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Hasegawa, Yuta; Onodera, Naoyuki; Idomura, Yasuhiro
Dai-34-Kai Suchi Ryutai Rikigaku Shimpojiumu Koen Rombunshu (Internet), 3 Pages, 2020/12
We developed a real-time ensemble simulation code for analyzing urban wind conditions and plume dispersion using a locally mesh-refined lattice Boltzmann method. We validated the developed code against the wind tunnel experiment by AIST, and against the field experiment JU2003 in Oklahoma City. In the case of the wind tunnel experiment, the wind condition showed a good agreement with the experiment, and 61.2% of the tracer gas concentration data observed on the ground satisfied the FACTOR2 condition, that is an accuracy criterion given by the environmental assessment guideline. In the case of the field experiment JU2003, the instantaneous wind speed showed a good agreement with the experiment, while the wind direction showed a difference up to 100. The means of the tracer gas concentration satisfied the FACTOR2 condition at all observation interval. These results demonstrate that the developed code is accurate enough for the environmental assessment.
Onodera, Naoyuki
no journal, ,
The plume dispersion simulations is very important from the viewpoint of nuclear security, and it is also required to satisfy both calculation speed and accuracy. GPU accelerated CFD method enables both high-resolution and real-time wind simulations in urban areas. In this presentation, we applied a communication reduced multi-time-step algorithm to AMR based CityLBM, and we realized a real-time plume dispersion simulation on GPU supercomputers.
Hasegawa, Yuta; Onodera, Naoyuki; Idomura, Yasuhiro
no journal, ,
Towards the prediction of the plume dispersion in the urban area, we are developing the urban wind simulation code CityLBM which is based on the lattice Boltzmann method with GPU computing. In this presentation, we implemented the ensemble simulation with MPI parallelization, and estimated the uncertainty of the plume dispersion simulation. The ensemble simulation with 100 members was performed, and the dependency of the simulation against the number of ensemble members was tested. We confirmed that the ensemble simulation can be statistically assessed with the number of ensemble members being 10.