| Issue |
EPJ Nuclear Sci. Technol.
Volume 9, 2023
|
|
|---|---|---|
| Article Number | 14 | |
| Number of page(s) | 24 | |
| DOI | https://doi.org/10.1051/epjn/2022055 | |
| Published online | 27 February 2023 | |
https://doi.org/10.1051/epjn/2022055
Regular Article
On the estimation of nuclide inventory and decay heat: a review from the EURAD European project
1
Reactor Physics and Thermal hydraulic Laboratory, Paul Scherrer Institut, Villigen, Switzerland
2
Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas, CIEMAT, Madrid, Spain
3
Institut für Neutronenphysik und Reaktortechnik (INR), Karlsruher Institut für Technologie (KIT), Karlsruhe, Germany
4
Belgian Nuclear Research Centre SCK-CEN, Mol, Belgium
5
VTT Technical Research Centre of Finland, Espoo, Finland
6
Reactor Physics Division, Jožef Stefan Institute (JSI), Ljubljana, Slovenija
7
Empresa Nacional de Residuos Radiactivos, ENRESA, Madrid, Spain
8
PreussenElektra GmbH, Hannover, Germany
9
European Commission, Joint Research Centre – JRC, Geel, Belgium
10
National Cooperative for the Disposal of Radioactive Waste (Nagra), Wettingen, Switzerland
* e-mail: dimitri-alexandre.rochman@psi.ch
Received:
7
September
2022
Received in final form:
15
December
2022
Accepted:
16
December
2022
Published online: 27 February 2023
In this work, a study dedicated to the characterization of the neutronics aspect of the Spent Nuclear Fuel (SNF), as part of the European project EURAD (Work Package 8), is presented. Both measured nuclide concentrations from Post Irradiation Examination samples and decay heat from calorimetric measurements are compared to simulations performed by different partners of the project. Based on these detailed studies and data from the published literature, recommendations are proposed with respect to best practices for SNF modelling, as well as biases and uncertainties for a number of important nuclides and the SNF decay heat for a cooling period from 1 to 1000 years. Finally, specific needs are presented for the improvement of current code prediction capabilities.
© D.A. Rochman et al., Published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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