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HPC Technology Promotion Office
JAEA-Review 2023-018, 159 Pages, 2023/12
JAEA-Review-2023-018.pdf:13.62MB
Japan Atomic Energy Agency (JAEA) conducts research and development (R&D) in various fields related to nuclear power as a comprehensive institution of nuclear energy R&Ds, and utilizes computational science and technology in many activities. Over the past 10 years or so, the publication of papers utilizing computational science and technology at JAEA has accounted for about 20 percent of the total publications each fiscal year. The supercomputer system of JAEA has become an important infrastructure to support computational science and technology. In FY2022, the system was used for R&D of light water reactors, high-temperature gas reactors, and fast reactors to contribute to carbon neutrality as a priority issue, as well as for JAEA's major projects such as Various R&D related to nuclear science and technology, R&D related to the response to the accident at TEPCO's Fukushima Daiichi Nuclear Power Station, Development of technology for treatment and disposal of high-level radioactive waste, Support of nuclear safety regulation and nuclear disaster prevention, and safety research for this purpose. This report presents a great number of R&D results accomplished by using the system in FY2022, as well as user support, operational records and overviews of the system, and so on.
Center for Computational Science & e-Systems
JAEA-Evaluation 2023-001, 38 Pages, 2023/07
JAEA-Evaluation-2023-001.pdf:1.04MB
Research on advanced computational science for nuclear applications, based on "the plan to achieve the medium- and long-term goal of the Japan Atomic Energy Agency", has been performed by Center for Computational Science & e-Systems (CCSE), Japan Atomic Energy Agency. CCSE established a committee consisting of external experts and authorities which evaluates and advises toward the future research and development. This report summarizes the results of the R&D performed by CCSE in FY2022 (April 1st, 2022 - March 31st, 2023) and their evaluation by the committee.
Li, C.-Y.; Watanabe, Akira*; Uchibori, Akihiro; Okano, Yasushi
Proceedings of 30th International Conference on Nuclear Engineering (ICONE30) (Internet), 10 Pages, 2023/05
HPC Technology Promotion Office
JAEA-Review 2022-035, 219 Pages, 2023/01
JAEA-Review-2022-035.pdf:10.94MB
Japan Atomic Energy Agency (JAEA) conducts research and development (R&D) in various fields related to nuclear power as a comprehensive institution of nuclear energy R&Ds, and utilizes computational science and technology in many activities. As shown in the fact that about 20 percent of papers published by JAEA are concerned with R&D using computational science, the supercomputer system of JAEA has become an important infrastructure to support computational science and technology. In FY2021, the system was used for R&D aiming to restore Fukushima (environmental recovery and nuclear installation decommissioning) as a priority issue, as well as for JAEA's major projects such as research and development of fast reactor cycle technology, research for safety improvement in the field of nuclear energy, and basic nuclear science and engineering research. This report presents a great number of R&D results accomplished by using the system in FY2021, as well as user support, operational records and overviews of the system, and so on.
Center for Computational Science & e-Systems
JAEA-Evaluation 2022-004, 38 Pages, 2022/11
JAEA-Evaluation-2022-004.pdf:1.38MB
Research on advanced computational science for nuclear applications, based on "the plan to achieve the mid- and long-term goal of the Japan Atomic Energy Agency", has been performed by Center for Computational Science & e-Systems (CCSE), Japan Atomic Energy Agency. CCSE established a committee consisting of external experts and authorities which evaluates and advises toward the future research and development. This report summarizes the results of the R&D performed by CCSE in FY2021 (April 1st, 2021 - March 31st, 2022) and their evaluation by the committee.
