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Journal Articles

Toward mechanistic evaluation of critical heat flux in nuclear reactors, 2; Recent studies and future challenges toward mechanistic and reliable CHF evaluation

Okawa, Tomio*; Mori, Shoji*; Liu, W.*; Ose, Yasuo*; Yoshida, Hiroyuki; Ono, Ayako

Nihon Genshiryoku Gakkai-Shi ATOMO$$Sigma$$, 63(12), p.820 - 824, 2021/12

The evaluation method of the critical heat flux based on the mechanism is needed for the efficient design and development of fuel in reactors and the appropriate safety evaluation. In this paper, the current researches relating to the mechanism of the critical heat flux are reviewed, and the issue to be considered in the future are discussed.

Journal Articles

Degassing behavior of noble gases from groundwater during groundwater sampling

Nakata, Kotaro*; Hasegawa, Takuma*; Solomon, D. K.*; Miyakawa, Kazuya; Tomioka, Yuichi*; Ota, Tomoko*; Matsumoto, Takuya*; Hama, Katsuhiro; Iwatsuki, Teruki; Ono, Masahiko*; et al.

Applied Geochemistry, 104, p.60 - 70, 2019/05

 Times Cited Count:2 Percentile:26.9(Geochemistry & Geophysics)

no abstracts in English

Journal Articles

Integrated on-line plant monitoring system for HTTR with neural networks

Nabeshima, Kunihiko; Subekti, M.*; Matsuishi, Tomomi*; Ono, Tomio*; Kudo, Kazuhiko*; Nakagawa, Shigeaki

Journal of Power and Energy Systems (Internet), 2(1), p.92 - 103, 2008/00

The neural networks have been utilized in on-line monitoring-system of High Temperature Engineering Tested Reactor (HTTR) with thermal power of 30 MW. In this system, several neural networks can independently model the plant dynamics with different architecture, input and output signals and learning algorithm. One of main task is real-time plant monitoring by Multi-Layer Perceptron (MLP) in auto-associative mode, which can model and estimate the whole plant dynamics by training normal operational data only. Other tasks are on-line reactivity prediction, reactivity and helium leak monitoring, respectively. From the on-line monitoring results at the safety demonstration tests, each neural network shows good prediction and reliable detection performances.

Journal Articles

Integrated on-line plant monitoring system for HTTR using neural networks

Nabeshima, Kunihiko; Matsuishi, Tomomi*; Makino, Jun*; Subekti, M.*; Ono, Tomio*; Kudo, Kazuhiko*; Nakagawa, Shigeaki

Proceedings of 15th International Conference on Nuclear Engineering (ICONE-15) (CD-ROM), 6 Pages, 2007/04

The neural networks have been utilized in on-line monitoring system of High Temperature Engineering Tested Reactor (HTTR) with thermal power of 30MW. In this system, several neural networks can independently model the plant dynamics with different architecture, input and output signals and learning algorithm. One of main task is real-time plant monitoring by Multi-Layer Perceptron (MLP) in auto-associative mode, which can model and estimate the whole plant dynamics by training normal operational data only. Other tasks are on-line reactivity prediction, reactivity and helium leak monitoring, respectively. From the on-line test results, each neural network shows good prediction and reliable detection performances.

Journal Articles

Development of on-line monitoring system for Nuclear Power Plant (NPP) using neuro-expert, noise analysis, and modified neural networks

Subekti, M.*; Ono, Tomio*; Kudo, Kazuhiko*; Nabeshima, Kunihiko; Takamatsu, Kuniyoshi

Proceedings of 5th American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Controls, and Human Machine Interface Technology (NPIC & HMIT 2006) (CD-ROM), p.75 - 82, 2006/11

The neuro-expert has been utilized in previous monitoring-system research of Pressure Water Reactor (PWR). The research improved the monitoring system by utilizing neuro-expert, conventional noise analysis and modified neural networks for capability extension. The parallel method applications required distributed architecture of computernetwork for performing real-time tasks. The research aimed to improve the previous monitoring system, which could detect sensor degradation, and to perform the monitoring demonstration in High Temperature Engineering Tested Reactor (HTTR). The developing monitoring system based on some methods that have been tested using the data from online PWR simulator, as well as RSG-GAS (30 MW research reactor in Indonesia), will be applied in HTTR for more complex monitoring.

