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Nguyen, T.-B.; Hirose, Yoshiyasu; Satou, Akira; Abe, Satoshi; Shibamoto, Yasuteru; Okawa, Tomio*
Nuclear Engineering and Design, 457, p.115036_1 - 115036_20, 2026/10
Times Cited Count：0 Percentile：0.00(Nuclear Science & Technology)Collaborative Laboratories for Advanced Decommissioning Science; Sapporo University*
JAEA-Review 2026-020, 81 Pages, 2026/09
The Collaborative Laboratories for Advanced Decommissioning Science (CLADS), Japan Atomic Energy Agency (JAEA), has been conducting the Nuclear Energy Science & Technology and Human Resource Development Project (hereafter referred to "the Project") from FY2019. The Project aims to contribute to solving problems in the nuclear energy field represented by the decommissioning of the Fukushima Daiichi Nuclear Power Station (1F), Tokyo Electric Power Company Holdings, Inc. (TEPCO). For this purpose, intelligence was collected from all over the world, and basic research and human resource development were promoted by closely integrating/collaborating knowledge and experiences in various fields beyond the barrier of conventional organizations and research fields. The sponsor of the Project was moved from the Ministry of Education, Culture, Sports, Science and Technology to JAEA since the newly adopted proposals in FY2018. On this occasion, JAEA constructed a new research system where JAEA-academia collaboration is reinforced and medium-to-long term research/development and human resource development contributing to the decommissioning are stably and consecutively implemented. Among the adopted proposals in FY2023, this report summarizes the research results of the "High-speed 3D modeling for nuclear reactor environment based on feature extraction results from video images" conducted in FY2024. The present study aims to develop a methodology for 3D modeling of workspaces using video footage captured during surveys of the reactor containment vessel and reactor building at 1F. Based on features extracted from the video within a specified timeframe, the method selects a high-information-content 3D reconstruction approach while augmenting the surrounding contextual information. In fiscal year 2024, we developed a methodology to achieve high-precision photogrammetry-based 3D reconstruction within a specified time limit, using a single video sequence obtained from simulation as input. We also devised a 3D modeling algorithm based on structural grouping and concurrently implemented an optimization algorithm to reduce computation time for 3D reconstruction, thereby accelerating the overall modeling process. Furthermore, we quantitatively evaluated the generation accuracy of moving image data using generative models and continued to enhance both hardware construction and software development to further improve computational efficiency.
Collaborative Laboratories for Advanced Decommissioning Science; Sapporo University*
JAEA-Review 2025-033, 71 Pages, 2025/11
JAEA-Review-2025-033.pdf:4.48MB
The Collaborative Laboratories for Advanced Decommissioning Science (CLADS), Japan Atomic Energy Agency (JAEA), had been conducting the Nuclear Energy Science & Technology and Human Resource Development Project (hereafter referred to "the Project") in FY2023. The Project aims to contribute to solving problems in the nuclear energy field represented by the decommissioning of the Fukushima Daiichi Nuclear Power Station (1F), Tokyo Electric Power Company Holdings, Inc. (TEPCO). For this purpose, intelligence was collected from all over the world, and basic research and human resource development were promoted by closely integrating/collaborating knowledge and experiences in various fields beyond the barrier of conventional organizations and research fields. The sponsor of the Project was moved from the Ministry of Education, Culture, Sports, Science and Technology to JAEA since the newly adopted proposals in FY2018. On this occasion, JAEA constructed a new research system where JAEA-academia collaboration is reinforced and medium-to-long term research/development and human resource development contributing to the decommissioning are stably and consecutively implemented. Among the adopted proposals in FY2023, this report summarizes the research results of the "High-speed 3D modeling for nuclear reactor environment based on feature extraction results from video images" conducted in FY2023. The present study aims to develop a 3D model for a workspace that maximizes the amount of information based on the features extracted from video, which is taken when surveying the primary containment vessel and inside the reactor building as part of the decommissioning of 1F, considering within a specified time. In FY2023, we verified extracting effective shooting conditions for obtaining 3D reconstruction based on photogrammetry and the method extracting feature values that can generate 3D restoration results from a small amount of data within a specified time based on deep learning. In addition, we applied point cloud data extracted from video to segmentation and classified it into parts with instance labels.