Center for Computational Science & e-Systems
JAEA-Evaluation 2022-003, 61 Pages, 2022/11
JAEA-Evaluation-2022-003.pdf:1.42MB
JAEA-Evaluation-2022-003-appendix(CD-ROM).zip:6.16MB
Japan Atomic Energy Agency (hereinafter referred to as "JAEA") consults an assessment committee, "Evaluation Committee of Research Activities for Computational Science and Technology Research" (hereinafter referred to as "Committee") for result and in-advance evaluation of "Computational Science and Technology Research", in accordance with "General Guideline for the Evaluation of Government Research and Development (R&D) Activities" by Cabinet Office, Government of Japan, "Guideline for Evaluation of R&D in Ministry of Education, Culture, Sports, Science and Technology" and "Regulation on Conduct for Evaluation of R&D Activities" by the JAEA. In response to the JAEA's request, the Committee assessed the research program of the Center for Computational Science and e-Systems (hereinafter referred to as "CCSE"). The Committee evaluated the management and research activities of the CCSE based on explanatory documents prepared by the CCSE, and oral presentations with questions-and answers.
Hamdani, A.; Abe, Satoshi; Ishigaki, Masahiro; Shibamoto, Yasuteru; Yonomoto, Taisuke
Progress in Nuclear Energy, 153, p.104415_1 - 104415_16, 2022/11
Times Cited Count：3 Percentile：68.71(Nuclear Science & Technology)Nakamura, Hideo; Bentaib, A.*; Herranz, L. E.*; Ruyer, P.*; Mascari, F.*; Jacquemain, D.*; Adorni, M.*
Proceedings of International Conference on Topical Issues in Nuclear Installation Safety; Strengthening Safety of Evolutionary and Innovative Reactor Designs (TIC 2022) (Internet), 10 Pages, 2022/10
Kubo, Kotaro; Fujiwara, Keita*; Tanaka, Yoichi; Hakuta, Yuto*; Arake, Daisuke*; Uchiyama, Tomoaki*; Muramatsu, Ken*
Proceedings of 29th International Conference on Nuclear Engineering (ICONE 29) (Internet), 8 Pages, 2022/08
After the Fukushima Daiichi Nuclear Power Plant accident, the importance of conducting probabilistic risk assessments (PRAs) of external events, especially seismic activities and tsunamis, was recognized. The Japan Atomic Energy Agency has been developing a computational methodology for seismic PRA, called the direct quantification of fault tree using Monte Carlo simulation (DQFM). When appropriate correlation matrices are available for seismic responses and capacities of components, the DQFM makes it possible to consider the effect of correlated failures of components connected through AND and/or OR gates in fault trees, which is practically difficult when methods using analytical solutions or multidimensional numerical integrations are used to obtain minimal cut set probabilities. The usefulness of DQFM has already been demonstrated. Nevertheless, a reduction of the computational time of DQFM would allow the large number of analyses required in PRAs conducted by regulators and/or operators. We; therefore, performed scoping calculations using three different approaches, namely quasi-Monte Carlo sampling, importance sampling, and parallel computing, to improve calculation efficiency. Quasi-Monte Carlo sampling, importance sampling, and parallel computing were applied when calculating the conditional core damage probability of a simplified PRA model of a pressurized water reactor, using the DQFM method. The results indicated that the quasi-Monte Carlo sampling works well at assumed medium and high ground motion levels, importance sampling is suitable for assumed low ground motion level, and that parallel computing enables practical uncertainty and importance analysis. The combined implementation of these improvements in a PRA code is expected to provide a significant acceleration of computation and offers the prospect of practical use of DQFM in risk-informed decision-making.
Li, C.-Y.; Watanabe, Akira*; Uchibori, Akihiro; Okano, Yasushi
Dai-26-Kai Doryoku, Enerugi Gijutsu Shimpojiumu Koen Rombunshu (Internet), 4 Pages, 2022/07
Identifying accident scenarios that could lead to severe accidents and evaluating their frequency of occurrence are essential issues. This study aims to establish the methodology of the dynamic Probabilistic Risk Assessment (PRA) for sodium-cooled fast reactors that can consider the time dependency and the interdependence of each event. Specifically, the Continuous Markov chain Monte Carlo (CMMC) method is newly applied to the SPECTRA code, which analyzes the severe accident conditions of nuclear reactors, to develop an evaluation methodology for typical external hazards. Currently, a fault-tree model of air coolers of decay heat removal system is implemented as the CMMC method, and a series of preliminary analysis of the plant's transient characteristics under the scenario of volcanic ashfall has been conducted.