Journal Articles

Full integrated system of real-time monitoring based on distributed architecture for the High Temperature Engineering Test Reactor (HTTR)

Subekti, M.*; Ono, Tomio*; Kudo, Kazuhiko*; Takamatsu, Kuniyoshi; Nabeshima, Kunihiko

Proceedings of International Conference on Nuclear Energy System for Future Generation and Global Sustainability (GLOBAL 2005) (CD-ROM), 6 Pages, 2005/10

In this study, a new full integrated monitoring system scheme based on distributed architecture is proposed. This monitoring system has a distributed architecture; monitoring tasks are assigned to client PCs by the central server. As a result of the distributed architecture, it is expected that the processing capabilities is maximized and the real time consistency is not impaired even if heavy monitoring tasks cause a shortage of bandwidth. And this system integrates signal processing modules in the main system and the main system distributes the monitoring tasks on its client PCs with TCP-IP technology. Signal processing between the main system and the client PCs is optimized so that monitoring tasks are distributed very efficiently. And, each client PC is completely separated, processing condition of one PC never effects on the other PC's processing.

Journal Articles

Pre-test analysis method using a neural network for control-rod withdrawal tests of HTTR

Ono, Tomio*; Subekti, M.*; Kudo, Kazuhiko*; Takamatsu, Kuniyoshi; Nakagawa, Shigeaki; Nabeshima, Kunihiko

Nihon Genshiryoku Gakkai Wabun Rombunshi, 4(2), p.115 - 126, 2005/06

Control-rod withdrawal tests simulating reactivity insertion are carried out in the HTTR to verify the inherent safety features of HTGRs. This paper describes pre-test analysis method using artificial neural networks to predict the changes of reactor power and reactivity. The network model applied in this study is based on recurrent neural networks. The inputs of the network are the changes of the central control rods position and other significant core parameters, and the outputs are the changes of reactor power and reactivity. Furthermore, Time Synchronizing Signal(TSS) is added to input to improve the modeling of time series data. The actual tests data, which were previously carried out in the HTTR, were used for learning the model of the plant dynamics. After the learning, the network can predict the changes of reactor power and reactivity in the following tests.

Journal Articles

Development and test results of a 60kA HTS current lead for fusion application

Isono, Takaaki; Hamada, Kazuya; Kawano, Katsumi; Abe, Kanako*; Nunoya, Yoshihiko; Sugimoto, Makoto; Ando, Toshinari*; Okuno, Kiyoshi; Bono, Takaaki*; Tomioka, Akira*; et al.

Teion Kogaku, 39(3), p.122 - 129, 2004/03

JAERI has been developing a large-capacity high-temperature superconductor (HTS) current lead for fusion application, and succeeded in fabricating and testing a 60kA HTS current lead satisfying ITER requirements. Targets of performance are 1/10 heat leak and 1/3 electric power consumption of cryogenic system compared with a conventional lead. To achieve the target, selection of sheath material of HTS, optimizing the Cu part, reduction of joule heat at joint between HTS and Cu parts, improve of heat transfer between HTS and stainless steel tube. Developed 60kA HTS current lead satisfied the design condition and almost achieved the targets. Adoption of the HTS current lead can reduce 13% electric power consumption of cryogenic system for ITER.

Journal Articles

Nuclear power plant simulation using multilayer perceptron

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.

Journal Articles

Test results of 60-kA HTS current lead for fusion application

Isono, Takaaki; Kawano, Katsumi; Hamada, Kazuya; Matsui, Kunihiro; Nunoya, Yoshihiko; Hara, Eiji*; Kato, Takashi; Ando, Toshinari*; Okuno, Kiyoshi; Bono, Takaaki*; et al.

Physica C, 392-396(Part2), p.1219 - 1224, 2003/10

A 60-kA high-temperature-superconductor (HTS) current lead has been fabricated and tested for aiming at the application to a fusion magnet system, providing a low heat leak current lead. The design of HTS current leads is optimized not only to reduce the heat leak but also to perform safe operation even in fault conditions. The HTS current lead consists of a forced flow cooled copper part and a conduction cooled HTS part. The HTS part is composed of 288 Ag-10at.%Au sheathed Bi-2223 tapes and they are cylindrically arrayed on a stainless steel tube. The diameter and the length of the HTS part are 146 mm and 300 mm, respectively. Operation of a 60 kA current, which is the world record, was successfully achieved at coolant of 20 K, 3.2 g/s for the copper part, and a low heat leak of 5.5 W at 4.2 K was demonstrated. This result shows that the electric power of a refrigerator to cool the current lead can be reduced by 1/3 of that in a conventional current lead. In conclusion, technology of a large HTS current lead for fusion application is established.

