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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2019.1
- 수록면
- 702 - 708 (7page)
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This paper deals with break size estimation of loss of coolant accidents (LOCA) using a nonlinearautoregressive with exogenous inputs (NARX) neural network. Previous studies used static approaches,requiring time-integrated parameters and independent firing algorithms. NARX neural network is able todirectly deal with time-dependent signals for dynamic estimation of break sizes in real-time. The casestudied is a LOCA in the primary system of Bushehr nuclear power plant (NPP). In this study, number ofhidden layers, neurons, feedbacks, inputs, and training duration of transients are selected by performingparametric studies to determine the network architecture with minimum error. The developed NARXneural network is trained by error back propagation algorithm with different break sizes, covering 5%e100% of main coolant pipeline area. This database of LOCA scenarios is developed using RELAP5thermal-hydraulic code. The results are satisfactory and indicate feasibility of implementing NARX neuralnetwork for break size estimation in NPPs. It is able to find a general solution for break size estimationproblem in real-time, using a limited number of training data sets. This study has been performed in theframework of a research project, aiming to develop an appropriate accident management support tool forBushehr NPP.
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참고문헌 (26)
참고문헌 신청
M. Marseguerra / 1994 / Identification of a line break by a neural network methodology / Ann. Nucl. Energy 21:249~258
P. Baraldi / 2015 / Clustering for unsupervised fault diagnosis in nuclear turbine shut-down transients / Mech. Syst. Signal Process 58-59:160~178
Z. -Q. Wang / 2018 / Remaining useful life prediction of degrading systems subjected to imperfect maintenance : application to draught fans / Mech. Syst. Signal Process 100:802~813
T. V. Santosh / 2009 / Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks / Reliab. Eng. Syst. Saf 94:759~762
S. J. Lee / 2005 / Computational Intelligence in Nuclear Applications : lessons Learned and Recent DevelopmentsA dynamic neural network based accident diagnosis advisory system for nuclear power plants / Prog. Nucl. Energy 46:268~281
P. Baraldi / 2015 / Clustering for unsupervised fault diagnosis in nuclear turbine shut-down transients / Mech. Syst. Signal Process 58-59:160~178
Z. -Q. Wang / 2018 / Remaining useful life prediction of degrading systems subjected to imperfect maintenance : application to draught fans / Mech. Syst. Signal Process 100:802~813
T. V. Santosh / 2009 / Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks / Reliab. Eng. Syst. Saf 94:759~762
S. J. Lee / 2005 / Computational Intelligence in Nuclear Applications : lessons Learned and Recent DevelopmentsA dynamic neural network based accident diagnosis advisory system for nuclear power plants / Prog. Nucl. Energy 46:268~281
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