Analysis of uncertainty propagation in scenario studies Surrogate models application to the French historical PWR fleet

G. Krivtchik (1) , C. Coquelet-Pascal (1) , P. Blaise (1) , C. Garzenne (2) , J. Le Mer (2)

1 CEA-DES (ex-DEN) - CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire)
2 EDF-R and D, Research Group R16 (Energy Strategy and Economics),

Nuclear scenario studies simulate the whole fuel cycle over a period of time, from extraction of natural resources to geological storage. They enable the comparison of different strategies related to the reactor fleet evolution, fuel cycle materials management, etc. based on criteria such as the installed capacity per reactor technology, mass inventories and flows, in the fuel cycle as well as in the waste. Several sources of uncertainty have an impact on the scenario results, such as nuclear data and industrial parameters. Nuclear data uncertainties propagate in the scenario along the isotopic chains through depletion, cooling and fuel equivalence models, while industrial parameters impact directly the fuel cycle facilities, such as the plutonium and minor actinides recovery rates at the reprocessing plant or the spent fuels burnup. A method dedicated to uncertainty propagation in scenario studies based on a Monte-Carlo approach and surrogate models was developed. In the present study, the uncertainty propagation methodology is applied to the French historical PWR fleet, up to 2010.

Domains: Nuclear Experiment [nucl-ex]; Nuclear Theory [nucl-th]

G. Krivtchik, C. Coquelet-Pascal, P. Blaise, C. Garzenne, J. Le Mer. Analysis of uncertainty propagation in scenario studies Surrogate models application to the French historical PWR fleet. GLOBAL 2015, Sep 2015, Paris, France. ⟨cea-02509678⟩