SYSTEMATIC ANALYSIS OF MULTIVARIATE SCENARIOS USING ADVANCED CLUSTERING METHODS

Nicolas Thiollière ,Marc Ernoult ,Liang Jiali,David Stoppa ,Léa Tillard ,X Doligez

doi:10.1051/epjconf/202124713002

Abstract

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.

Highlights

If fuel cycle simulators have been used for several decades, the last years have seen a renewed interest and many new developments, new codes as well as major updates for older codes
The FIT Benchmark [6] were 6 different fuel cycle simulators simulate 2000 trajectories of a simple scenario illustrates that such capacity to run large number of simulation has spread
Because the objective of this work is more focused on method testing than specific fuel cycle analysis, the scenario has been chosen to only use only reactor for which the physics and the fuel cycle behavior is well understood : PWRs, with UOX and MOX fuels

Summary

Introduction

If fuel cycle simulators have been used for several decades, the last years have seen a renewed interest and many new developments, new codes as well as major updates for older codes In this context, fuel cycle simulators capabilities have improved and several simulators adapted themselves to parallels calculations [1,2,3]. To understand variables of impact on one observable and analyze sensitivities, a method taking advantage of this newly possible big number of calculation have been developed within the CLASS team : the Wide Parametric Sweeping method [6]. This method is a direct extension of the Global sensitivity analysis for fuel cycle studies [3]. Because numerous trajectories are simulated, the analyst can apply a filter on the inputs and see the impact on the solution space without the need of any new calculations from the fuel cycle simulator

Objectives

Methods

Conclusion