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大平 早希; 飯田 芳久
Proceedings of Waste Management Symposia 2023 (WM2023) (Internet), 10 Pages, 2023/02
ニオブ-94(Nb-94)の鉱物への収着分配係数(
d)は、放射性廃棄物処分の安全評価において重要なパラメータの一つである。先行研究で、アルカリ条件下におけるNbのdは、Caの存在下で、Naの存在下よりも2桁高い値が報告されていた。本研究では、粘土鉱物へのNb収着に対するCaの影響を再検討するためにNb収着実験を行い、沈殿生成の有無を確認するためにブランクテストを行った。その結果、モンモリロナイトとイライトへのNb収着は、Ca濃度には依存せず、Ca存在下で得られたd値はCa非存在下での値と同じであることが分かった。鉱物表面での錯形成による収着を仮定した収着モデルを構築し、地球化学計算コードを用いて計算を行った。その結果、表面種X_ONb(OH)
とX_ONb(OH)
を用いたモデルにより、得られたデータの傾向を再現可能なことを確認した。
Shi, W.*; 町田 昌彦; 山田 進; 吉田 亨*; 長谷川 幸弘*; 岡本 孝司*
Proceedings of Waste Management Symposia 2023 (WM2023) (Internet), 8 Pages, 2023/02
Clarifying hot spots of radioactive sources inside reactor building rooms based on monitoring air dose rates is one of the most essential steps in decommissioning of nuclear power plants. However, the attempt is regarded as a rather difficult task, because information obtained by air dose rate measurements is generally not enough to inversely estimate contaminated distribution among a tremendous number of potential distributions inside complex reactor building rooms as far as one uses the conventional ways. Then, in order to successfully perform the inverse estimations on source distributions even in such ill-posed circumstances, we suggest that a machine learning method, least absolute shrinkage and selection operator (LASSO) is a promising scheme. Subsequently, we construct a simple room model and employ Monte Carlo simulation code, Particle and Heavy Ion Transport Systems (PHITS) to numerically test feasibility of LASSO inverse estimation scheme. Consequently, we confirm high reconstruction performance of the LASSO scheme in successfully predicting radioactive source distributions. In addition, we carry out uncertainty analysis for the inverse estimation and derive an error function describing uncertainty of the inverse estimation as a useful error estimator. Finally, we find that additional use of spectral information in the measurements can significantly decrease the number of measurement points for the present inverse estimation. In conclusion, LASSO scheme is a quite useful way to explore radioactive hot spots toward the future decommissioning of nuclear power plants.
町田 昌彦; Shi, W.*; 山田 進; 宮村 浩子; 吉田 亨*; 長谷川 幸弘*; 岡本 孝司; 青木 勇斗; 伊藤 倫太郎; 山口 隆司; et al.
Proceedings of Waste Management Symposia 2023 (WM2023) (Internet), 11 Pages, 2023/02
In order to find radioactive hot spots inside reactor building rooms from structural data together with air dose rate measurement data, Least Absolute Shrinkage and Selection Operator (LASSO) has been recently suggested as a promising scheme. The scheme has been examined in simplified room models and its high estimation feasibility has been confirmed by employing Particle and Heavy Ion Transport code System (PHITS) as a radiation simulation code. In this paper, we apply the scheme to complex room models inside real reactor buildings. The target rooms are pool canal circulation system room and main circulation system room in Japan Materials Testing Reactor (JMTR) at Oarai area, Japan Atomic Energy Agency (JAEA). In these real rooms, we create STL format structural data based on Computer Aided Design (CAD) models made directly from their point group data measured by laser scanning devices, and we notice that the total number of their surface meshes in these real rooms reaches to the order of 1 million. Then, this order of the mesh number clearly indicates that one needs a simplified radiation simulation code considering only direct transmission of gamma ray as a radiation calculation instead of PHITS demanding high computational costs. By developing such a simplified code and customizing it to perform LASSO scheme, we consequently confirm that LASSO scheme driven by the simplified simulation can also successfully predict unknown radioactive hot spots on real structural models.
青木 勇斗; 伊藤 倫太郎; 北村 哲浩; 町田 昌彦; 鈴木 政浩; 大森 崇純; 谷口 達郎; 井手 広史
no journal, ,
Prior to the full-scale implementation of fuel debris removal operations in decommissioning of Fukushima Daiichi Nuclear Power Plants (1F), it is crucial to improve the on-site environment in order to safely and efficiently construct access routes inside reactor buildings. Among various improvement operations, it is one of the most essential activities to identify distributions of scattered radioactive sources based on the on-site structural data and air dose rate measurement data. In order to meet the demands, we now develop a prototype system enabling to inversely predict radioactive source distributions from air dose rate measurement data and structural data. In order to evaluate this system, a blind test was conducted in a real reactor building, JMTR in JAEA Oarai area. The test results reveal that if we cooperatively use two structural datasets, i.e., detailed and simplified structural datasets, then we can successfully reproduce radioactive source distributions and air dose rate fields inside the target room. These results suggest that the prototype system is promising in the future decommissioning in 1F.
鈴木 政浩; 青木 勇斗; 町田 昌彦; 伊藤 倫太郎; 川端 邦明; 山口 隆司; 岡本 孝司
no journal, ,
In this paper, we overview the R&D project and present key results obtained since last fiscal year. Particularly, we show that the core scheme of the protype system, i.e., LASSO scheme to inversely estimate hot spots actually works in Pool-canal circular operation (PCO) room of JMTR selected as a test field prior to applying it to 1F. In addition to the scheme verification, the present status of measurement technique developments is also presented. Their all results suggest that the project is successful, and furthermore, it is expected that this system is applicable to the future operations to improve the radiation environments inside 1F buildings.
鈴木 政浩; 町田 昌彦; 伊藤 倫太郎; 川端 邦明; 山口 隆司; 岡本 孝司
no journal, ,
福島第一原子力発電所の廃炉措置において、1号機から3号機の現場では高線量エリアが多々あることから、安全に効率的な作業を進めるには、まずは現場の空間線量率低減が必須である。原子力機構では、今回その解決策として、LASSO法を用いた逆解析手法を原子力現場に適用したプロトタイプシステムを開発している。限定された空間線量率の計測点から線源の特定を可能とし、特定された線源分布から空間線量率の算出・推定ができるものである。また、その線源対策として遮へい、除染を行った際、線量率変化を可視化で理解できるように最新のVR等の可視化媒体により作業現場の状況が分かるようなシステム開発を進めている。本報告では、本プロジェクトの全体概要と主要な成果を紹介する。