Kojima, Yuya*; Murakawa, Hideki*; Sugimoto, Katsumi*; Kondo, Teppei*; Abe, Yuta; Aizawa, Kosuke
Proceedings of 13th International Symposium on Measurement Techniques for Multiphase Flows (ISMTMF 2025) (Internet), 5 Pages, 2025/10
Kim, Y. S.*; Kang, T.*; Hong, S.-K.*; Brechtl, J.*; Lebyodkin, M.*; Cheng, Y.-H.*; Huang, E.-W.*; Liaw, P. K.*; Harjo, S.; Gong, W.; et al.
Acta Materialia, 292, p.120970_1 - 120970_16, 2025/06
Times Cited Count：16 Percentile：96.07(Materials Science, Multidisciplinary)Nuclear Human Resource Development Center
JAEA-Review 2024-048, 69 Pages, 2025/01
JAEA-Review-2024-048.pdf:3.5MB
This annual report summarizes the activities of Nuclear Human Resource Development Center (NuHRDeC) of Japan Atomic Energy Agency (JAEA) in the fiscal year (FY) 2023. In FY 2023, in addition to the regular training programs at NuHRDeC, we actively organized special training courses responding to the external training needs, cooperated with universities, offered international training courses for Asian countries, and promoted activities of the Japan Nuclear Human Resource Development Network (JN-HRD.net) and the human resource development concierge. Regular domestic training programs; training courses for radioisotopes and radiation engineers, nuclear energy engineers and national qualification examinations, were conducted as scheduled in the annual plan. We also delivered training for other organizations outside the JAEA. We continued cooperative activities with universities, such as acceptance of postdoctoral researchers, and activities in line with the cooperative graduate school system, including the acceptance of students from Nuclear Professional School, the University of Tokyo. Furthermore, joint course among seven universities was successfully held by utilizing remote education system. The joint course and the intensive summer course and nuclear fuel cycle training were conducted as part of the collaboration network with universities. The Instructor Training Program (ITP) under the contract with Ministry of Education, Culture, Sports, Science and Technology, was continually offered to the ITP participating countries. As part of the ITP, the Instructor Training courses such as "Reactor Engineering Course", advanced instructor training course, and the nuclear technology seminar "Basic Radiation Knowledge for School Education" were conducted at NuHRDeC. As joint secretariat of JN-HRD.net, we steadily facilitated the network and actively held seminar, training, and facility tours for students.
Nuclear Human Resource Development Center
JAEA-Review 2023-034, 67 Pages, 2024/01
JAEA-Review-2023-034.pdf:2.32MB
This annual report summarizes the activities of Nuclear Human Resource Development Center (NuHRDeC) of Japan Atomic Energy Agency (JAEA) in the fiscal year (FY) 2022. In FY 2022, in addition to the regular training programs at NuHRDeC, we actively organized special training courses responding to the external training needs, cooperated with universities, offered international training courses for Asian countries and promoted activities of the Japan Nuclear Human Resource Development Network (JN-HRD.net). In FY2022, we were able to implement face-to-face training, etc., after thoroughly implementing measures to prevent the spread of the new coronavirus infection. Regular domestic training programs; training courses for radioisotopes and radiation engineers, nuclear energy engineers and national qualification examinations, were conducted as scheduled in the annual plan. We also delivered training for the Japan Atomic Power Company and other organizations outside the JAEA. We continued cooperative activities with universities, such as acceptance of postdoctoral researchers, and activities in line with the cooperative graduate school system, including the acceptance of students from Nuclear Professional School, the University of Tokyo. Furthermore, joint course among seven universities was successfully held by utilizing remote education system. The joint course and the intensive summer course and nuclear fuel cycle training were conducted as part of the collaboration network with universities. The Instructor Training Program (ITP) under the contract with Ministry of Education, Culture, Sports, Science and Technology, was continually offered to the ITP participating countries. As part of the ITP, the Instructor Training courses such as "Reactor Engineering Course", advanced instructor training course, and the nuclear technology seminar "Basic Radiation Knowledge for School Education" were conducted face-to-face at NuHRDeC.
