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

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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Main Authors: Ososkov G., Pepelyshev Yu., Tsogtsaikhan Ts.
Format: Article
Language:English
Published: EDP Sciences 2016-01-01
Series:EPJ Web of Conferences
Online Access:http://dx.doi.org/10.1051/epjconf/201610802036
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spelling doaj-a82348eac04a4308a2aaa3179384101c2021-08-02T13:57:45ZengEDP SciencesEPJ Web of Conferences2100-014X2016-01-011080203610.1051/epjconf/201610802036epjconf_mmcp2016_02036Prediction of Liquid Sodium Flow Rate through the Core of the IBR-2M Reactor Using Nonlinear Autoregressive Neural NetworksOsoskov G.0Pepelyshev Yu.1Tsogtsaikhan Ts.Joint Institute for Nuclear ResearchJoint Institute for Nuclear ResearchThis 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%.http://dx.doi.org/10.1051/epjconf/201610802036
collection DOAJ
language English
format Article
sources DOAJ
author Ososkov G.
Pepelyshev Yu.
Tsogtsaikhan Ts.
spellingShingle Ososkov G.
Pepelyshev Yu.
Tsogtsaikhan Ts.
Prediction of Liquid Sodium Flow Rate through the Core of the IBR-2M Reactor Using Nonlinear Autoregressive Neural Networks
EPJ Web of Conferences
author_facet Ososkov G.
Pepelyshev Yu.
Tsogtsaikhan Ts.
author_sort Ososkov G.
title Prediction of Liquid Sodium Flow Rate through the Core of the IBR-2M Reactor Using Nonlinear Autoregressive Neural Networks
title_short Prediction of Liquid Sodium Flow Rate through the Core of the IBR-2M Reactor Using Nonlinear Autoregressive Neural Networks
title_full Prediction of Liquid Sodium Flow Rate through the Core of the IBR-2M Reactor Using Nonlinear Autoregressive Neural Networks
title_fullStr Prediction of Liquid Sodium Flow Rate through the Core of the IBR-2M Reactor Using Nonlinear Autoregressive Neural Networks
title_full_unstemmed Prediction of Liquid Sodium Flow Rate through the Core of the IBR-2M Reactor Using Nonlinear Autoregressive Neural Networks
title_sort prediction of liquid sodium flow rate through the core of the ibr-2m reactor using nonlinear autoregressive neural networks
publisher EDP Sciences
series EPJ Web of Conferences
issn 2100-014X
publishDate 2016-01-01
description 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%.
url http://dx.doi.org/10.1051/epjconf/201610802036
work_keys_str_mv AT ososkovg predictionofliquidsodiumflowratethroughthecoreoftheibr2mreactorusingnonlinearautoregressiveneuralnetworks
AT pepelyshevyu predictionofliquidsodiumflowratethroughthecoreoftheibr2mreactorusingnonlinearautoregressiveneuralnetworks
AT tsogtsaikhants predictionofliquidsodiumflowratethroughthecoreoftheibr2mreactorusingnonlinearautoregressiveneuralnetworks
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