Development of a Machine Learning Method to Predict the Break Diameter during PWR Loss-of-Coolant Accident-Reference-Cited by-同舟云学术

Development of a Machine Learning Method to Predict the Break Diameter during PWR Loss-of-Coolant Accident

Published:2022 Issue:2 Volume:21 Page:96-105
ISSN:1347-2879
Container-title:Transactions of the Atomic Energy Society of Japan
language:en
Short-container-title:Transactions of the Atomic Energy Society of Japan

Author:

NAKAMURA Akira¹,KUSUNOKI Takayoshi¹

Affiliation:

1. Institute of Nuclear Safety System, Inc.

Publisher

Atomic Energy Society of Japan

Subject

Safety, Risk, Reliability and Quality,Nuclear Energy and Engineering

Link

https://www.jstage.jst.go.jp/article/taesj/21/2/21_J21.009/_pdf

Reference13 articles.

1. 1) Japan Atomic Energy Commission, Genshiryokuhakusyo (2019), [in Japanese], [Internet], http://www.aec.go.jp/jicst/NC/about/hakusho/hakusho2019/zentai.pdf, (cited 2021 Jun 22).

2. 2) Central Disaster Management Council, Bousaikihonkeikaku (2020), [in Japanese], [Internet], http://www.bousai.go.jp/taisaku/keikaku/pdf/kihon_basicplan.pdf, (cited 2021 Jun 22).

3. 3) Japan Nuclear Regulation Authority, The 11th Meeting of the Debrief Meeting of Nuclear Operator’s Emergency Training (2019), [in Japanese], [Internet], https://www.nsr.go.jp/data/000275094.pdf, (cited 2021 Jun 22).

4. 4) Y. Yoshida, Y. Yamamoto, T. Kusunoki et al., “Development of incident progress prediction technologies for nuclear emergency preparedness – Current status and future subjects –,” INSS J., 21, RV-1, Institute of Nuclear Safety System, Inc. (2014), [in Japanese], http://www.inss.co.jp/wp-content/uploads/2017/03/2014_21J223_237.pdf.

5. 5) MG. Na, SH. Shin, DW. Jung et al., “Estimation of break location and size for loss of coolant accidents using neural networks,” Nucl. Eng. Des., 232, 289–300 (2004), DOI：https://doi.org/10.1016/j.nucengdes.2004.06.007.