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Journal Articles

Nuclear data processing code FRENDY; A Verification with HTTR criticality benchmark experiments

Fujimoto, Nozomu*; Tada, Kenichi; Ho, H. Q.; Hamamoto, Shimpei; Nagasumi, Satoru; Ishitsuka, Etsuo

Annals of Nuclear Energy, 158, p.108270_1 - 108270_8, 2021/08

 Times Cited Count:0 Percentile:0.01(Nuclear Science & Technology)

Journal Articles

FRENDY; A New nuclear date processing system being developed at JAEA

Tada, Kenichi; Nagaya, Yasunobu; Kunieda, Satoshi; Suyama, Kenya; Fukahori, Tokio

EPJ Web of Conferences, 146, p.02028_1 - 02028_5, 2017/09

 Times Cited Count:0 Percentile:0.08

JAEA has started to develop new nuclear data processing system FRENDY (FRom Evaluated Nuclear Data libralY to any application). In this presentation, the outline of the development of FRENDY is presented. And functions and performances of FRENDY are demonstrated by generation and validation of the continuous energy cross section data libraries for MVP, PHITS and MCNP codes.

Journal Articles

New critical facilities toward their first criticality, STACY and TRACY in NUCEF

Tonoike, Kotaro; Izawa, Naoki; Okazaki, Shuji; Sugikawa, Susumu; Takeshita, Isao; *

ICNC 95: 5th Int. Conf. on Nuclear Criticality Safety,Vol. II, 0, p.10.25 - 10.32, 1995/00

no abstracts in English

JAEA Reports

System Program for MICRO-CAMAC Terminal System

*; ; ;

JAERI-M 8349, 68 Pages, 1979/08


no abstracts in English

Oral presentation

Improvement of parallel visualization efficiency by pre-processing large-scale distributed data

Guo, Z.; Nishida, Akemi; Choi, B.; Nakajima, Norihiro

no journal, , 

In the field of seismic analysis of nuclear facilities, large-scale parallel analyses using numerical models with several hundred millions of DOFs are becoming possible by the recent advances in high-performance parallel computing technologies. In dealing with such three dimensional time series data, the post-processing may be often more difficult than the seismic response simulation itself. The purpose of the current study is to develop a parallel visualization application, which can visualize large-scale simulation results (distributed time series data) effectively. In this report, we show an approach to increase the efficiency of parallel visualization by more than 200 times by using appropriate pre-processing for this kind of large-scale distributed time series data.

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