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Uchibori, Akihiro; Watanabe, Akira*; Takata, Takashi; Ohshima, Hiroyuki
Nippon Kikai Gakkai Rombunshu (Internet), 84(859), p.17-00394_1 - 17-00394_6, 2018/03
For assessment of the wastage environment under tube failure accident in a steam generator of sodium-cooled fast reactors, a mechanistic computer code called SERAPHIM calculating compressible multicomponent multiphase flow with sodium-water chemical reaction has been developed. The original SERAPHIM code is based on the finite difference method. In this study, unstructured mesh-based numerical method was developed and introduced into the SERAPHIM code to advance a numerical accuracy for the complex-shaped domain including multiple heat transfer tubes. Validity of the unstructured mesh-based SERAPHIM code was investigated through the analysis of an underexpanded jet experiment. The calculated pressure profile showed good agreement with the experimental data. Numerical analysis of water vapor discharging into liquid sodium was also performed. It was demonstrated that the proposed numerical method could be applicable to evaluation of the sodium-water reaction phenomenon.
Muramatsu, Kazuhiro; Otani, Takayuki; Matsumoto, Hideki*; Takei, Toshifumi*; Doi, Shun*
JAERI-Data/Code 98-014, 35 Pages, 1998/03
JAERI-Data-Code-98-014.pdf:1.83MB
no abstracts in English
*
Parallel Computational Fluid Dynamics, p.561 - 568, 1998/00
no abstracts in English
*
JAERI-Data/Code 97-012, 20 Pages, 1997/03
JAERI-Data-Code-97-012.pdf:1.09MB
no abstracts in English
Ishigaki, Masahiro; Abe, Satoshi; Shibamoto, Yasuteru; Yonomoto, Taisuke
no journal, ,
no abstracts in English
Kaku, Eiji*; Okamoto, Koji*; Kondo, Masahiro*; Ozdemir, E.*; Shiba, Tomoki*; Sato, Ikken
no journal, ,
In this study aiming at contribution for safe decommissioning of Fukushima-Daiichi NPP, CFD (Computational Fluid Dynamics) method was applied and temperature distribution of Unit 3 was reproduced. This temperature distribution was then compared with the measured data obtained by TEPCO so that debris distribution can be estimated. Combined application of optimized tools and CFD method to resolve inverse problem determining best suited thermal balance within the containment vessel is a characteristic of this study.
Motegi, Kosuke; Trianti, N.; Matsumoto, Toshinori; Sugiyama, Tomoyuki; Maruyama, Yu
no journal, ,
no abstracts in English
Hatayama, Sora*; Shimokawabe, Takashi*; Onodera, Naoyuki
no journal, ,
Computational fluid dynamics (CFD) is widely used as a fluid analysis technique. However, these have a problem that the calculation cost is very expensive and the execution time for reaching a steady-state is long. To solve this problem, we use convolutional neural networks (CNN), which is one of the deep learning methods, to predict CFD results. In this research, we provide the method and implementation of steady flow prediction using CNN with boundary exchange to predict the CFD results in a large area.
