIALS; UNCERTAINTY ANALYSIS;

SENSITIVITY ANALYSIS;

BOUNDARY CONDITION; CALIBRATION; COMPUTER SIMULATION; EXPERIMENTAL STUDY; HYDRAULIC CONDUCTIVITY; LABORATORY METHOD; MEASUREMENT METHOD; NUMERICAL MODEL; SENSITIVITY ANALYSIS; STEADY-STATE EQUILIBRIUM; TRANSPORT PROCESS; UNCERTAINTY ANALYSIS;

EID: 79951778390     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2010WR009639     Document Type: Article

References (36)

1. Acharjee, S., and N. Zabaras (2007), A non-intrusive stochastic Galerkin approach for modeling uncertainty propagation i (Pubitemid 46135638)
2. Berkowitz, B., A. Cortis, M. Dentz, and H. Scher (2006), Modeling non- Fickian transport in geological formations as a continuous time random walk, Rev. Geophys., 44, RG2003, doi:10.1029/2005RG000178. (Pubitemid 46044201)
3. Cirpka, O. A., A. Olsson, Q. Ju, M. D. A. Rahman, and P. Grathwohl (2006), Determination of transverse dispersion coefficients from reactive plume lengths, Ground Water, 44, 212-221. (Pubitemid 43362902)
4. Dullien, F. A. L. (1979), Porous Media: Fluid Transport and Pore Structure, Academic, New York.
5. Frey, C. H., and S. R. Patil (2002), Identification and review of sensitivity analysis methods, Risk Anal., 22, 553-578. (Pubitemid 34655123)
6. Ghanem, R. G., and P. D. Spanos (1991), Stochastic Finite Elements-A Spectral Approach, Springer, Berlin.
7. Gramling, C, C. F. Harvey, and L. Meigs (2002), Reactive transport in porous media: A comparison of model prediction with laboratory visualization, Environ. Sci. Technol., 36, 2508-2514. (Pubitemid 34595192)
8. Haupt, R. L., and S. E. Haupt (2004), Practical Genetic Algorithms, 2nd ed., Wiley, New York.
9. Homma, T., and A. Saltelli (1996), Importance measures in global sensitivity analysis of nonlinear models, Reliab. Eng. Syst. Safety, 52, 1-17. (Pubitemid 126374988)
10. Huang, S. P., S. Mahadevan, and R. Rebba (2007), Collocation-based stochastic finite element analysis for random field problems, Probab. Eng. Mech., 22, 194-205. (Pubitemid 46341514)
11. Katz, G. E., B. Berkowitz, A. Guadagnini, and M. W. Saaltink (2010), Experimental and modeling investigation of multicomponent reactive transport in porous media, J. Contam. Hydrol., doi:10.1016/j.jconhyd.2009. 11.002, in press.
12. Kiparissides, A., S. S. Kucherenko, A. Mantalaris, and E. N. Pistikopoulos (2009), Global sensitivity analysis challenges in biological systems modeling, Ind. Eng. Chem. Res., 48, 7168-7180.
13. Kontoravdi, C., S. P. Asprey, E. N. Pistikopoulos, and A. Mantalaris (2005), Application of global sensitivity analysis to determine goals for design of experiments: An example study on antibody-producing cell cultures, Biotechnol. Prog., 21, 1128-1135. (Pubitemid 41102248)
14. Konz, M., P. Ackerer, A. Younes, P. Huggenberger, and E. Zechner (2009a), Two-dimensional stable-layered laboratory-scale experiments for testing density-coupled flow models, Water Resour. Res., 45, W02404, doi:10.1029/2008WR007118.
15. Konz, M., P. Ackerer, P. Huggenberger, and C. Veit (2009b), Comparison of light transmission and reflection techniques to determine concentrations in flow tank experiments, Exp. Fluids, 47, 85-93.
16. Li, H., and D. Zhang (2007), Probabilistic collocation method for flow in porous media: Comparisons with other stochastic methods, Water Resour. Res., 43, W09409, doi:10.1029/2006WR005673. (Pubitemid 350064084)
17. Li, H., and D. Zhang (2009), Efficient and accurate quantification of uncertainty for multiphase flow with probabilistic collocation method, SPE J., 14, 665-679, doi:10.2118/114802-PA.
