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Signal Prediction in the LOCA Using Elman Recurrent Neural Networks

Journal Name:

  • International Journal of Science and Engineering Investigations

Publication Year:

  • 2012

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
In a reactor accident like a loss of coolant accident (LOCA), one or some signals can not be monitored by control panel for some reasons such as interruptions and so on. Therefore a fast alternative method could guarantee the safe and reliable exploration of nuclear power planets. In this study, an artificial neural network (ANN) with Elman recurrent structure is used to predict five thermal hydraulic signals in a LOCA after the upper plenum break. In the prediction procedure, a few previous samples are fed to the ANN and the output value of the next time step is estimated by the network output. The Elman recurrent network is trained with data obtained from the benchmark simulation of a LOCA in VVER. The results reveal that the predicted values follow the real trends well and ANNs can be used as a fast alternative prediction tool in LOCA.
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  • 7
1-4
  • English

REFERENCES

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