Work

Uncertainty quantification of Cl-35 nuclear data on a Molten Chloride Fast Reactor

Year: 2025

Type: article

Abstract: This study investigates the impact of \Cl nuclear data uncertainties on the neutronics of Molten Chloride Fast Reactors (MCFR), specifically focusing on two models: MCFR-C and MCFR-D. Using the Monte Carlo code SERPENT2, a comprehensive sensitivity analysis and uncertainty quantification was conducted for both initial and equilibrium fuel compositions. This study was done synchronously with nuclear data evaluators at Los Alamos National Laboratory who were creating a new evaluation for \Cl. These findings reveal that the new \Cl evaluation has minimal effect on core neutronics, but reduces the uncertainty in the effective neutron multiplication factor (\keff) to $\sim$1000 pcm (from $\sim$1400 pcm). A robust method was developed to propagate uncertainties through SERPENT’s sensitivity analysis, incorporating the Monte Carlo statistical uncertainties and using a Positive Semi-Definite correction algorithm for nuclear covariance data. This approach offers a reliable method for uncertainty propagation, though it is limited by computational time and memory usage.

Source: Annals of Nuclear Energy

DOI: 10.1016/j.anucene.2025.111529

Authors: Jean-Baptiste Valentin, Massimiliano Fratoni, Daniel Siefman, Ludovic Jantzen, Mathieu Hursin

Institutions: École Polytechnique Fédérale de Lausanne, University of California, Berkeley

Language: English

Topic: Nuclear reactor physics and engineering

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,820

Funders: European Commission, Los Alamos National Laboratory