Tuya, D.; Nagaya, Yasunobu
Journal of Nuclear Engineering (Internet), 4(4), p.691 - 710, 2023/11
The Monte Carlo method is used to accurately estimate various quantities such as k-eigenvalue and integral neutron flux. However, when a distribution of a quantity is desired, the Monte Carlo method does not typically provide continuous distribution. Recently, the functional expansion tally and kernel density estimation methods have been developed to provide continuous distribution. In this paper, we propose a method to estimate a continuous distribution of a quantity using artificial neural network (ANN) model with Monte Carlo-based training data. As a proof of concept, a continuous distribution of iterated fission probability (IFP) is estimated by ANN models in two systems. The IFP distributions by the ANN models were compared with the Monte Carlo-based data and the adjoint angular neutron fluxes by the PARTISN code. The comparisons showed varying degrees of agreement or discrepancy; however, it was observed that the ANN models learned the general trend of the IFP distributions.
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HRLSawada, Atsushi; Sakamoto, Kazuhiko*; Watahiki, Takanori*; Imai, Hisashi*
SKB P-17-06, 154 Pages, 2023/08
Asahi, Yuichi; Onodera, Naoyuki; Hasegawa, Yuta; Shimokawabe, Takashi*; Shiba, Hayato*; Idomura, Yasuhiro
Boundary-Layer Meteorology, 186(3), p.659 - 692, 2023/03
Times Cited Count：8 Percentile：45.09(Meteorology & Atmospheric Sciences)We develop a Transformer-based deep learning model to predict the plume concentrations in the urban area under uniform flow conditions. Our model has two distinct input layers: Transformer layers for sequential data and convolutional layers in convolutional neural networks (CNNs) for image-like data. Our model can predict the plume concentration from realistically available data such as the time series monitoring data at a few observation stations and the building shapes and the source location. It is shown that the model can give reasonably accurate prediction with orders of magnitude faster than CFD simulations. It is also shown that the exactly same model can be applied to predict the source location, which also gives reasonable prediction accuracy.
Park, Y.-J.*; Sawada, Atsushi; Ozutsumi, Takenori*; Tanaka, Tatsuya*; Hashimoto, Shuji*; Morita, Yutaka*
Proceedings of 3rd International Conference on Discrete Fracture Network Engineering (DFNE 2022) (Internet), 8 Pages, 2022/00
Safety analysis for underground disposal facilities for high-level radioactive waste requires thorough understanding of long-term groundwater flow and nuclide migration processes in geologic media. In the coastal subsurface systems, groundwater flow is defined by the complex interactions between freshwater of meteoric origin and denser saline water from the sea. In addition, sea levels are expected to fluctuate significantly due to a transgression and regression of the sea over the millions of years for safety analysis. This study presents long-term evolution of groundwater environment such as salinity concentration and flow velocity with focus of the interaction between fractures and matrix blocks in regional and near-field scale analysis framework for groundwater flow and nuclide migration for underground disposal facilities in hypothetical fractured crystalline coastal systems.
Nomura, Masahiro; Okita, Hidefumi; Shimada, Taihei; Tamura, Fumihiko; Yamamoto, Masanobu; Furusawa, Masashi*; Sugiyama, Yasuyuki*; Hasegawa, Katsushi*; Hara, Keigo*; Omori, Chihiro*; et al.
Proceedings of 18th Annual Meeting of Particle Accelerator Society of Japan (Internet), p.80 - 82, 2021/10
no abstracts in English
Asahi, Yuichi; Hatayama, Sora*; Shimokawabe, Takashi*; Onodera, Naoyuki; Hasegawa, Yuta; Idomura, Yasuhiro
Proceedings of 2021 IEEE International Conference on Cluster Computing (IEEE Cluster 2021) (Internet), p.686 - 691, 2021/10
Times Cited Count：3 Percentile：62.52(Computer Science, Hardware & Architecture)We develop a convolutional neural network model to predict the multi-resolution steady flow. Based on the state-of-the-art image-to-image translation model pix2pixHD, our model can predict the high resolution flow field from the set of patched signed distance functions. By patching the high resolution data, the memory requirements in our model is suppressed compared to pix2pixHD.
Nuclear Human Resource Development Center
JAEA-Review 2021-010, 70 Pages, 2021/09
JAEA-Review-2021-010.pdf:3.53MB
This annual report summarizes the activities of Nuclear Human Resource Development Center (NuHRDeC) of Japan Atomic Energy Agency (JAEA) in the fiscal year (FY) 2019.
