The future of nuclear power depends on the interests and decisions. To take the relevant deep uncertainty into account, a new methodology of robustness analysis of fuel cycle strategies has been developed. The method has been applied to the French cycle, considering the future deployment of fast reactor as the preselected objective and an uncertain change, called disruption, towards a new objective: the minimization of transuranic inventories without fast reactor. The status of plutonium is contradictory in two cases. Two approaches of identifying robust strategies were tested, which correspond respectively to the static and adaptive robustness assessment. One identifies static strategies in a pre-disruption scenario, which achieve acceptable outcomes for both objectives. The other takes a trajectory pursuing the pre-selected objective and, in case of disruption, adapts it for the new objective. The comparison of two approaches indicates the temporality of adaptation relative to immediate actions under the uncertain disruption.

The continuous improvement of fuel cycle simulators in conjunction with the increase of computing capacities have led to a new scale of scenario studies. Taking into consideration multiple variable parameters and observing their effect on multiple evaluation criteria, these scenario studies regroup several thousands of trajectories paving the different possible values for multiple operational parameters. If global methods like sensitivity analysis allow extracting useful information from these groups of trajectories, they only provide average and global values. In this work we present a new method to analyze these groups of trajectories while keeping some localization in the information. Based on principal component analysis, clustering method have been implemented in order to mathematically extract, from the ensemble of trajectories simulated for a scenario study, subgroups of trajectories that have similar behaviors. Typical trajectories, representative of these subgroups, are then determined. The application of this new method on a sample scenario for two different output, the total amount of transuranic elements within the fuel cycle and the number of time the MOX fuel could not be built during the simulated time, is presented. The comparison of the results between the two analyses shows that the method allows good clustering for continuous and regular outputs but struggle with discrete highly non-linear ones.