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Yamada, Susumu; Yoshida, Toru*; Hasegawa, Yukihiro*; Machida, Masahiko
Proceedings of Waste Management Symposia 2024 (WM2024) (Internet), 15 Pages, 2024/03
In order to safely carry out the decommission of reactor buildings, it is extremely important to identify the radiation source distribution. It has been reported that when the structural model of the building is constructed by uniform cells, the source distribution can be estimated from the measured air dose rates by minimizing an evaluation function using the Least Absolute Shrinkage and Selection Operator (LASSO). Moreover, if cells are non-uniform, we can estimate the distribution using the fused LASSO which minimizes the evaluation function that takes account of the connectivity between the adjacent cells. However, when a group of some cells is considered disconnected from the surrounding ones due to the precision of the measured structural data, the concentration of the group can be singularly high. Therefore, in order to avoid the problem, we propose a new evaluation function that can prevent the singularity. We estimated the distribution for the test model using the proposed evaluation function and confirmed the validity of the function. Moreover, we succeeded in estimating the source distribution in the pool canal circulation system room in JMTR in the Japan Atomic Energy Agency by the fused LASSO for the new function more accurately than previous analysis.
Machida, Masahiko; Yamada, Susumu; Kim, M.; Okumura, Masahiko; Miyamura, Hiroko; Shikaze, Yoshiaki; Sato, Tomoki*; Numata, Yoshiaki*; Tobita, Yasuhiro*; Yamaguchi, Takashi; et al.
RIST News, (69), p.2 - 18, 2023/09
The contamination of radioactive materials leaked from the reactor has resulted in numerous hot spots in the Fukushima Daiichi Nuclear Power Station (1F) building, posing obstacles to its decommissioning. In order to solve this problem, JAEA has conducted research and development of the digital technique for inverse estimation of radiation source distribution and countermeasures against the estimated source in virtual space for two years from 2021 based on the subsidy program "Project of Decommissioning and Contaminated Water Management" performed by the funds from the Ministry of Economy, Trade and Industry. In this article, we introduce the results of the project and the plan of the renewal project started in April 2023. For the former project, we report the derivative method for LASSO method considering the complex structure inside the building and the character of the source and show the result of the inverse estimation using the method in the real reactor building. Moreover, we explain the platform software "3D-ADRES-Indoor" which integrates these achievements. Finally, we introduce the plan of the latter project.
Shi, W.*; Machida, Masahiko; Yamada, Susumu; Yoshida, Toru*; Hasegawa, Yukihiro*; Okamoto, Koji*
Progress in Nuclear Energy, 162, p.104792_1 - 104792_19, 2023/08
Times Cited Count:0 Percentile:0.01(Nuclear Science & Technology)Predicting radioactive source distributions inside reactor building rooms based on monitoring air dose rates is one of the most essential steps towards decommissioning of nuclear power plants. However, the attempt is rather a difficult task, because it can be generally mapped onto mathematically ill-posed problem. Then, in order to successfully perform the inverse estimations on radioactive source distributions even in such ill-posed conditions, we suggest that a machine learning method, least absolute shrinkage and selection operator (LASSO) minimizing the loss function, is a promising scheme. For the purpose of its feasibility demonstrations in real building rooms, we employ PHITS code to make LASSO input as the above matrix C connecting the radioactive source vector P defined on surface meshes of structural materials with the air dose rate vector Q measured at internal positions inside the rooms. We develop a mathematical criterion on the number of monitoring points to correctly predict source distributions based on the theory of Candes and Tao. Then, we confirm that LASSO actually shows extremely high possibility for source distribution reconstructions as far as the number of detection points satisfies our criterion. Moreover, we verify that radioactive hot spots can be truly reconstructed in an experiment setup. At last, we examine an influence factor like detector-source distance to enhance the predicting possibility in the inverse estimation. From the above demonstrations, we propose that LASSO scheme is a quite useful way to explore hot spots as seen in damaged nuclear power plants like Fukushima Daiichi nuclear power plants.
