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Response surfaces and sensitivity analyses for an environmental model of dose calculations

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  • Iooss, Bertrand
  • Van Dorpe, François
  • Devictor, Nicolas

Abstract

A parametric sensitivity analysis is carried out on GASCON, a radiological impact software describing the radionuclides transfer to the man following a chronic gas release of a nuclear facility. An effective dose received by age group can thus be calculated according to a specific radionuclide and to the duration of the release. In this study, we are concerned by 18 output variables, each depending of approximately 50 uncertain input parameters. First, the generation of 1000 Monte-Carlo simulations allows us to calculate correlation coefficients between input parameters and output variables, which give a first overview of important factors. Response surfaces are then constructed in polynomial form, and used to predict system responses at reduced computation time cost; this response surface will be very useful for global sensitivity analysis where thousands of runs are required. Using the response surfaces, we calculate the total sensitivity indices of Sobol by the Monte-Carlo method. We demonstrate the application of this method to one site of study and to one reference group near the nuclear research Center of Cadarache (France), for two radionuclides: iodine 129 and uranium 238. It is thus shown that the most influential parameters are all related to the food chain of the goat's milk, in decreasing order of importance: dose coefficient “effective ingestion†, goat's milk ration of the individuals of the reference group, grass ration of the goat, dry deposition velocity and transfer factor to the goat's milk.

Suggested Citation

  • Iooss, Bertrand & Van Dorpe, François & Devictor, Nicolas, 2006. "Response surfaces and sensitivity analyses for an environmental model of dose calculations," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1241-1251.
  • Handle: RePEc:eee:reensy:v:91:y:2006:i:10:p:1241-1251
    DOI: 10.1016/j.ress.2005.11.021
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    References listed on IDEAS

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    1. Kleijnen, Jack P. C., 2005. "An overview of the design and analysis of simulation experiments for sensitivity analysis," European Journal of Operational Research, Elsevier, vol. 164(2), pages 287-300, July.
    2. Kleijnen, Jack P. C. & Sargent, Robert G., 2000. "A methodology for fitting and validating metamodels in simulation," European Journal of Operational Research, Elsevier, vol. 120(1), pages 14-29, January.
    3. Kleijnen, J.P.C., 1997. "Sensitivity analysis and related analyses : A review of some statistical techniques," Other publications TiSEM 7969b135-47c5-4d76-9241-c, Tilburg University, School of Economics and Management.
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    1. Iooss, Bertrand & Ribatet, Mathieu, 2009. "Global sensitivity analysis of computer models with functional inputs," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1194-1204.
    2. Venelin Todorov & Slavi Georgiev & Ivan Georgiev & Snezhinka Zaharieva & Ivan Dimov, 2023. "Optimizing Air Pollution Modeling with a Highly-Convergent Quasi-Monte Carlo Method: A Case Study on the UNI-DEM Framework," Mathematics, MDPI, vol. 11(13), pages 1-14, June.
    3. Venelin Todorov & Ivan Dimov, 2022. "Innovative Digital Stochastic Methods for Multidimensional Sensitivity Analysis in Air Pollution Modelling," Mathematics, MDPI, vol. 10(12), pages 1-14, June.
    4. Auder, Benjamin & De Crecy, Agnès & Iooss, Bertrand & Marquès, Michel, 2012. "Screening and metamodeling of computer experiments with functional outputs. Application to thermal–hydraulic computations," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 122-131.
    5. Deman, G. & Kerrou, J. & Benabderrahmane, H. & Perrochet, P., 2015. "Sensitivity analysis of groundwater lifetime expectancy to hydro-dispersive parameters: The case of ANDRA Meuse/Haute-Marne site," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 276-286.
    6. Zio, E. & Pedroni, N., 2009. "Functional failure analysis of a thermal–hydraulic passive system by means of Line Sampling," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1764-1781.
    7. Marrel, Amandine & Iooss, Bertrand & Laurent, Béatrice & Roustant, Olivier, 2009. "Calculations of Sobol indices for the Gaussian process metamodel," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 742-751.
    8. Labopin-Richard T. & Gamboa F. & Garivier A. & Iooss B., 2016. "Bregman superquantiles. Estimation methods and applications," Dependence Modeling, De Gruyter, vol. 4(1), pages 1-33, March.
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    10. Marrel, Amandine & Iooss, Bertrand & Van Dorpe, François & Volkova, Elena, 2008. "An efficient methodology for modeling complex computer codes with Gaussian processes," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4731-4744, June.

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