18. Li, W., Z. Lu, and D. Zhang (2009), Stochastic analysis of unsaturated flow with probabilistic collocation method, Water Resour. Res., 45, W08425, doi:10.1029/2008WR007530.
19. Marrel, A., B. Iooss, B. Laurent, and O. Roustant (2009), Calculations of Sobol indices for the Gaussian process metamodel, Reliab. Eng. Syst. Safety, 94, 742-751.
20. Oates, P.M., and C. F. Harvey (2006), A colorimetric reaction to quantify fluid mixing, Exp. Fluids, 41, 673-683.
21. Saaltink, M. W., F. Batlle, C. Ayora, J. Carrera, and S. Olivella (2004), RETRASO, a code for modeling reactive transport in saturated and unsaturated porous media, Geol. Acta, 2(3), 235-251. (Pubitemid 39239197)
22. Saltelli, A., M. Ratto, S. Tarantola, and F. Campolongo (2006), Sensitivity analysis practices: Strategies for model-based inference, Reliab. Eng. Syst. Safety, 91, 1109-1125.
23. Sobol, I. M. (1993), Sensitivity estimates for nonlinear mathematical models, Math. Model. Comput., 1, 407-414.
24. Sobol, I.M. (2001), Global sensitivity indices for nonlinear mathematical models and theirMonte Carlo estimates,Math. Comput. Simul., 55, 271-280. (Pubitemid 32672729)
25. Soize, C., and R. Ghanem (2004), Physical systems with random uncertainties: Chaos representations with arbitrary probability measure, SIAM J. Sci. Comput., 26, 395-410. (Pubitemid 40449850)
26. Sudret, B. (2008), Global sensitivity analysis using polynomial chaos expansions, Reliab. Eng. Syst. Safety, 93, 964-979. (Pubitemid 351313888)
27. Tatang, M. A., W. W. Pan, R. G. Prin, and G. J. McRae (1997), An efficient method for parametric uncertainty analysis of numerical geophysical models, J. Geophys. Res., 102, 21925-21932.
28. van Riel, N. A. W. (2006), Dynamic modelling and analysis of biochemical networks : Mechanism-based models and model-based experiments, Brief. Bioinform., 7, 364-374.
29. Volkova, E., B. Iooss, and F. Van Dorpe (2008), Global sensitivity analysis for a numerical model of radionuclide migration from the RRC "Kurchatov Institute" radwaste disposal site, Stoch. Environ. Res. Risk Assess., 22, 17-31. (Pubitemid 350142755)
30. Wiener, N. (1938), The homogeneous chaos, Am. J. Math., 60, 897-936.
31. Xiu, D., and G. E. Karniadakis (2002), The Weiner-Askey Polynomial Chaos for stochastic differential equations, SIAM J. Sci. Comput., 24(2), 619-644, doi:10.1137/S1064827501387826.
32. Xiu, D., and G. E. Karniadakis (2003), A new stochastic approach to transient heat conduction modeling with uncertainty, Int. J. Heat Mass Transfer, 46, 4681-4693. (Pubitemid 37111155)
33. Younes, A., and P. Ackerer (2005), Solving the advection-diffusion equation with the Eulerian-Lagrangian localized adjoint method on unstructured meshes and non uniform time stepping, J. Comput. Phys., 208, 384-402. (Pubitemid 40714228)
34. Younes, A., P. Ackerer, and F. Lehman (2006), A new efficient Eulerian- Lagrangian localized adjoint method for solving the advection-dispersion equation on unstructured meshes, Adv. Water. Resour., 29, 1056-1074. (Pubitemid 43812207)
35. Younes, A., M. Fahs, and P. Ackerer (2008), A new approach to avoid excessive numerical diffusion in Eulerian-Lagrangian methods, Comm. Numer. Methods Eng., 24, 897-910.
36. Zhang, D., L. Shi, H. Chang, and J. Yang (2010), A comparative study of numerical approaches to risk assessment of contaminant transport, Stoch. Environ. Res. Risk Assess., in press.