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Yoneyama, Kai; Nitta, Ayako; Tanaka, Yasuyuki; Kodaka, Noriyasu; Kikuchi, Riku; Sakano, Takuma; Furuse, Takahiro; Sato, Soichi; Sambongi, Mitsuru; Tanaka, Kosuke
JAEA-Technology 2025-008, 44 Pages, 2025/12
JAEA-Technology-2025-008.pdf:4.3MB
At the TEPCO's Fukushima Daiichi Nuclear Power Station (1F), an investigation inside the reactors has been carried out. In order to safely carry out the decommissioning work such as fuel debris retrieval and building demolition, it is important to estimate the contamination in primary containment vessel for control the decommissioning planning and the worker radiation exposure levels. Therefore, the analysis of the deposit inside the penetration for the 1F Unit 2 primary containment vessel ("X-6 penetration") was performed to clarify the components and activity. The smears from the deposit were used for the analysis. Non-destructive analysis such as gamma-ray spectrometry, X-ray Fluorescence (XRF) and Scanning Electron Microscope-Energy dispersive X-ray spectroscopy (SEM-EDX) for the smear-samples were performed to determine the gamma-nuclides and the morphology of elements in the deposit. Furthermore, in order to evaluate the nuclides and nuclide composition of the deposit in detail, the smear-samples were dissolved and the quantitative analysis of gamma-nuclides, Sr-90, alpha-nuclides in the dissolved solution were conducted. The results (non-destructive analysis and quantitative analysis) were compared with the results of samples collected at different locations in the X-6 penetration in 2020. In the gamma-ray spectrometry as non-destructive analysis where the smears were analyzed directly, Co-60, Sb-125, Cs-134, Cs-137, Eu-154, Eu-155 and Am-241 were detected. In XRF results, Fe originating from construction material was detected as a major element and small amount of U and Zr originating from the fuel and fuel cladding were also detected. In SEM-EDX results, O and Fe were found as a major element of the deposit and U particles coexisting with Fe, Si, Cr, Ni and Zr were also found. These results were consistent with the SEM-EDX results of the samples collected in 2020. In radioactivity analysis, quantitative values for gamma-nuclides (Co-60, Sb-125, Cs-134, Cs-137, Eu-154, Eu-155), Sr-90, Pu-238, Pu-239+240, Am-241, Cm-244, U-235 and U-238 were obtained. Using the results, the ratios of radioactivity based on Cs-137 and U-238 were calculated. Both sets of the ratios were compared to the calculated value of the Unit 2 fuel composition from ORIGEN.
Tanaka, Yasuyuki; Nitta, Ayako; Ota, Yuki; Kikuchi, Riku; Iwai, Tenga; Kodaka, Noriyasu; Aita, Rena; Sakano, Takuma; Furuse, Takahiro; Tanaka, Kosuke
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no abstracts in English
Tanaka, Yasuyuki; Ota, Yuki; Kodaka, Noriyasu; Kikuchi, Riku; Banjarnahor, I. M.; Do, V. K.; Furuse, Takahiro; Tanaka, Kosuke; Watanabe, Masahisa
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no abstracts in English
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radiation sources discrimination software using a 2d-imaging radiation detectorLaffolley, H.; Tsubota, Yoichi; Tsuji, Tomoya; Honda, Fumiya; Nitta, Ayako; Kikuchi, Riku
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In support of the decommissioning efforts at the Fukushima Daiichi Nuclear Power Station, the Japan Atomic Energy Agency (JAEA) is developing the TRACE software for the accurate discrimination of alpha, beta, and gamma radiation sources in radioactive samples. This new technology utilizes the MiniPIX, a commercial 2D imaging semiconductor radiation sensor. The MiniPIX provides both a spatial image of radiation interactions within its pixel matrix and quantification of the energy transferred. The TRACE software analyzes the data from the MiniPIX by examining the energy and shape of pixel clusters created by radiation events. It identifies alpha particles primarily through the higher energy transferred in their interactions. For other events, it extracts shape parameters: gamma particles, having a quasi-straight path, create very small pixel clusters, while beta particles, being more deviated, generate snake-like clusters. The software can also provide an energy spectrum split by radiation type to assist in identifying radioisotopes. A core component of TRACE is a machine learning algorithm, trained using data acquired from standard sources such as Cobalt-60, Strontium-90, Cesium-137, and Americium-241, chosen for their relevance to Fukushima samples. Planned future integration with an XYZ motorized stage aims to automate data acquisition, improve efficiency, facilitate handling of radioactive and non-flat samples, and enable real-time adjustment of acquisition duration.
Furuse, Takahiro; Tanaka, Yasuyuki; Nitta, Ayako; Kikuchi, Riku; Iwai, Tenga; Kodaka, Noriyasu; Aita, Rena; Sakano, Takuma; Sato, Soichi; Watanabe, Masahisa; et al.
no journal, ,
no abstracts in English