LASSO reconstruction scheme to predict radioactive source distributions inside reactor building rooms; Practical applications

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

Parameter optimization for generating atmospheric boundary layers by using the locally mesh-refined lattice Boltzmann method

Onodera, Naoyuki; Idomura, Yasuhiro; Hasegawa, Yuta; Shimokawabe, Takashi*; Aoki, Takayuki*

Keisan Kogaku Koenkai Rombunshu (CD-ROM), 27, 4 Pages, 2022/06

We have developed a wind simulation code named CityLBM to realize wind digital twins. Mesoscale wind conditions are given as boundary conditions in CityLBM by using a nudging data assimilation method. It is found that conventional approaches with constant nudging coefficients fail to reproduce turbulent intensity in long time simulations, where atmospheric stability conditions change significantly. We propose a dynamic parameter optimization method for the nudging coefficient based on a particle filter. CityLBM was validated against plume dispersion experiments in the complex urban environment of Oklahoma City. The nudging coefficient was updated to reduce the error of the turbulent intensity between the simulation and the observation, and the atmospheric boundary layer was reproduced throughout the day.

Deep learning approach for an interface structure analysis with a large statistical noise in neutron reflectometry

Aoki, Hiroyuki; Liu, Y.*; Yamashita, Takashi*

Scientific Reports (Internet), 11(1), p.22711_1 - 22711_9, 2021/11

https://doi.org/10.1038/s41598-021-02085-6

Critical slowing-down and field-dependent paramagnetic fluctuations in the skyrmion host EuPtSi; SR and NMR studies

Higa, Nonoka*; Ito, Takashi; Yogi, Mamoru*; Hattori, Taisuke; Sakai, Hironori; Kambe, Shinsaku; Guguchia, Z.*; Higemoto, Wataru; Nakashima, Miho*; Homma, Yoshiya*; et al.

Physical Review B, 104(4), p.045145_1 - 045145_7, 2021/07

https://doi.org/10.1103/PhysRevB.104.045145

Acceleration of locally mesh allocated Poisson solver using mixed precision

Onodera, Naoyuki; Idomura, Yasuhiro; Hasegawa, Yuta; Shimokawabe, Takashi*; Aoki, Takayuki*

Keisan Kogaku Koenkai Rombunshu (CD-ROM), 26, 3 Pages, 2021/05

We develop a mixed-precision preconditioner for the pressure Poisson equation in a two-phase flow CFD code JUPITER-AMR. The multi-grid (MG) preconditioner is constructed based on the geometric MG method with a three- stage V-cycle, and a cache-reuse SOR (CR-SOR) method at each stage. The numerical experiments are conducted for two-phase flows in a fuel bundle of a nuclear reactor. The MG-CG solver in single-precision shows the same convergence histories as double-precision, which is about 75% of the computational time in double-precision. In the strong scaling test, the MG-CG solver in single-precision is accelerated by 1.88 times between 32 and 96 GPUs.

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.

GPU-acceleration of locally mesh allocated two phase flow solver for nuclear reactors

Onodera, Naoyuki; Idomura, Yasuhiro; Ali, Y.*; Yamashita, Susumu; Shimokawabe, Takashi*; Aoki, Takayuki*

Proceedings of Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo 2020 (SNA + MC 2020), p.210 - 215, 2020/10

This paper presents a GPU-based Poisson solver on a block-based adaptive mesh refinement (block-AMR) framework. The block-AMR method is essential for GPU computation and efficient description of the nuclear reactor. In this paper, we successfully implement a conjugate gradient method with a state-of-the-art multi-grid preconditioner (MG-CG) on the block-AMR framework. GPU kernel performance was measured on the GPU-based supercomputer TSUBAME3.0. The bandwidth of a vector-vector sum, a matrix-vector product, and a dot product in the CG kernel gave good performance at about 60% of the peak performance. In the MG kernel, the smoothers in a three-stage V-cycle MG method are implemented using a mixed precision RB-SOR method, which also gave good performance. For a large-scale Poisson problem with cells, the developed MG-CG method reduced the number of iterations to less than 30% and achieved 2.5 speedup compared with the original preconditioned CG method.

Magnetic, thermal, and neutron diffraction studies of a coordination polymer; bis(glycolato)cobalt(II)

Nakane, Tomohiro*; Yoneyama, Shota*; Kodama, Takeshi*; Kikuchi, Koichi*; Nakao, Akiko*; Ohara, Takashi; Higashinaka, Ryuji*; Matsuda, Tatsuma*; Aoki, Yuji*; Fujita, Wataru*

Dalton Transactions (Internet), 48(1), p.333 - 338, 2019/01

https://doi.org/10.1039/C8DT04358B

Materials and Life Science Experimental Facility (MLF) at the Japan Proton Accelerator Research Complex, 2; Neutron scattering instruments

Nakajima, Kenji; Kawakita, Yukinobu; Ito, Shinichi*; Abe, Jun*; Aizawa, Kazuya; Aoki, Hiroyuki; Endo, Hitoshi*; Fujita, Masaki*; Funakoshi, Kenichi*; Gong, W.*; et al.

Quantum Beam Science (Internet), 1(3), p.9_1 - 9_59, 2017/12

https://doi.org/10.3390/qubs1030009

The neutron instruments suite, installed at the spallation neutron source of the Materials and Life Science Experimental Facility (MLF) at the Japan Proton Accelerator Research Complex (J-PARC), is reviewed. MLF has 23 neutron beam ports and 21 instruments are in operation for user programs or are under commissioning. A unique and challenging instrumental suite in MLF has been realized via combination of a high-performance neutron source, optimized for neutron scattering, and unique instruments using cutting-edge technologies. All instruments are/will serve in world-leading investigations in a broad range of fields, from fundamental physics to industrial applications. In this review, overviews, characteristic features, and typical applications of the individual instruments are mentioned.

A Stencil framework to realize large-scale computations beyond device memory capacity on GPU supercomputers

Shimokawabe, Takashi*; Endo, Toshio*; Onodera, Naoyuki; Aoki, Takayuki*

Proceedings of 2017 IEEE International Conference on Cluster Computing (IEEE Cluster 2017) (Internet), p.525 - 529, 2017/09

Stencil-based applications such as CFD have succeeded in obtaining high performance on GPU supercomputers. The problem sizes of these applications are limited by the GPU device memory capacity, which is typically smaller than the host memory. On GPU supercomputers, a locality improvement technique using temporal blocking method with memory swapping between host and device enables large computation beyond the device memory capacity. Our high-productivity stencil framework automatically applies temporal blocking to boundary exchange required for stencil computation and supports automatic memory swapping provided by a MPI/CUDA wrapper library. The framework-based application for the airflow in an urban city maintains 80% performance even with the twice larger than the GPU memory capacity and have demonstrated good weak scalability on the TSUBAME 2.5 supercomputer.