Prediction of Liquid Sodium Flow Rate through the Core of the IBR-2M Reactor Using Nonlinear Autoregressive Neural Networks

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....

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Hauptverfasser: Ososkov, G., Pepelyshev, Yu, Tsogtsaikhan, Ts
Format: Tagungsbericht
Sprache:eng
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Zusammenfassung: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%.
ISSN:2100-014X
2101-6275
2100-014X
DOI:10.1051/epjconf/201610802036