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Journal Articles

Dynamic probabilistic risk assessment of nuclear power plants using multi-fidelity simulations

Zheng, X.; Tamaki, Hitoshi; Sugiyama, Tomoyuki; Maruyama, Yu

Reliability Engineering & System Safety, 223, p.108503_1 - 108503_12, 2022/07

 Times Cited Count:1 Percentile:0.03(Engineering, Industrial)

JAEA Reports

Interdisciplinary evaluation of biological effect of internal exposure by inhaling alpha-ray emitting nuclides represented by radon (Contract research); FY2020 Nuclear Energy Science & Technology and Human Resource Development Project

Collaborative Laboratories for Advanced Decommissioning Science; Okayama University*

JAEA-Review 2021-028, 57 Pages, 2021/11

JAEA-Review-2021-028.pdf:1.94MB

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 FY2020. The Project aims to contribute to solving problems in the nuclear energy field represented by the decommissioning of the Fukushima Daiichi Nuclear Power Station, 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 FY2018, this report summarizes the research results of the "Interdisciplinary evaluation of biological effect of internal exposure by inhaling alpha-ray emitting nuclides represented by radon" conducted from FY2018 to FY2020. Since the final year of this proposal was FY2020, the results for three fiscal years were summarized. The present study aims to evaluate the influence of radiation exposure to alpha-ray emitting dusts generated in decommissioning of the nuclear reactors. Radon is used here as a surrogate nuclide because it is an alpha-ray emitter and there have been extensive studies on it so far. The effect of alpha-ray emitted from a certain cell on its surrounding cells is estimated, and also biological response to alpha-ray exposure is investigated at the tissue and

JAEA Reports

Interdisciplinary evaluation of biological effect of internal exposure by inhaling alpha-ray emitting nuclides represented by radon (Contract research); FY2019 Nuclear Energy Science & Technology and Human Resource Development Project

Collaborative Laboratories for Advanced Decommissioning Science; Okayama University*

JAEA-Review 2020-029, 55 Pages, 2020/12

JAEA-Review-2020-029.pdf:2.08MB

JAEA/CLADS had been conducting the Nuclear Energy Science & Technology and Human Resource Development Project in FY2019. Among the adopted proposals in FY2018, this report summarizes the research results of the "Interdisciplinary Evaluation of Biological Effect of Internal Exposure by Inhaling Alpha-ray Emitting Nuclides Represented by Radon" conducted in FY2019.

Journal Articles

Optimizing long-term monitoring of radiation air-dose rates after the Fukushima Daiichi Nuclear Power Plant

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:6 Percentile:54.31(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.

JAEA Reports

Interdisciplinary evaluation of biological effect of internal exposure by inhaling alpha-ray emitting nuclides represented by radon (Contract research); FY2018 Center of World Intelligence Project for Nuclear Science/Technology and Human Resource Development

Collaborative Laboratories for Advanced Decommissioning Science; Okayama University*

JAEA-Review 2019-024, 61 Pages, 2020/01

JAEA-Review-2019-024.pdf:2.22MB

CLADS, JAEA, had been conducting the Center of World Intelligence Project for Nuclear Science/Technology and Human Resource Development (hereafter referred to "the Project") in FY2018. The Project aims to contribute to solving problems in nuclear energy field represented by the decommissioning of the Fukushima Daiichi Nuclear Power Station, Tokyo Electric Power Company Holdings, Inc. 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 FY2018, this report summarizes the research results of the "Interdisciplinary Evaluation of Biological Effect of Internal Exposure by Inhaling Alpha-ray Emitting Nuclides Represented by Radon". In the present study, the effect of alpha-ray emission in human body on the surrounding cells is estimated, and biological response to alpha-ray exposure is investigated at the whole organism level, by the evaluation method for radiation effects using radon that is an alpha-ray emitting nuclide, because there have been extensive studies on radon so far. From the obtained results, a model to evaluate the effect of internal exposure by alpha-ray emitting nuclides on health is constructed. Through these studies, we aim to form a research base by the interdisciplinary organic collaboration among research organizations.

