| Issue |
EPJ Nuclear Sci. Technol.
Volume 6, 2020
Euratom Research and Training in 2019: challenges, achievements and future perspectives
|
|
|---|---|---|
| Article Number | 42 | |
| Number of page(s) | 14 | |
| Section | Part 1: Safety research and training of reactor systems | |
| DOI | https://doi.org/10.1051/epjn/2019006 | |
| Published online | 05 May 2020 | |
https://doi.org/10.1051/epjn/2019006
Review Article
Advanced numerical simulation and modelling for reactor safety − contributions from the CORTEX, HPMC, McSAFE and NURESAFE projects
1
Department of Physic, Division of Subatomic and Plasma Physics, Chalmers University of Technology, 412 96 Gothenburg, Sweden
2
Institute for Neutron Physics and Reactor Technology (INR), Karlsruhe Institute of Technology (KIT), Hermann-vom-Helmholtz-Platz-1, 76344 Eggenstein-Leopoldshafen, Germany
3
Commissariat à l'Energie Atomique et aux Energies Alternatives, Centre de Saclay, 91191 Gif-sur-Yvette Cedex, France
* e-mail: demaz@chalmers.se
Received:
12
March
2019
Accepted:
4
June
2019
Published online: 5 May 2020
Predictive modelling capabilities have long represented one of the pillars of reactor safety. In this paper, an account of some projects funded by the European Commission within the seventh Framework Program (HPMC and NURESAFE projects) and Horizon 2020 Program (CORTEX and McSAFE) is given. Such projects aim at, among others, developing improved solution strategies for the modelling of neutronics, thermal-hydraulics, and/or thermo-mechanics during normal operation, reactor transients and/or situations involving stationary perturbations. Although the different projects have different focus areas, they all capitalize on the most recent advancements in deterministic and probabilistic neutron transport, as well as in DNS, LES, CFD and macroscopic thermal-hydraulics modelling. The goal of the simulation strategies is to model complex multi-physics and multi-scale phenomena specific to nuclear reactors. The use of machine learning combined with such advanced simulation tools is also demonstrated to be capable of providing useful information for the detection of anomalies during operation.
© C. Demazière et al., published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://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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