| Issue |
EPJ Web of Conferences
Volume 108, 2016
Mathematical Modeling and Computational Physics (MMCP 2015)
|
|
|---|---|---|
| Article Number | 02036 | |
| Number of page(s) | 6 | |
| Section | Conference Contributions | |
| DOI | https://doi.org/10.1051/epjconf/201610802036 | |
| Published online | 09 February 2016 | |
https://doi.org/10.1051/epjconf/201610802036
Prediction of Liquid Sodium Flow Rate through the Core of the IBR-2M Reactor Using Nonlinear Autoregressive Neural Networks
1 Joint Institute for Nuclear Research, 141980, Dubna, Moscow region, Russia
2 Institute of physics and technology, MAS, Mongolia
a e-mail: ososkov@jinr.ru
b e-mail: pepel@nf.jinr.ru
c e-mail: tsolmon@nf.jinr.ru
Published online: 9 February 2016
This paper presents an artificial neural network method for long-term prediction of liquid sodium flow rate through the core of the IBR-2M reactor. The nonlinear autoregressive neural network (NAR) with local feedback connection has been considered as the most appropriate tool for such a prediction. The predicted results were compared with experimental values. NAR model predicts slow changes of liquid sodium flow rate up to two days with an error less than 5%.
© Owned by the authors, published by EDP Sciences, 2016
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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