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Tamaki, Hitoshi; Yoshida, Kazuo; Watanabe, Norio; Muramatsu, Ken
Proceedings of International Topical Meeting on Probabilistic Safety Analysis (PSA '05) (CD-ROM), 11 Pages, 2005/00
A probabilistic safety assessment (PSA) procedure for Mixed Oxide (MOX) fuel fabrication facilities is being developed applicable to nuclear facilities at Japan Atomic Energy Research Institute (JAERI). As part of the PSA procedure, the approach to hazard analysis was established, which consists of two analysis steps: Functional Failure Modes and Effects Analysis (Functional FMEA) and Risk Matrix Analysis. In the Functional FMEA, a variety of functions of equipment composing the facility are analyzed to identify potential abnormal events exhaustively. In the second step, these potential events are screened to select abnormal events as candidate events to be analyzed for frequency and consequence by using two-dimensional matrix based on the rough estimation of likelihood and maximum unmitigated release of radioactive material. The applicability of the hazard analysis approach established was demonstrated through the trial application of the PSA procedure being developed to model plant of MOX fuel fabrication facility.
Ono, Tomio*; Subekti, M.*; Maruyama, Yuta*; Nabeshima, Kunihiko; Kudo, Kazuhiko*
Dai-13-Kai Interijento, Shisutemu, Shimpojiumu Koen Rombunshu, p.212 - 217, 2003/12
In this research, we present nuclear power plant simulation method using Multilayer Perceptron, which is one of the models of Artificial Neural Networks(ANNs). The major characteristics of ANNs are to obtain the model through learning, analogy and very high speed processing. Furthermore, 'time synchronizing signal' and 'progress synchronizing signal' are added as the inputs to adapt the abnormal events with various scales or progress rates. This ANN, learned some sample data, can be flexibly adapted to simulate the abnormal events with different scales including explicit progress rates. In the verification using PWR simulator, we confirmed that this method could model NPP abnormal events by learning data and simulate the data which have different progress rates from learning data.
Arika, Mitsuhiro*
JAERI-M 94-045, 132 Pages, 1994/03
no abstracts in English
Nakagawa, Shigeaki; Sawa, Kazuhiro; Ohashi, Kazutaka*
Journal of Nuclear Science and Technology, 30(6), p.579 - 588, 1993/06
Times Cited Count：1 Percentile：18.76(Nuclear Science & Technology)no abstracts in English
Nakagawa, Shigeaki; Sawa, Kazuhiro; Ohashi, Kazutaka*
Proc. of the 1st JSME/ASME Joint Int. Conf. on Nuclear Engineering,Vol. 2, p.299 - 304, 1991/00
no abstracts in English