Journal Articles

Nuclear reactor monitoring with the combination of neural network and expert system

Nabeshima, Kunihiko; Suzudo, Tomoaki; Ono, Tomio*; Kudo, Kazuhiko*

Mathematics and Computers in Simulation, 60(3-5), p.233 - 244, 2002/09

 Times Cited Count:12 Percentile:65.93(Computer Science, Interdisciplinary Applications)

This study presents a hybrid monitoring system for nuclear reactor utilizing neural networks and a rule-based real-time expert system. The whole monitoring system including a data acquisition system and the advisory displays has been tested by an on-line simulator of pressurized water reactor. From the testing results, it was shown that the neural network in the monitoring system successfully modeled the plant dynamics and detected the symptoms of anomalies earlier than the conventional alarm system. The real-time expert system also worked satisfactorily in diagnosing and displaying the system status by using the outputs of neural networks and a priori knowledge base.

Journal Articles

Neutro-expert monitoring system for nuclear power plant

Nabeshima, Kunihiko; Suzudo, Tomoaki; Ono, Tomio*; Kudo, Kazuhiko*

Knowledge-Based Intelligent Information Engineering Systems & Allied Technologies, p.1506 - 1510, 2001/09

no abstracts in English

Journal Articles

Design of a 60-kA HTS current lead for fusion magnets and its R&D

Ando, Toshinari; Isono, Takaaki; Hamada, Kazuya; Nishijima, Gen; Tsuji, Hiroshi; Tomioka, Akira*; Bono, Takaaki*; Yasukawa, Yukio*; Konno, Masayuki*; Uede, Toshio*

IEEE Transactions on Applied Superconductivity, 11(1), p.2535 - 2538, 2001/03

 Times Cited Count:6 Percentile:43.05(Engineering, Electrical & Electronic)

no abstracts in English

Journal Articles

Plant monitoring with the combination of recurrent neural network and real-time expert system

Nabeshima, Kunihiko; Suzudo, Tomoaki; Takizawa, Hiroshi*; Ono, Tomio*; Kudo, Kazuhiko*

Proceedings of International Topical Meeting on Nuclear Plant Instrumentation, Controls, and Human-Machine Interface Technologies (NPIC&HMIT 2000) (CD-ROM), 9 Pages, 2000/00

no abstracts in English

Journal Articles

Test results of high temperature superconductor current lead at 14.5kA operation

Isono, Takaaki; Hamada, Kazuya; Ando, Toshinari; Tsuji, Hiroshi; Yasukawa, Yukio*; Tomioka, Akira*; *; *; *

IEEE Transactions on Applied Superconductivity, 9(2), p.519 - 522, 1999/06

 Times Cited Count:8 Percentile:52.23(Engineering, Electrical & Electronic)

no abstracts in English

Journal Articles

Hybrid monitoring system for high temperature gas cooling reactor

Nabeshima, Kunihiko; Tuerkcan, E.*; Suzudo, Tomoaki; Nakagawa, Shigeaki; Inoue, K.*; Oono, Tomio*; *; Suzuki, Katsuo

Proc. of Human-Computer Interaction International'99, 2, p.1187 - 1191, 1999/00

no abstracts in English

Oral presentation

Verification of neural network application for reactivity determination during C-CR withdrawal test at HTTR

Subekti, M.*; Ono, Tomio*; Kudo, Kazuhiko*; Nabeshima, Kunihiko; Takamatsu, Kuniyoshi

no journal, , 

The determination of reactivity using alternative method, neural networks has been verified for C-CR withdrawal tests at power level of 9MW, 15MW, and 18MW. The neural network application has demonstrated for pre-test analysis and online reactivity determination as reference data for reactivity anomaly detection. The verification shows the best architecture of neural network that proposed for advanced online application.

Oral presentation

On-line monitoring for High Temperature Engineering Test Reactor (HTTR) using neural networks

Nabeshima, Kunihiko; Nakagawa, Shigeaki; Makino, Jun*; Matsuishi, Tomomi*; Subekti, M.*; Ono, Tomio*; Kudo, Kazuhiko*

no journal, , 

The neural networks have been utilized in on-line monitoring-system of High Temperature Engineering Tested Reactor (HTTR) with thermal power of 30MW. From the real-time test results during "reactivity insertion test; control rod withdrawal test" and "coolant flow reduction test", the monitoring system with neural networks showed good prediction and reliable detection performances.

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