| Issue |
EPJ Web Conf.
Volume 173, 2018
Mathematical Modeling and Computational Physics 2017 (MMCP 2017)
|
|
|---|---|---|
| Article Number | 04013 | |
| Number of page(s) | 4 | |
| Section | Mathematical and Computational Support of the Experiments | |
| DOI | https://doi.org/10.1051/epjconf/201817304013 | |
| Published online | 14 February 2018 | |
https://doi.org/10.1051/epjconf/201817304013
Study of the Possibility of Reducing the Slow Fluctuations of the Reactivity and Thermal Power of the IBR-2M Reactor
1 Joint Institute for Nuclear Research, Dubna, Russia
2 Institute of Physics and Technology, Ulaanbaatar, Mongolia
* e-mail: pepel@nf.jinr.ru
** e-mail: tsolmonuc@gmail.com
Published online: 14 February 2018
This paper presents an artificial neural network method for long-term prediction of the thermal dynamic parameters of the IBR-2M reactor. Attention is focused mainly on the prediction of the temperature and sodium flow at the entry into the core as well as the thermal power. It is shown that the prediction makes it possible to reduce by a factor of 3 the influence of slow fluctuations of reactivity on the power and thereby reduce the operational requirements for the automatic power stabilization system.
© The Authors, published by EDP Sciences, 2018
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. (http://creativecommons.org/licenses/by/4.0/).
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