Uncertainty Analysis in Reactor Physics Modelling in the Framework of UAM-Benchmark using SERPENT and SCALE-6.2 for Transport Calculations and TSUNAMI and SAMPLER for Cross Section Perturbation

In recent years, there has been an increasing demand from nuclear safety research for best estimate predictions to be provided with their confidence bounds. Consequently, a deep knowledge on Sensitivity and Uncertainty Analysis of Models is needed. The present study, involved in the framework of international research of NEA-OECD, estimates confidence bounds of results of simulation and performs sensitivity and uncertainty analysis in real cases of Light Water Reactors (LWRs). The proposed technical approach is to establish a benchmark for uncertainties analysis in LWRs best-estimate modelling, coupling multi-physics and multi-scale analysis, using as bases, a complete sets of input specifications of boiling water reactors (BWR) and pressurized water reactors (PWR). The objective is to determine and quantify the uncertainty in all steps of calculation and propagate these uncertainties in LWR whole computation. Transport Calculations are carried out using the SCALE-6.2beta3 program (TRITON/NEWT and TRITON/KENO modules) as well as Monte Carlo SERPENT code. The uncertainties calculation for a PWR 15×15 and a BWR 7×7 fuel elements, in two different configurations (with and without control rod), and two different states, Hot Full Power (HFP) and Hot Zero Power (HZP), has been performed using the TSUNAMI module, which uses the Generalized Perturbation Theory (GPT), and SAMPLER, which uses stochastic sampling techniques for cross sections perturbations. Nuclear data are perturbed and finally the sensitivity coefficients are calculated to discriminate the most sensitive and influential parameters in results of the keff and macroscopic and microscopic cross sections. The results obtained and validated are compared with references results and similar studies presented in the exercise I-1 (Cell Physics) and I-2 (Lattice Physics) of UAM-Benchmark.