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Maekawa, Fujio
JAEA-Conf 2022-001, p.7 - 13, 2022/11
The partitioning and transmutation (P-T) technology has promising potential for volume reduction and mitigation of degree of harmfulness of high-level radioactive waste. JAEA is developing the P-T technology combined with accelerator driven systems (ADS). One of critical issues affecting the feasibility of ADS is the proton beam window (PBW) which functions as a boundary between the accelerator and the sub-critical reactor core. The PBW is damaged by a high-intensity proton beam and spallation neutrons produced in the target, and also by flowing high-temperature liquid lead bismuth eutectic alloy which is corrosive to steel materials. To study the materials damage under the ADS environment, J-PARC is proposing a plan of proton irradiation facility which equips with a liquid lead-bismuth spallation target bombarded by a 400 MeV - 250 kW proton beam. The facility is also open for versatile purposes such as soft error testing of semi-conductor devises, RI production, materials irradiation for fission and fusion reactors, and so on. Application to nuclear data research with using the proton beam and spallation neutrons is also one of such versatile purposes, and we welcome unique ideas from the nuclear data community.
Rodriguez, D.; Rossi, F.; Seya, Michio; Koizumi, Mitsuo
Proceedings of 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC 2017) (Internet), 3 Pages, 2018/11
Suyama, Kenya; Nouri, A.*; Mochizuki, Hiroki*; Nomura, Yasushi*
JAERI-Conf 2003-019, p.890 - 892, 2003/10
Isotopic composition is one of the most relevant data to be used in the calculation of burnup of irradiated nuclear fuel. Since autumn 2002, the Organisation for Economic Co-operation and Development/Nuclear Energy Agency OECD/NEA) has operated a database of isotopic composition; SFCOMPO, initially developed in Japan Atomic Energy research Institute. This paper describes latest version of SFCOMPO and the future development plan in OECD/NEA.
Tsuji, Hirokazu; Fujii, Hidetoshi*
Proceedings of 10th German-Japanese Workshop on Chemical Information, p.127 - 130, 2002/00
A neural network model within a Bayesian framework was adopted based on the material database constructed by JAERI for prediction of creep rupture properties of irradiated type 304 stainless steel. Stress level was modeled as a function of 18 variables, including rupture life, creep test temperature, chemical compositions; 10 elements, heat treatment temperature, heat treatment duration, neutron irradiation temperature, fast neutron fluence, thermal neutron fluence, irradiation time, based on JAERI material database in which 347 creep rupture data sets of type 304 stainless steels were stored. The Bayesian method puts error bars on the predicted values of the rupture strength and allows the significance of each individual factor to be estimated.
Tanaka, Shigeru; R.Matera*; G.Kalinin*; V.Barabash*; Mori, Kensuke*
Journal of Nuclear Materials, 271-272, p.478 - 485, 1999/00
Times Cited Count：4 Percentile：34.81(Materials Science, Multidisciplinary)no abstracts in English
*; ; Tone, Tatsuzo; Mizumoto, Motoharu
10th Pacific Basin Nuclear Conf. (10-PBNC), 2, p.1425 - 1431, 1996/00
no abstracts in English
; ; *; Ikawa, Katsuichi
Nihon Genshiryoku Gakkai-Shi, 24(6), p.429 - 434, 1982/00
Times Cited Count：0 Percentile：0.02(Nuclear Science & Technology)no abstracts in English
