Development of a prediction model for ambient dose equivalent rate distribution based on ecological half-life profiles using LASSO regression and KURAMA data

Shikaze, Yoshiaki; Saito, Kimiaki; Tanimura, Naoki*; Yoshimura, Kazuya; Liu, X.; Machida, Masahiko

Radiation Protection Dosimetry, 201(15), p.1025 - 1042, 2025/09

https://doi.org/10.1093/rpd/ncaf086

The two-component model, comprising a fast-decay and a slow-decay component, has been widely used to approximate the decreasing trends of air dose rates in contaminated areas surrounding major nuclear accident sites. However, its adequacy is yet to be thoroughly validated. This study analyzed extensive car-borne survey data collected from 2011 to 2016 after the Fukushima Daiichi Nuclear Power Plant accident using the least absolute shrinkage and selection operator regression with a high-degree-of-freedom model. This analysis aimed to evaluate the adequacy of the two-component model and investigate the profiles of ecological half-lives. Next, future predictions of air dose rate distributions were made using a prediction model formula that incorporated the average ecological half-life profiles calculated for each land-use and initial air dose rate category. Prediction accuracy was verified through comparison with integrated map data, which merge air dose rate datasets obtained using different monitoring methods and represent the most currently reliable source. In this paper, we present the results of the analysis of the above environmental half-life profiles and the evaluation of the predictive model calculations, and discuss the reasons that led to these results.

Development of reconstruction method of void distribution for ultrasonic tomography using a machine learning algorithm

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), 5 Pages, 2025/02

A Numerical study on machine-learning-based ultrasound tomography of bubbly two-phase flows

Wada, Yuki; Hirose, Yoshiyasu; Shibamoto, Yasuteru

Ultrasonics, 141, p.107346_1 - 107346_16, 2024/07

https://doi.org/10.1016/j.ultras.2024.107346

Insights into machine-learning modeling for Cr(VI) removal from contaminated water using nano-nickel hydroxide

Maamoun, I.; Rushdi, M.*; Falyouna, O.*; Eljamal, R.*; Eljamal, O.*

Separation and Purification Technology, 308, p.122863_1 - 122863_16, 2023/03

https://doi.org/10.1016/j.seppur.2022.122863

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

AA2021-0588.pdf:1.29MB

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.

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, , 

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.