HPC Technology Promotion Office
JAEA-Review 2021-022, 187 Pages, 2022/01
JAEA-Review-2021-022.pdf:10.11MB
Japan Atomic Energy Agency (JAEA) conducts research and development (R&D) in various fields related to nuclear power as a comprehensive institution of nuclear energy R&Ds, and utilizes computational science and technology in many activities. As shown in the fact that about 20 percent of papers published by JAEA are concerned with R&D using computational science, the supercomputer system of JAEA has become an important infrastructure to support computational science and technology. In FY2020, the system was used for R&D aiming to restore Fukushima (environmental recovery and nuclear installation decommissioning) as a priority issue, as well as for JAEA's major projects such as research and development of fast reactor cycle technology, research for safety improvement in the field of nuclear energy, and basic nuclear science and engineering research. This report presents a great number of R&D results accomplished by using the system in FY2020, as well as user support, operational records and overviews of the system, and so on.
Center for Computational Science & e-Systems
JAEA-Evaluation 2021-001, 66 Pages, 2021/11
JAEA-Evaluation-2021-001.pdf:1.66MB
Research on advanced computational science for nuclear applications, based on "the plan to achieve the mid- and long-term goal of the Japan Atomic Energy Agency", has been performed by Center for Computational Science & e-Systems (CCSE), Japan Atomic Energy Agency. CCSE established a committee consisting of external experts and authorities which does research evaluation and advice for the assistance of the future research and development. This report summarizes the results of the R&D performed by CCSE in FY2020 (April 1st, 2020 - March 31st, 2021), the results expected at the end of the 3rd mid and long-term goal period, and the evaluation by the committee on them.
Li, C.-Y.; Uchibori, Akihiro; Takata, Takashi; Pellegrini, M.*; Erkan, N.*; Okamoto, Koji*
Dai-25-Kai Doryoku, Enerugi Gijutsu Shimpojiumu Koen Rombunshu (Internet), 4 Pages, 2021/07
The capability of stable cooling and avoiding re-criticality on the debris bed are the main issues for achieving IVR (In-Vessel Retention). In the actual situation, the debris bed is composed of mixed-density debris particles. Hence, when these mixed-density debris particles were launched to re-distribute, the debris bed would possibly form a density-stratified distribution. For the proper evaluation of this scenario, the multi-physics model of CFD-DEM-Monte-Carlo based neutronics is established to investigate the coolability and re-criticality on the heterogeneous density-stratified debris bed with considering the particle relocation. The CFD-DEM model has been verified by utilizing water injection experiments on the mixed-density particle bed in the first portion of this research. In the second portion, the coupled system of the CFD-DEM-Monte-Carlo based neutronics model is applied to reactor cases. Afterward, the debris particles' movement, debris particles' and coolant's temperature, and the k-eff eigenvalue are successfully tracked. Ultimately, the relocation and stratification effects on debris bed's coolability and re-criticality had been quantitatively confirmed.
HPC Technology Promotion Office
JAEA-Review 2020-021, 215 Pages, 2021/02
JAEA-Review-2020-021.pdf:13.11MB
Japan Atomic Energy Agency (JAEA) conducts research and development (R&D) in various fields related to nuclear power as a comprehensive institution of nuclear energy R&Ds, and utilizes computational science and technology in many activities. As shown in the fact that about 20 percent of papers published by JAEA are concerned with R&D using computational science, the supercomputer system of JAEA has become an important infrastructure to support computational science and technology. In FY2019, the system was used for R&D aiming to restore Fukushima (environmental recovery and nuclear installation decommissioning) as a priority issue, as well as for JAEA's major projects such as research and development of fast reactor cycle technology, research for safety improvement in the field of nuclear energy, and basic nuclear science and engineering research. This report presents a great number of R&D results accomplished by using the system in FY2019, as well as user support, operational records and overviews of the system, and so on.
