On the importance of target accuracy assessments and data assimilation for the co-development of nuclear data and fast reactors: MYRRHA and ESFR

Research output: Contribution to journalArticlepeer-review


  • Pablo Romojaro
  • Francisco Álvarez-Velarde
  • Oscar Cabellos
  • Nuria García-Herranz
  • Antonio Jiménez-Carrascosa

Institutes & Expert groups

  • UPM - Universidad Politécnica de Madrid
  • CIEMAT - Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas

Documents & links


Sodium-cooled Fast Reactors and Lead-cooled Fast Reactors have been selected by the Sustainable Nuclear Energy Technology Platform as technologies that can meet future European energy needs. To achieve the requested level of safety for these reactor technologies and to minimize the increase in the costs due to additional safety measures, accurate and reliable nuclear data are necessary. The objective of this work has been to analyse and improve the nuclear data required for the development, safety assessment and licensing of SFRs and LFRs, reducing the uncertainties in the reactor integral responses due to the uncertainties in nuclear data, in order to reach the target accuracies defined by researchers, industry and regulators. To that end, target accuracy and data assimilation analyses have been performed to identify nuclear data weaknesses and to reduce the uncertainties of integral safety-related parameters due to neutron induced nuclear data. As a result of this work, nuclear data needs for advanced SFRs and LFRs have been identified, improvements of existing nuclear data libraries have been suggested, nuclear data have been adjusted and uncertainties in reactor integral parameters have been reduced, meeting target accuracies after adjustment.


Original languageEnglish
Article number108416
Pages (from-to)1-13
Number of pages13
JournalAnnals of nuclear energy
Publication statusPublished - 1 Oct 2021


  • LRF, SFR, MYRRHA, ESFR, Nuclear data, JEFF-3.3, ENDF/B-VIII.0, Sensitivity and uncertainty, Target accuracy assessment, Data assimilation

ID: 7157785