Estimation of continuous distribution of iterated fission probability using an artificial neural network with Monte Carlo-based training data

Tuya, D.; Nagaya, Yasunobu

Journal of Nuclear Engineering (Internet), 4(4), p.691 - 710, 2023/11

https://doi.org/10.3390/jne4040043

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.

New approach to understanding the experimental Cs NMR chemical shift of clay minerals via machine learning and DFT-GIPAW calculations

Okubo, Takahiro*; Takei, Akihiro*; Tachi, Yukio; Fukatsu, Yuta; Deguchi, Kenzo*; Oki, Shinobu*; Shimizu, Tadashi*

Journal of Physical Chemistry A, 127(4), p.973 - 986, 2023/02

https://doi.org/10.1021/acs.jpca.2c08880

The identification of adsorption sites of Cs on clay minerals has been studied in the fields of environmental chemistry. The nuclear magnetic resonance (NMR) experiments allow direct observations of the local structures of adsorbed Cs. The NMR parameters of Cs, derived from solid-state NMR experiments, are sensitive to the local neighboring structures of adsorbed Cs. However, determining the Cs positions from NMR data alone is difficult. This paper describes an approach for identifying the expected atomic positions of Cs adsorbed on clay minerals by combining machine learning (ML) with experimentally observed chemical shifts. A linear ridge regression model for ML is constructed from the smooth overlap of atomic positions descriptor and gauge-including projector augmented wave (GIPAW) ab initio data. The Cs chemical shifts can be instantaneously calculated from the Cs positions on any clay layers using ML. The inverse analysis from the ML model can derive the atomic positions from experimentally observed chemical shifts.

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

https://doi.org/10.1016/j.ress.2022.108503

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.

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.

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 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.

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.

Prediction of the operating control rod position of the HTTR with supervised machine learning

Ho, H. Q.; Nagasumi, Satoru; Shimazaki, Yosuke; Hamamoto, Shimpei; Iigaki, Kazuhiko; Goto, Minoru; Simanullang, I. L.*; Fujimoto, Nozomu*; Ishitsuka, Etsuo

no journal, ,