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Hasegawa, Yuta; Onodera, Naoyuki; Asahi, Yuichi; Idomura, Yasuhiro
Dai-36-Kai Suchi Ryutai Rikigaku Shimpojiumu Koen Rombunshu (Internet), 5 Pages, 2022/12
This study implemented and tested the ensemble data assimilation (DA) of turbulent flows using the lattice Boltzmann method and the local ensemble transform Kalman filter (LBM-LETKF). The computational code was implemented fully on GPUs. The test was carried out for the 3D turbulent flow around a square cylinder with 
meshes and 32 ensemble members using 32 GPUs. The time interval of the DA in the test was a half of the period of the Kalman vortex shedding. The normalized mean absolute errors (NMAE) of the lift coefficient were 132%, 148%, and 13.2% for the non-DA case, the nudging case (a simpler DA algorithm), and the LETKF case, respectively. It was found that the LETKF achieved good DA accuracy even though the observation was not frequent enough for the small scale turbulence, while the nudging showed systematic delays in its solution, and could not keep the DA accurately.
Yamamoto, Shinya*; Honda, Makoto*; Sakurai, Hideyuki*; Onoe, Hironori; Masumoto, Kiyoshi*
no journal, ,
no abstracts in English
Hasegawa, Yuta; Idomura, Yasuhiro; Onodera, Naoyuki; Asahi, Yuichi
no journal, ,
The authors are developing a lattice Boltzmann method-local ensemble transformed Kalman filter (LBM-LETKF) to enable the ensemble data assimilation for turbulence with GPUs. The state vector (simulation variables) and observation vector (quantities that can be measured from experiments) have a significant impact on the performance of LETKF: as the state vector, the na
ve method uses a 27-elements vector composed of the LBM velocity distribution functions. However, it is also possible to use the 4-elements vector of macroscopic quantities composed of density and velocity. In this study, we compare the calculation accuracy and speed of the above two methods and select a state vector suitable for turbulence data assimilation.