EPJ Nuclear Sci. Technol. 11, 27 (2025)
https://doi.org/10.1051/epjn/2025018

Regular Article

Representativity studies of GEN-III large cores to ZPR experiments with respect to nuclear data

A first step towards transposition

¹ EDF Lab Paris-Saclay 7 Boulevard Gaspard Monge 91120 Palaiseau France
² Université Grenoble Alpes 621 Avenue Centrale 38400 Saint-Martin-d’Hères France
³ DTIPDM-F, Framatome 2 rue Pr Jean Bernard 69007 Lyon France
⁴ EDF Lab Chatou 6 Quai Watier 78400 Chatou France

^* e-mail: eric-karson.njayou-tsepeng@edf.fr

Received: 2 October 2024
Received in final form: 19 February 2025
Accepted: 4 April 2025
Published online: 13 June 2025

Abstract

Uncertainty quantification plays a crucial role in demonstrating the safety of nuclear reactors by assessing and accounting for the various sources of uncertainty in reactor performance predictions. This process helps establish safety margins, which are essential for ensuring that the reactor operates safely under a wide range of conditions. For existing reactors, it is mainly based on comparisons between calculations and measurements. However, the lack of experimental data in some cases (new reactor concepts, accidental conditions,…) has made the so-called “transposition”, at the very least, a complement to the latter. The most commonly used methods for this purpose rely on bayesian inference and requires a high degree of similarity between the integral parameters of the different configurations, also called representativity. This paper presents the methodology and some results of evaluated representativity factors between ZPR experiments and a Gen-III+ target core issued from the UAM benchmark at different scales and their evolution throughout the fuel cycle life, using the industrial state-of-the-art code COCAGNE. The goal is to study the relevance of such approach in an industrial context. The paper focuses on the effective multiplication factor and the center over periphery fission rate ratio. Standard (SPT) and generalized (GPT) perturbation theories are employed to determine sensitivities with respect to nuclear data and their uncertainties are propagate to the outputs through the sandwich rule with covariance data collapsed from a fine to a coarse energy mesh.