Effectiveness of fused LASSO for prediction of distribution of radioactive materials in reactor buildings

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.

Inverse estimation scheme of radioactive source distributions inside building rooms based on monitoring air dose rates using LASSO; Theory and demonstration

Shi, W.*; Machida, Masahiko; Yamada, Susumu; Yoshida, Toru*; Hasegawa, Yukihiro*; Okamoto, Koji*

Progress in Nuclear Energy, 162, p.104792_1 - 104792_19, 2023/08

https://doi.org/10.1016/j.pnucene.2023.104792

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.

LASSO reconstruction scheme to predict radioactive source distributions inside reactor building rooms; Theory & demonstration

Shi, W.*; Machida, Masahiko; Yamada, Susumu; Yoshida, Toru*; Hasegawa, Yukihiro*; Okamoto, Koji*

Proceedings of Waste Management Symposia 2023 (WM2023) (Internet), 8 Pages, 2023/02

Measurement of differential cross sections for elastic scattering in the momentum range 0.44-0.80 GeV/c

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

https://doi.org/10.1093/ptep/ptac101

Probing gluon spin-momentum correlations in transversely polarized protons through midrapidity isolated direct photons in collisions at =200 GeV

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

https://doi.org/10.1103/PhysRevLett.127.162001

Study of identification method by the reaction for a scattering experiment at J-PARC

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

https://doi.org/10.7566/JPSCP.33.011133

Transverse momentum dependent forward neutron single spin asymmetries in transversely polarized collisions at GeV

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

https://doi.org/10.1103/PhysRevD.103.032007

GPU acceleration of multigrid preconditioned conjugate gradient solver on block-structured Cartesian grid

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

https://doi.org/10.1145/3432261.3432273

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.

Study of interaction from the scattering experiment at J-PARC

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

https://doi.org/10.1088/1742-6596/1643/1/012174

Measurement of jet-medium interactions via direct photon-hadron correlations in Au+Au and +Au collisions at GeV

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

https://doi.org/10.1103/PhysRevC.102.054910