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
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Published online: 9 February 2016
Abstract
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
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