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Hasegawa, Yuta; Onodera, Naoyuki; Idomura, Yasuhiro
Keisan Kogaku Koenkai Rombunshu (CD-ROM), 25, 4 Pages, 2020/06
We developed a GPU-based CFD code using a mesh-refined lattice Boltzmann method (LBM), which enables ensemble simulations for wind and plume dispersion in urban cities. The code is tuned for Pascal or Volta GPU architectures, and is able to perform real-time wind simulations with several kilometers square region and several meters of grid resolution. We examined the developed code against the field experiment JU2003 in Oklahoma City. In the comparison, wind conditions showed good agreements, and the ensemble-averaged and maximum values of tracer concentration satisfied the factor 2 agreements.
Onodera, Naoyuki; Idomura, Yasuhiro; Ali, Y.*; Shimokawabe, Takashi*; Aoki, Takayuki*
Keisan Kogaku Koenkai Rombunshu (CD-ROM), 25, 4 Pages, 2020/06
We have developed the stencil-based CFD code JUPITER for simulating three-dimensional multiphase flows. A GPU-accelerated Poisson solver based on the preconditioned conjugate gradient (P-CG) method with a multigrid preconditioner was developed for the JUPITER with block-structured AMR mesh. All Poisson kernels were implemented using CUDA, and the GPU kernel function is well tuned to achieve high performance on GPU supercomputers. The developed multigrid solver shows good convergence of about 1/7 compared with the original P-CG method, and 
3 speed up is achieved with strong scaling test from 8 to 216 GPUs on TSUBAME 3.0.
Doda, Norihiro; Hamase, Erina; Yokoyama, Kenji; Tanaka, Masaaki
Keisan Kogaku Koenkai Rombunshu (CD-ROM), 25, 4 Pages, 2020/06
With the aim of advancing the design optimization in fast reactors, neutronics and thermal-hydraulics coupled analysis method which can consider the temporal change of neutron flux distribution in the core has been developed. A three-dimensional neutronics analysis code and a plant dynamics analysis code are coupled on a platform using Python programing. In this report, outlines of the coupling method of analysis codes, the results of its application to the actual plant under a virtual accidental condition, and the future development is described.
Hatayama, Sora*; Shimokawabe, Takashi*; Onodera, Naoyuki
no journal, ,
Computational fluid dynamics (CFD) is widely used as a fluid analysis technique. However, these have a problem that the calculation cost is very expensive and the execution time for reaching a steady-state is long. To solve this problem, we use convolutional neural networks (CNN), which is one of the deep learning methods, to predict CFD results. In this research, we provide the method and implementation of steady flow prediction using CNN with boundary exchange to predict the CFD results in a large area.