This paper is about a first optimization study of a transition scenario from the current French nuclear fleet to a Sodium Fast Reactors fleet as defined in the frame of the 2006 French Act for waste management. | Multiobjective optimization for nuclear fleet evolution scenarios using COSI EPJ Nuclear Sci. Technol. 2 9 2016 Nuclear Sciences D. Freynet et al. published by EDP Sciences 2016 amp Technologies DOI epjn e2015-50066-7 Available online at http REGULAR ARTICLE Multiobjective optimization for nuclear fleet evolution scenarios using COSI David Freynet1 Christine Coquelet-Pascal1 Romain Eschbach1 Guillaume Krivtchik1 and Elsa Merle-Lucotte2 1 CEA DEN Cadarache DER SPRC LECy 13108 Saint-Paul-lez-Durance France 2 LPSC-IN2P3-CNRS UJF Grenoble INP 53 rue des Martyrs 38026 Grenoble France Received 5 October 2015 Accepted 17 December 2015 Published online 4 March 2016 Abstract. The consequences of various fleet evolution options on material inventories and flux in fuel cycle and waste can be analysed by means of transition scenario studies. The COSI code is currently simulating chronologically scenarios whose parameters are fully defined by the user and is coupled with the CESAR depletion code. As the interactions among reactors and fuel cycle facilities can be complex and the ways in which they may be configured are many the development of optimization methodology could improve scenario studies. The optimization problem definition needs to list i criteria . saving natural resources and minimizing waste production ii variables scenario parameters related to reprocessing reactor operation installed power distribution etc. iii constraints making scenarios industrially feasible. The large number of scenario calculations needed to solve an optimization problem can be time-consuming and hardly achievable therefore it requires the shortening of the COSI computation time. Given that CESAR depletion calculations represent about 95 of this computation time CESAR surrogate models have been developed and coupled with COSI. Different regression models are compared to estimate CESAR outputs first- and second-order polynomial regressions Gaussian process and artificial .
