EPJ Web Conf.
Volume 247, 2021PHYSOR2020 – International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future
|Number of page(s)||8|
|Section||Fuel Cycle and Scenarios|
|Published online||22 February 2021|
SYSTEMATIC ANALYSIS OF MULTIVARIATE SCENARIOS USING ADVANCED CLUSTERING METHODS
1 Institut de physique nucleaire dOrsay, IN2P3/CNRS University Paris-Sud 15 rue Georges CLEMENCEAU 91406 ORSAY, France
2 Subatech, IMTA IN2P3/CNRS University of Nantes Nantes, F-44307, France
3 IRSN/PSN-EXP/SNC/LN 92262 Fontenay-aux-Roses, France
Published online: 22 February 2021
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.
Key words: fuel cycle / MOX / Scenario / clustering
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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