Shi, W.*; Machida, Masahiko; Yamada, Susumu; Yoshida, Toru*; Hasegawa, Yukihiro*; Okamoto, Koji*
Annals of Nuclear Energy, 184, p.109686_1 - 109686_12, 2023/05
Times Cited Count:1 Percentile:68.31(Nuclear Science & Technology)Shi, W.*; Machida, Masahiko; Yamada, Susumu; Yoshida, Toru*; Hasegawa, Yukihiro*; Okamoto, Koji*
Proceedings of Waste Management Symposia 2023 (WM2023) (Internet), 8 Pages, 2023/02
Machida, Masahiko; Shi, W.*; Yamada, Susumu; Miyamura, Hiroko; Yoshida, Toru*; Hasegawa, Yukihiro*; Okamoto, Koji; Aoki, Yuto; Ito, Rintaro; Yamaguchi, Takashi; et al.
Proceedings of Waste Management Symposia 2023 (WM2023) (Internet), 11 Pages, 2023/02
Nanamura, Takuya; Fujita, Manami; Hasegawa, Shoichi; Ichikawa, Masaya; Ichikawa, Yudai; Imai, Kenichi*; Naruki, Megumi; Sato, Susumu; Sako, Hiroyuki; Tamura, Hirokazu; et al.
Progress of Theoretical and Experimental Physics (Internet), 2022(9), p.093D01_1 - 093D01_35, 2022/09
Times Cited Count:6 Percentile:71.82(Physics, Multidisciplinary)Miwa, Koji*; Fujita, Manami; Hasegawa, Shoichi; Hosomi, Kenji; Ichikawa, Yudai; Imai, Kenichi*; Nanamura, Takuya; Naruki, Megumi; Sako, Hiroyuki; Sato, Susumu; et al.
Physical Review C, 104(4), p.045204_1 - 045204_20, 2021/10
Times Cited Count:13 Percentile:88.31(Physics, Nuclear)Acharya, U. A.*; Hasegawa, Shoichi; Imai, Kenichi*; Sako, Hiroyuki; Sato, Susumu; Tanida, Kiyoshi; PHENIX Collaboration*; 306 of others*
Physical Review Letters, 127(16), p.162001_1 - 162001_8, 2021/10
Times Cited Count:8 Percentile:71.38(Physics, Multidisciplinary)Yoshida, Junya; Akaishi, Takaya; Fujita, Manami; Hasegawa, Shoichi; Hashimoto, Tadashi; Hosomi, Kenji; Ichikawa, Masaya; Ichikawa, Yudai; Imai, Kenichi*; Kim, S.; et al.
JPS Conference Proceedings (Internet), 33, p.011112_1 - 011112_8, 2021/03
Sakao, Tamao*; Fujita, Manami; Hasegawa, Shoichi; Hosomi, Kenji; Ichikawa, Masaya; Ichikawa, Yudai; Imai, Kenichi*; Nanamura, Takuya; Naruki, Megumi; Sako, Hiroyuki; et al.
JPS Conference Proceedings (Internet), 33, p.011133_1 - 011133_6, 2021/03
Acharya, U. A.*; Hasegawa, Shoichi; Imai, Kenichi*; Sako, Hiroyuki; Sato, Susumu; Tanida, Kiyoshi; PHENIX Collaboration*; 309 of others*
Physical Review D, 103(5), p.052009_1 - 052009_10, 2021/03
Times Cited Count:5 Percentile:43.93(Astronomy & Astrophysics)Acharya, U. A.*; Hasegawa, Shoichi; Imai, Kenichi*; Sako, Hiroyuki; Sato, Susumu; Tanida, Kiyoshi; PHENIX Collaboration*; 308 of others*
Physical Review D, 103(3), p.032007_1 - 032007_8, 2021/02
Times Cited Count:2 Percentile:19.82(Astronomy & Astrophysics)Hayakawa, Shuhei; Fujita, Manami; Hasegawa, Shoichi; Hashimoto, Tadashi; Hosomi, Kenji; Ichikawa, Yudai; Imai, Kenichi*; Nanamura, Takuya; Naruki, Megumi; Sako, Hiroyuki; et al.
