Propagation of Nuclear Data Uncertainties for ELECTRA Burn-up Calculations

Abstract

The European Lead-Cooled Training Reactor (ELECTRA) has been proposed as a training reactor for fast systems within the Swedish nuclear program. It is a low-power fast reactor cooled by pure liquid lead. In this work, we propagate the uncertainties in 239 P u transport data to uncertainties in the fuel inventory of ELECTRA during the reactor life using the Total Monte Carlo approach (TMC). Within the TENDL project the nuclear models input parameters were randomized within their uncertainties and 740 239 P u nuclear data libraries were generated. These libraries are used as inputs to reactor codes, in our case SERPENT, to perform uncertainty analysis of nuclear reactor inventory during burn-up. The uncertainty in the inventory determines uncertainties in: the longterm radio-toxicity, the decay heat, the evolution of reactivity parameters, gas pressure and volatile fission product content. In this work, a methodology called fast TMC is utilized, which reduces the overall calculation time. The uncertainty in the long-term radiotoxicity, decay heat, gas pressure and volatile fission products were found to be insignificant. However, the uncertainty of some minor actinides were observed to be rather large and therefore their impact on multiple recycling should be investigated further. It was also found that, criticality benchmarks can be used to reduce inventory uncertainties due to nuclear data. Further studies are needed to include fission yield uncertainties, more isotopes, and a larger set of benchmarks.

Uncertainty and correlation analysis of lead nuclear data on reactor parameters for the European Lead Cooled Training Reactor

“…The TMC approach was utilized earlier in assessing the impact of 239 Pu cross section uncertainties on the full core 3-D SERPENT [22] model of the ELEC-TRA reactor at steady state [28] and in burnup calculations [29]. In this work however, we apply the TMC method for the propagation of nuclear uncertainties of the lead coolant ( 204,206,207,208 Pb) on the following four macroscopic parameters sensitive to nuclear data: the effective multiplication factor, the coolant temperature coefficient (CTC), the coolant void worth (CVW) and the effective delayed neutron fraction at zero burnup.…”

Propagation of neutron-reaction uncertainties through multi-physics models of novel LWR's

“…TENDL-2014 contains different random files for important nuclides, where all the important reactions for U235, U238 and Pu239, as well as the thermal scattering part of H1-H20 have been perturbed. This feature offers a good and pragmatic opportunity to extend the capabilities of CORES SIM -TH in order to perform uncertainty analysis (UA) via the Total Monte Carlo (TMC) method [3,4]. This is possible since after the proper processing of such random files into the common ACE format by means of the NJOY code, the computation of the energy-collapsed and homogenized macroscopic cross-sections can be performed with the Monte Carlo SERPENT code.…”

Bayesian updating for data adjustments and multi-level uncertainty propagation within Total Monte Carlo

“…The term uncertainty as used in this work is represented by an estimated one standard deviation of the distribution under consideration. The TMC method has been presented extensively in several references: [3,4,19,20,21] as well as applied to many applications: [10,22,23,24,25,26,27,28,29]. The first step in our updating scheme involves the identification of model input parameters (and the corresponding parameter widths) that play significant roles in defining or characterizing the reaction observables of interest.…”