Koyamada, Koji*; Yu, L.*; Kawamura, Takuma; Konishi, Katsumi*
International Journal of Modeling, Simulation, and Scientific Computing, 12(2), p.2140001_1 - 2140001_19, 2021/04
With the improvement of sensors technologies in various fields such as fluid dynamics, meteorology, and space observation, it is an important issue to derive explanatory models using partial differential equations (PDEs) for the big data obtained from them. In this paper, we propose a technique for estimating linear PDEs with higher-order derivatives for spatiotemporally discrete point cloud data. The technique calculates the time and space derivatives from a neural network (NN) trained on the point cloud data, and estimates the derivative term of the PDE using regression analysis techniques. In the experiment, we computed the error of the estimated PDEs for various meta-parameters for the PDEs with exact solutions. As a result, we found that increasing the hierarchy of NNs to match the order of the derivative terms in the exact solution PDEs and adopting L1 regularization with LASSO as the method of regression analysis increased the accuracy of the model.
Okuno, Hiroshi; Yamamoto, Kazuya
JAEA-Review 2020-066, 32 Pages, 2021/02
JAEA-Review-2020-066.pdf:3.01MB
The International Atomic Energy Agency (abbreviated as IAEA) has been implementing the Asian Nuclear Safety Network (abbreviated as ANSN) activities since 2002. As part of this effort, Topical Group on Emergency Preparedness and Response (abbreviated as EPRTG) for nuclear or radiation disasters was established in 2006 under the umbrella of the ANSN. Based on the EPRTG proposal, the IAEA conducted 23 Asian regional workshops in the 12 years from 2006 to 2017. Typical topical fields of the regional workshops were nuclear emergency drills, emergency medical care, long-term response after nuclear/radiological emergency, international cooperation, national nuclear disaster prevention system. The Japan Atomic Energy Agency has produced coordinators for EPRTG since its establishment and has led its activities since then. This report summarizes the Asian regional workshops conducted by the IAEA based on the recommendations of the EPRTG.
Togawa, Orihiko; Hayakawa, Tsuyoshi; Tanaka, Tadao; Yamamoto, Kazuya; Okuno, Hiroshi
JAEA-Review 2020-017, 36 Pages, 2020/09
JAEA-Review-2020-017.pdf:2.24MB
In 2010, the government of Japan joined the Response and Assistance Network (RANET) of the International Atomic Energy Agency (IAEA), in order to contribute to offering international assistance in the case of a nuclear accident or radiological emergency. At that occasion, the Japan Atomic Energy Agency (JAEA) was registered as the National Assistance Capability (NAC) having resources capable of the External Based Support (EBS) in the following seven areas: (1) aerial survey, (2) radiation monitoring, (3) environmental measurements, (4) assessment and advice, (5) internal dose assessment, (6) bioassay and (7) dose reconstruction. After the registration, three inquiries were directed to the JAEA about a possibility of its support. However, the JAEA's assistance has not eventually been realized. On the other hand, the JAEA participated almost every year in the international Convention Exercise (ConvEx) carried out by the IAEA in connection with RANET. This report describes an outline of the RANET and related activities of the JAEA for RANET registration and participation in the ConvEx.
Sun, D.*; Wainwright-Murakami, Haruko*; Oroza, C. A.*; Seki, Akiyuki; Mikami, Satoshi; Takemiya, Hiroshi; Saito, Kimiaki
Journal of Environmental Radioactivity, 220-221, p.106281_1 - 106281_8, 2020/09
Times Cited Count：17 Percentile：45.01(Environmental Sciences)We have developed a methodology for optimizing the monitoring locations of radiation air dose-rate monitoring. For the method, we use a Gaussian mixture model to identify the representative locations among multiple environmental variables, such as elevation and land-cover types. Next, we use a Gaussian process model to capture and estimate the heterogeneity of air-dose rates across the domain. Our results have shown that this approach allows us to select monitoring locations in a systematic manner such that the heterogeneity of air dose rates is captured by the minimal number of monitoring locations.
Nuclear Human Resource Development Center
JAEA-Review 2020-008, 74 Pages, 2020/06
JAEA-Review-2020-008.pdf:3.5MB
This annual report summarizes the activities of Nuclear Human Resource Development Center (NuHRDeC) of Japan Atomic Energy Agency (JAEA) in the fiscal year (FY) 2018.
Nuclear Human Resource Development Center
JAEA-Review 2019-009, 65 Pages, 2019/09
JAEA-Review-2019-009.pdf:5.56MB
This annual report summarizes the activities of Nuclear Human Resource Development Center (NuHRDeC) of Japan Atomic Energy Agency (JAEA) in the fiscal year (FY) 2017.
