Taillefer—A Tool for Sensitivity Analysis and Uncertainty Propagation Studies for Steady-State Thermal-Hydraulic Simulations of Involute Fuel Element Research Reactors

Abstract

Taillefer is a versatile Python tool for carrying out Sensitivity Analysis (SA) and uncertainty propagation (UP) studies based on Monte Carlo sampling. Developed with the primary goal of investigating sensitivities and uncertainties of steady-state thermal-hydraulic (SSTH) safety parameters of the high-performance research reactors Forschungs Neutronenquelle Heinz Maier-Leibnitz (FRM II) in Garching, Germany, and the Réacteur à Haut Flux (RHF) in Grenoble, France, it can also be used for a large variety of other modeling problems. The work presented here aims to explain the underlying mathematical background of SA and UP studies with Taillefer and to show some steps to verify these routines. Furthermore, a real-life application example is provided that demonstrates Taillefer’s use in SSTH analysis of the RHF. For this purpose, Taillefer is coupled to the external thermal-hydraulic software PLTEMP/ANL, which is one of the codes used at FRM II and RHF to access SSTH performance and safety parameters. Determining these crucial quantities is part of identifying possible low-enriched uranium (LEU) core designs that are suitable to replace the currently used highly enriched uranium fuels of the two reactors, supporting global nonproliferation efforts. Taillefer is a powerful tool in these conversion studies, as it increases the reliability of the LEU safety parameters by providing information about sensitivities and uncertainties in addition to the nominal values predicted by the thermal-hydraulic software.