Journal Articles

Global continuous optimization with error bound and fast convergence

Kawaguchi, Kenji*; Maruyama, Yu; Zheng, X.

Journal of Artificial Intelligence Research, 56, p.153 - 195, 2016/06

 Times Cited Count:11 Percentile:56.95(Computer Science, Artificial Intelligence)

Oral presentation

Machine learning based metabolic analysis about the regulation of glutathione synthesis in mouse brain due to radon inhalation

Kanzaki, Norie; Sakoda, Akihiro; Tanaka, Hiroshi; Kataoka, Takahiro*; Ishida, Tsuyoshi; Yamaoka, Kiyonori*

no journal, , 

We found that glutathione was elevated by radon inhalation. However, the mechanism of production of glutathione has been known quite little. In the present study, the metabolic analysis focusing on the sulfur metabolites including anti-oxidative substances was performed to find the change in sulfur metabolites in mouse brain following radon inhalation. Brains taken from male BALB/c mice after radon exposure at background level, 1000 or 10000 Bq/m$$^{3}$$ for 24 h were used for the exhaustive assay of sulfur metabolism-related substances. As a result, we detected 27 out of 55 metabolites in brain. We found the induction of glutathione precursor cysteine and cystathionine due to radon inhalation. This suggests that the increase of glutathione associated with radon inhalation could rely on the transsulfuration pathway. Moreover, the information on 27 sulfur metabolites detected in the present assay was comprehensively analyzed using self-organizing maps. Based on this clustering, a possibility of sulfur metabolites working as a biomarker for estimating radiation doses will be discussed.

Oral presentation

Spectral analysis method trial by neural network machine learning

Oba, Masaki

no journal, , 

Emission spectrum analysis was attempted by neural network machine learning. As a result, it was shown that the composition ratio can be analyzed with an error of several percent.

Oral presentation

Machine learning for activity evaluation of crush zones using chemical composition; Introduction of examples

Shimada, Koji; Tateishi, Ryo*

no journal, , 

We need a method to assess the activity of crush zones alternative to the application of overlying sediments, because the zones encountered in underground tunnels and boring have unknown extensions to the ground surface. The method to be developed is one in which the result is objective and independent of the person, which helps professional judgment. In addition, implementation, dissemination and verification must be executable by a general geological engineer. In light of these goals, the whole-rock chemical composition of the fault gouge along a principal slip zone of the crush zone is attractive. Is there any difference in the chemical composition of fault rocks between active and inactive faults? We thought that the utilization of multivariate analysis could be a solution. Therefore, we collected literature values of the chemical composition of fault gouges for faults with known activities, and began searching for a primary equation that distinguishes active from non-active faults by multivariate analysis in 2018. The results of studies on granitic rocks show that there are multiple discriminants that separate active and non-active faults with a discrimination rate of 100%. In the presentation, we would like to introduce the current status of initiatives, including past case studies.

Oral presentation

Spectral analysis method trial by machine learning

Oba, Masaki; Miyabe, Masabumi; Akaoka, Katsuaki; Wakaida, Ikuo

no journal, , 

Emission spectrum analysis was attempted by neural network machine learning. As a result, it was shown that the composition ratio can be analyzed with an error of several percent.

Oral presentation

Developing data driven analysis methods for extreme scale numerical simulations

Asahi, Yuichi; Maeyama, Shinya*; Bigot, J.*; Garbet, X.*; Grandgirard, V.*; Fujii, Keisuke*; Shimokawabe, Takashi*; Watanabe, Tomohiko*; Idomura, Yasuhiro; Onodera, Naoyuki; et al.

no journal, , 

We have established an in-situ data analysis method for large scale fluid simulation data and developed deep learning based surrogate models to predict fluid simulation results. Firstly, we have developed an in-situ data processing approach, which loosely couples the MPI application and python scripts. It has been shown that this approach is simple and efficient which offers the speedup of 2.7 compared to post hoc data processing. Secondly, we have developed a deep learning model for predicting multiresolution steady flow fields. The deep learning model can give reasonably accurate predictions of simulation results with orders of magnitude faster compared to simulations.

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