References
- M. Marseguerra, E. Zio, Identification of a line break by a neural network methodology, Ann. Nucl. Energy 21 (1994) 249-258, 1994/04/01. https://doi.org/10.1016/0306-4549(94)90007-8
- P. Baraldi, F. Di Maio, M. Rigamonti, E. Zio, R. Seraoui, Clustering for unsupervised fault diagnosis in nuclear turbine shut-down transients, Mech. Syst. Signal Process. 58-59 (2015) 160-178, 2015/06/01/. https://doi.org/10.1016/j.ymssp.2014.12.018
- Z.-Q. Wang, C.-H. Hu, X.-S. Si, E. Zio, Remaining useful life prediction of degrading systems subjected to imperfect maintenance: application to draught fans, Mech. Syst. Signal Process. 100 (2018) 802-813, 2018/02/01/. https://doi.org/10.1016/j.ymssp.2017.08.016
- T.V. Santosh, A. Srivastava, V.V.S. Sanyasi Rao, A.K. Ghosh, H.S. Kushwaha, Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks, Reliab. Eng. Syst. Saf. 94 (2009) 759-762, 3//. https://doi.org/10.1016/j.ress.2008.08.005
- S.J. Lee, P.H. Seong, 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 (2005) 268-281, 2005/01/01. https://doi.org/10.1016/j.pnucene.2005.03.009
- D.Y. Kim, K.H. Yoo, J.H. Kim, M.G. Na, S. Hur, C.H. Kim, Prediction of leak flow rate using fuzzy neural networks in severe post-LOCA circumstances, IEEE Trans. Nucl. Sci. 61 (2014) 3644-3652. https://doi.org/10.1109/TNS.2014.2357583
- S.H. Park, J.H. Kim, K.H. Yoo, M.G. Na, Smart sensing of the RPV water level in NPP severe accidents using a GMDH algorithm, IEEE Trans. Nucl. Sci. 61 (2014) 931-938. https://doi.org/10.1109/TNS.2014.2305444
- M. Saghafi, M.B. Ghofrani, Accident management support tools in nuclear power plants: a post-Fukushima review, Prog. Nucl. Energy 92 (2016) 1-14, 9//. https://doi.org/10.1016/j.pnucene.2016.06.006
- EPRI, Severe Accident Management Guidance Technical Basis Report, Volumes 2: The Physics of Accident Progression, Palo Alto, CA, 2012.
- M.G. Na, S.H. Shin, D.W. Jung, S.P. Kim, J.H. Jeong, B.C. Lee, Estimation of break location and size for loss of coolant accidents using neural networks, Nucl. Eng. Des. 232 (2004) 289-300, 8//. https://doi.org/10.1016/j.nucengdes.2004.06.007
- K. Mo, S.J. Lee, P.H. Seong, A dynamic neural network aggregation model for transient diagnosis in nuclear power plants, Prog. Nucl. Energy 49 (2007) 262-272, 4//. https://doi.org/10.1016/j.pnucene.2007.01.002
- M.G. Na, W.S. Park, D.H. Lim, Detection and diagnostics of loss of coolant accidents using support vector machines, IEEE Trans. Nucl. Sci. 55 (2008) 628-636. https://doi.org/10.1109/TNS.2007.911136
- S.H. Lee, Y.G. No, M.G. Na, K.I. Ahn, S.Y. Park, Diagnostics of loss of coolant accidents using SVC and GMDH models, IEEE Trans. Nucl. Sci. 58 (2011) 267-276. https://doi.org/10.1109/TNS.2010.2091972
- D. Roverso, Plant diagnostics by transient classification: the aladdin approach, Int. J. Intell. Syst. 17 (2002) 767-790. https://doi.org/10.1002/int.10049
- M. Saghafi, M. Ghofrani, Introduction of a research project on development of accident management support tool for BNPP (WWER-1000) based on the lessons learned from Fukushima accident, in: Presented at the International Experts Meeting on Strengthening Research and Development Effectiveness in the Light of the Accident at the Fukushima Daiichi Nuclear Power Plant Vienna, Austria, 2015.
- M. Saghafi, M.B. Ghofrani, F. D'Auria, Development and qualification of a thermal-hydraulic nodalization for modeling station blackout accident in PSBVVER test facility, Nucl. Eng. Des. 303 (2016) 109-121, 7//. https://doi.org/10.1016/j.nucengdes.2016.04.012
- M. Saghafi, M.B. Ghofrani, F. D'Auria, Application of FFTBM with signal mirroring to improve accuracy assessment of MELCOR code, Nucl. Eng. Des. 308 (2016) 238-251, 2016/11/01/. https://doi.org/10.1016/j.nucengdes.2016.08.025
- M. Saghafi, F. Yousefpour, K. Karimi, S.M. Hoseyni, Determination of PAR configuration for PWR containment design: a hydrogen mitigation strategy, Int. J. Hydrogen Energy 42 (2017) 7104-7119, 2017/03/09/. https://doi.org/10.1016/j.ijhydene.2017.01.110
- L. Tsungnan, B.G. Horne, C.L. Giles, S.Y. Kung, What to remember: how memory order affects the performance of NARX neural networks, in: Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on vol. 2, 1998, pp. 1051-1056.
- L. Tsungnan, B.G. Horne, P. Tino, C.L. Giles, Learning long-term dependencies in NARX recurrent neural networks, IEEE Trans. Neural Network. 7 (1996) 1329-1338. https://doi.org/10.1109/72.548162
- M. Boroushaki, M.B. Ghofrani, C. Lucas, Identification of a nuclear reactor core (VVER) using recurrent neural networks, Ann. Nucl. Energy 29 (2002) 1225-1240, 7//. https://doi.org/10.1016/S0306-4549(01)00105-0
- A. Petruzzi, M. Cherubini, F. D'Auria, Thirty years' experience in RELAP5 applications at GRNSPG & NINE, Nucl. Technol. 193 (2016) 47-87. https://doi.org/10.13182/NT14-144
- T. Takeda, ROSA/LSTF test and RELAP5 code analyses on PWR 1% vessel upper head small-break LOCA with accident management measure based on core exit temperature, Nucl. Eng. Technol. 50 (8) (2018) 1412-1420, 2018/08/10/. https://doi.org/10.1016/j.net.2018.08.004
- T. Takeda, I. Ohtsu, Uncertainty analysis of ROSA/LSTF test by RELAP5 code and PKL counterpart test concerning PWR hot leg break LOCAs, Nuclear Engineering and Technology 50 (2018) 829-841, 2018/08/01/. https://doi.org/10.1016/j.net.2018.05.005
- J.M. Izquierdo, J. Hortal, M. Sanchez Perea, E. Melendez, C. Queral, J. Rivas-Lewicky, Current status and applications of integrated safety assessment and simulation code system for ISA, Nuclear Engineering and Technology 49 (2017) 295-305, 2017/03/01/. https://doi.org/10.1016/j.net.2017.01.013
- S.W. Lee, B.D. Chung, Y.-S. Bang, S.W. Bae, Analysis of uncertainty quantification method by comparing Monte-Carlo method and Wilks' formula, Nuclear Engineering and Technology 46 (2014) 481-488, 2014/08/01/. https://doi.org/10.5516/NET.02.2013.047
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