Multiobjective optimization for nuclear fleet evolution scenarios using COSI
Résumé
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:
- criteria (e.g. saving natural resources and minimizing waste production);
- variables (scenario parameters) related to reprocessing, reactor operation, installed power
repartition, etc.;
- constraints making scenarios industrially feasible.
The large number of scenario calculations needed to solve an optimization problem can be timeconsuming
and hardly achievable; therefore it requires shortening 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.