Hybrid accident simulation methodology using artificial neural networks for nuclear power plants

Authors:
Young Joon Choi;Hyun Koon Kim;Won Pil Baek;Soon Heung Chang
Affiliations:
Nuclear Safety Regulation Division, Korea Institute of Nuclear Safety, 19 Kusong-dong, Yusong-gu, Taejon 305-338, South Korea;Nuclear Safety Regulation Division, Korea Institute of Nuclear Safety, 19 Kusong-dong, Yusong-gu, Taejon 305-338, South Korea;Thermal Hydraulic Safety Research Team, Korea Atomic Energy Research Institute, P.O. Box 105, Yusong-gu, Taejon 305-600, South Korea;Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, 373-1 Kusong-dong, Yusong-gu, Taejon 305-701, South Korea
Venue:
Information Sciences—Informatics and Computer Science: An International Journal
Year:
2004

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Abstract

A hybrid accident simulation methodology for nuclear power plants is proposed to enhance the capabilities of compact simulator by introducing artificial neural networks. Two neural networks are trained with the target values obtained from the analyses of detailed computer codes and trained results are combined with the compact simulator to perform the following roles: (i) compensation for inaccuracies of a compact simulator occurring from simplified governing equation and reduced number of physical control volumes, and (ii) prediction of the critical parameter usually calculated from the sophisticated computer code: the autoassociative neural network improves the computational results of the compact simulator up to the accuracy level of detailed best estimate computer code, while the backpropagation neural network predicts the minimum departure from nucleate boiling ratio (DNBR). Simulations are carried out to verify the applicability of the proposed methodology for the loss of flow accidents and the results show that the neural networks can be used as a complementary tool to improve the results of a compact simulator.