Issue |
EPJ Web Conf.
Volume 211, 2019
WONDER-2018 – 5th International Workshop On Nuclear Data Evaluation for Reactor applications
|
|
---|---|---|
Article Number | 05002 | |
Number of page(s) | 7 | |
Section | Processing and Benchmarking | |
DOI | https://doi.org/10.1051/epjconf/201921105002 | |
Published online | 05 June 2019 |
https://doi.org/10.1051/epjconf/201921105002
Nuclear data analyses for improving the safety of advanced lead-cooled reactors
1 CIEMAT, Energy Department, Avenida Complutense 40, 28040, Spain
2 UPM, Energy Engineering Department, Calle José Gutiérrez Abascal 2, 28006, Spain
* Corresponding author: pablo.romojaro@ciemat.es
Published online: 5 June 2019
A target accuracy assessment of the effective neutron multiplication factor, keff, for MYRRHA (Multi-purpose hYbrid Research Reactor for High-tech Applications) lead-bismuth cooled fast reactor has been performed with JEFF-3.3 and ENDF/B-VIII.0 state-of-the-art nuclear data libraries and the SUMMON system. Uncertainties in keff due to uncertainties in nuclear data have been assessed against the target accuracies provided by SG-26 of the WPEC of OECD/NEA in 2008 for LFR. Results show that keff target accuracy is still exceeded by more than a factor of two using the latest nuclear data evaluations released in 2018. Consequently, nuclear data assimilation has been carried out using criticality experiments from the International Criticality Safety Benchmark Evaluation Project that are representative of MYRRHA. The results from this work show that the level of accuracy needed in nuclear data cannot be obtained using only differential experiments, but 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.
© The Authors, published by EDP Sciences, 2019
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.
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