Multiobjective optimization for nuclear fleet evolution scenarios using COSI
CEA, DEN, Cadarache, DER, SPRC, LECy, 13108
2 LPSC-IN2P3-CNRS, UJF, Grenoble INP, 53 rue des Martyrs, 38026 Grenoble, France
⁎ e-mail: firstname.lastname@example.org.
Accepted: 17 December 2015
Published online: 4 March 2016
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 (e.g. 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 neural network. 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. The present article deals with obtaining the optimal scenarios and validating the methodology implemented, i.e. the coupling between the simulation software COSI, depletion surrogate models and a genetic algorithm optimization method.
© D. Freynet et al., published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.