https://doi.org/10.1051/epjconf/202430203005
GPU-enabled ensemble data assimilation for mesh-refined lattice Boltzmann method
Japan Atomic Energy Agency Center for Computational Science and e-Systems, Japan
* e-mail: hasegawa.yuta@jaea.go.jp
Published online: 15 October 2024
We implemented the ensemble data assimilation (DA) method, the local ensemble transform Kalman filter (LETKF), into the mesh-refined lattice Boltzmann method (LBM) for turbulent flows. Both the LETKF and the mesh-refined LBM were fully implemented on GPUs, so that they are efficiently computed on modern GPU-based supercomputers. We examined the DA accuracy against the flow around a cylinder. The result showed that our method enabled accurate DA with spatially- and temporarily-sparse observation data; the error of the assimilated velocity field with the observation interval of τK/2 and the observation resolution D/16 (1.56% of the total computational grids) was smaller than the amplitude of the observation noise, where τK is the period of the Kármán vortex and D is diameter of the square cylinder.
© The Authors, published by EDP Sciences, 2024
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