Physical Review Letters, 126(6), p.062501_1 - 062501_6, 2021/02
Times Cited Count:34 Percentile:95.02(Physics, Multidisciplinary)Onodera, Naoyuki; Idomura, Yasuhiro; Hasegawa, Yuta; Yamashita, Susumu; Shimokawabe, Takashi*; Aoki, Takayuki*
Proceedings of International Conference on High Performance Computing in Asia-Pacific Region (HPC Asia 2021) (Internet), p.120 - 128, 2021/01
Times Cited Count:0 Percentile:0.01(Computer Science, Hardware & Architecture)We develop a multigrid preconditioned conjugate gradient (MG-CG) solver for the pressure Poisson equation in a two-phase flow CFD code JUPITER. The MG preconditioner is constructed based on the geometric MG method with a three-stage V-cycle, and a RB-SOR smoother and its variant with cache-reuse optimization (CR-SOR) are applied at each stage. The numerical experiments are conducted for two-phase flows in a fuel bundle of a nuclear reactor. The MG-CG solvers with the RB-SOR and CR-SOR smoothers reduce the number of iterations to less than 15% and 9% of the original preconditioned CG method, leading to 3.1- and 5.9-times speedups, respectively. The obtained performance indicates that the MG-CG solver designed for the block-structured grid is highly efficient and enables large-scale simulations of two-phase flows on GPU based supercomputers.
Gogami, Toshiyuki*; Fujita, Manami; Hasegawa, Shoichi; Hosomi, Kenji; Imai, Kenichi*; Ichikawa, Yudai; Nanamura, Takuya; Naruki, Megumi; Sako, Hiroyuki; Sato, Susumu; et al.
Journal of Physics; Conference Series, 1643, p.012133_1 - 012133_6, 2020/12
Times Cited Count:2 Percentile:84.36(Astronomy & Astrophysics)Miwa, Koji*; Fujita, Manami; Hasegawa, Shoichi; Hosomi, Kenji; Ichikawa, Masaya; Ichikawa, Yudai; Imai, Kenichi*; Nanamura, Takuya; Naruki, Megumi; Sako, Hiroyuki; et al.
Journal of Physics; Conference Series, 1643, p.012174_1 - 012174_6, 2020/12
Times Cited Count:2 Percentile:84.36(Astronomy & Astrophysics)Acharya, U.*; Hasegawa, Shoichi; Imai, Kenichi*; Nagamiya, Shoji*; Sako, Hiroyuki; Sato, Susumu; Tanida, Kiyoshi; PHENIX Collaboration*; 397 of others*
Physical Review C, 102(6), p.064905_1 - 064905_13, 2020/12
Times Cited Count:5 Percentile:51.79(Physics, Nuclear)Acharya, U.*; Hasegawa, Shoichi; Imai, Kenichi*; Nagamiya, Shoji*; Sako, Hiroyuki; Sato, Susumu; Tanida, Kiyoshi; PHENIX Collaboration*; 572 of others*
Physical Review C, 102(5), p.054910_1 - 054910_11, 2020/11
Times Cited Count:3 Percentile:35.72(Physics, Nuclear)Acharya, U.*; Hasegawa, Shoichi; Imai, Kenichi*; Nagamiya, Shoji*; Sako, Hiroyuki; Sato, Susumu; Tanida, Kiyoshi; PHENIX Collaboration*; 344 of others*
Physical Review D, 102(9), p.092002_1 - 092002_14, 2020/11
Times Cited Count:0 Percentile:0.01(Astronomy & Astrophysics)