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

This study investigates the impact of ³⁵Cl 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 ³⁵Cl. These findings reveal that the new ³⁵Cl evaluation has minimal effect on core neutronics for these designs (however could have a significant impact for a different flux spectrums), but significantly reduces the uncertainty in the effective neutron multiplication factor ($k_{\text{eff}}$) to $\sim$1000 pcm (from $\sim$1400 pcm). A robust method and workflow 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. Though limited by significant computational time and memory usage, this approach offers a reliable method for uncertainty propagation offering valuable insights into the static and uncertainty parameters of MCFR-C and MCFR-D reactors, thereby contributing to the advancement of MCFR technology. It also provides a valuable example of how downstream applied neutronics can work together with nuclear data evaluators to improve reactor analyses.