Nuclear data analyses for improving the safety of advanced lead-cooled reactors

Research output: ThesisDoctoral thesis

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The Lead-cooled Fast Reactor (LFR) is one of the three technologies selected by the Sustainable Nuclear Energy Technology Platform that can meet future European energy needs. Significant efforts are being made by researchers and industry to overcome the main drawbacks for the industrial deployment of LFR, which are the lack of operational experience and the impact of uncertainties in the reactor design, operation and safety assessment. In nuclear reactor design the uncertainties mainly come from material properties, fabrication tolerances, operative conditions, simulation tools and nuclear data. Indeed, the uncertainty in nuclear data is one of the most important sources of uncertainty in reactor design and reactor physics simulations, and significant gaps between the uncertainties and the target accuracies have been systematically shown in the past. Meeting the target accuracy is required not only to achieve the requested level of safety for this technology, but also to minimize the increase in the costs due to additional security measures. With that background, the main objective of this work has been to analyse and improve the nuclear data required for the development, safety assessment and licensing of LFR reactors, reducing the uncertainties in the criticality safety parameters due to the uncertainties in nuclear data, in order to reach the target accuracies defined by researchers, industry and regulators. To estimate the uncertainties in reactor key parameters (effective neutron multiplication factor, keff, effective delayed neutron fraction, βeff, effective neutron generation time, eff, safety coefficients, …) and to identify possible nuclear data weaknesses, accurate and reliable tools for sensitivity analysis and uncertainty quantification are needed. Tools able to calculate the uncertainty of a response due to uncertainties in nuclear data are available. However they possess several limitations such as no parallel processing capabilities; user selection of isotope and reaction channels to be included in the analysis; use of multi-group nuclear data; use of specific nuclear data library and/or specific covariance matrix; and limited complexity of the system under analysis due to the required number of simulations. Hence, in the framework of this work, a Sensitivity and Uncertainty Methodology for MONte carlo codes (SUMMON) has been developed. SUMMON is a tool conceived to perform complete automated sensitivity and uncertainty analyses of the most relevant criticality safety parameters of detailed complex reactor designs from the neutronic point of view, i.e., keff, eff, eff and reactivity coefficients, using state-of-the-art nuclear data libraries and covariances. SUMMON has been validated using integral experiments from the International Handbook of Evaluated Criticality Safety Benchmark Experiments (ICSBEP) and extensively verified against consolidated codes such as SCALE, SUSD3D and SERPENT. Good agreement between codes has been found. Once SUMMON was developed, preliminary analyses were carried out for MYRRHA (Multi-purpose hYbrid Research Reactor for High-tech Applications) lead-bismuth cooled fast reactor design. First, the ENDF/B-VII.0 nuclear data library was used, in order to identify the most important nuclear data for neutron induced reactions for criticality safety calculations of LFRs. Then, the recently released JEFF-3.3T1 library, the beta proposal at the time for the next version of the European evaluated nuclear data library, was analysed using the best documented energy dependent experimental data sets available. Bismuth and lead, identified in the previous analyses as key isotopes, were chosen as the main objects of study for improvement of nuclear data since they are of vital importance and were not covered in the CIELO pilot project. Problems were found in the resolved resonance region of JEFF-3.3T1 bismuth and lead evaluations and recommendations were given to the JEFF project, which were adopted in the release version of the library. Next, sensitivity and uncertainty analyses using the state-of-the-art JEFF-3.3 and ENDF/B-VIII.0 nuclear data libraries were performed with SUMMON to estimate the uncertainties in the criticality safety parameters of MYRRHA. While good agreement was observed in the total uncertainties yielded by both libraries, differences in evaluations, missing correlations and missing covariance evaluations, caused the contributors to the total uncertainty to differ. Furthermore, the design target accuracies for some criticality safety parameters, such as the effective neutron multiplication factor, still exceeded by more than a factor of two for the considered modern nuclear data evaluations. In order to provide adjusted nuclear data, not only capable of predicting reactor properties within the target design accuracy, but also statistically consistent with the various differential measurements, the Data Assimilation With summoN (DAWN) module was developed. DAWN is based on the combination of experimental covariance data and integral experiments together with advanced statistical adjustment techniques (Generalised Least Squares). DAWN has been verified against the Total Monte Carlo (TMC) method for several integral experiments. Finally, DAWN was used to perform an assimilation on the main contributors to the uncertainty using JEFF-3.3 nuclear data as a prior and publicly available critical mass experiments from the ICSBEP. The consistency of the nuclear data adjustment was checked against differential experimental data and good agreement was found. A significant reduction in uncertainty was obtained using the experiments most representative of MYRRHA, due to the reduction in the uncertainty of the major contributors and to the presence a posteriori of strong cross-correlations between isotopes and reactions that did not exist a priori. Results show that a reduction of nearly 300 pcm can be achieved performing an assimilation with the most sensitive experiment to the major contributor to the uncertainty. It proves that the combination of experimental covariance data and integral experiments together with Generalised Least Squares technique, can provide adjusted nuclear data capable of predicting reactor properties with lower uncertainty and consistent with differential data.


Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • UPM - Universidad Politécnica de Madrid - Spain
  • García-Herranz, Nuria , Supervisor, External person
  • Álvarez-Velarde, Francisco, Supervisor, External person
  • Krása, Antonin, SCK CEN Mentor
Thesis sponsors
  • UPM - Universidad Politécnica de Madrid - Spain
  • CIEMAT - Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas
Award date28 Oct 2019
  • Universidad Politécnica de Madrid
Publication statusPublished - 6 Nov 2019


  • Nuclear energy, LFR, Sensitivity analysis, uncertainty analysis, Nuclear data, Data assimilation, MYRRHA

ASJC Scopus subject areas

ID: 6761085