Okagaki, Yuria; Yonomoto, Taisuke; Ishigaki, Masahiro; Hirose, Yoshiyasu
Fluids (Internet), 6(2), p.80_1 - 80_17, 2021/02
Saito, Shimpei*; De Rosis, A.*; Fei, L.*; Luo, K. H.*; Ebihara, Kenichi; Kaneko, Akiko*; Abe, Yutaka*
Physics of Fluids, 33(2), p.023307_1 - 023307_21, 2021/02
Times Cited Count：31 Percentile：98.32(Mechanics)A Boiling phenomenon in a liquid flow field is known as forced-convection boiling. We numerically investigated the boiling system on a cylinder in a flow at a saturated condition. To deal with such a phenomenon, we developed a numerical scheme based on the pseudopotential lattice Boltzmann method. The collision was performed in the space of central moments (CMs) to enhance stability for high Reynolds numbers. Furthermore, additional terms for thermodynamic consistency were derived in a CMs framework. The effectiveness of the model was tested against some boiling processes, including nucleation, growth, and departure of a vapor bubble for high Reynolds numbers. Our model can reproduce all the boiling regimes without the artificial initial vapor phase. We found that the Nukiyama curve appears even though the focused system is the forced-convection system. Also, our simulations support experimental observations of intermittent direct solid-liquid contact even in the film-boiling regime.
Center for Computational Science & e-Systems
JAEA-Evaluation 2020-002, 37 Pages, 2020/12
JAEA-Evaluation-2020-002.pdf:1.59MB
Research on advanced computational science for nuclear applications, based on "the plan to achieve the mid and long term goal of the Japan Atomic Energy Agency", has been performed at Center for Computational Science & e-Systems (CCSE), Japan Atomic Energy Agency. CCSE established a committee consisting of outside experts and authorities which does research evaluation and advice for the assistance of the future research and development. This report summarizes the results of the R&D performed at CCSE in FY2019 (April 1st, 2019 - March 31st, 2020) and the evaluation by the committee on them.
Herranz, L. E.*; Jacquemain, D.*; Nitheanandan, T.*; Sandberg, N.*; Barr
, F.*; Bechta, S.*; Choi, K.-Y.*; D'Auria, F.*; Lee, R.*; Nakamura, Hideo
Progress in Nuclear Energy, 127, p.103432_1 - 103432_14, 2020/09
Times Cited Count：3 Percentile：11.26(Nuclear Science & Technology)Trianti, N.; Motegi, Kosuke; Sugiyama, Tomoyuki; Maruyama, Yu
Proceedings of 2020 International Conference on Nuclear Engineering (ICONE 2020) (Internet), 9 Pages, 2020/08
Asahi, Yuichi*; Latu, G.*; Bigot, J.*; Maeyama, Shinya*; Grandgirard, V.*; Idomura, Yasuhiro
Concurrency and Computation; Practice and Experience, 32(5), p.e5551_1 - e5551_21, 2020/03
Times Cited Count：1 Percentile：14.03(Computer Science, Software Engineering)Two five-dimensional gyrokinetic codes GYSELA and GKV were ported to the modern accelerators, Xeon Phi KNL and Tesla P100 GPU. Serial computing kernels of GYSELA on KNL and GKV on P100 GPU were respectively 1.3x and 7.4x faster than those on a single Skylake processor. Scaling tests of GYSELA and GKV were respectively performed from 16 to 512 KNLs and from 32 to 256 P100 GPUs, and data transpose communications in semi-Lagrangian kernels in GYSELA and in convolution kernels in GKV were found to be main bottlenecks, respectively. In order to mitigate the communication costs, pipeline-based and task-based communication overlapping were implemented in these codes.
