https://doi.org/10.1051/epjconf/202023913007
Impact of nuclear data evaluations on data assimilation for an LFR
Centro de investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Energy Department, Avda. Complutense 40, 28040 Madrid, Spain
* e-mail: pablo.romojaro@ciemat.es
** e-mail: francisco.alvarez@ciemat.es
Published online: 30 September 2020
The Lead-cooled Fast Reactor is one of the three technologies selected by the Sustainable Nuclear Energy Technology Platform that can meet future European energy needs. The main drawbacks for the industrial deployment of LFR are the lack of operational experience and the impact of uncertainties. In nuclear reactor design the uncertainties mainly come from material properties, fabrication tolerances, operation conditions, simulation tools and nuclear data. The uncertainty in nuclear data is one of the most important sources of uncertainty in reactor physics simulations. Furthermore, it is known that the uncertainties in reactor criti-cality safety parameters are severely dependent on the nuclear data library used to estimate them. However, the impact of using different evaluations while performing data assimilation to constraint the uncertainties in the criticality parameters has not been properly assessed yet. In this work, a data assimilation for the main isotopes contributing to the uncertainty in keff of the ALFRED lead-cooled fast reactor has been performed with the SUMMON system using JEFF-3.3, ENDF/B-VIII.0 and JENDL-4.0u2 state-of-the-art nuclear data libraries, together with critical mass experiments from the International Criticality Safety Benchmark Evaluation Project that are representative of ALFRED, in order to assess the impact of using different evaluations for data assimilation.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.