Work

GPU-enabled ensemble data assimilation for mesh-refined lattice Boltzmann method

Year: 2024

Type: article

Abstract: \nWe 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.\n

Source: Springer Link (Chiba Institute of Technology)

DOI: 10.1051/epjconf/202430203005/pdf

Authors: Yuta Hasegawa, Yasuhiro Idomura, Naoyuki Onodera

Institution: Japan Atomic Energy Agency

Language: English

Topic: Lattice Boltzmann Simulation Studies

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: Japan Society for the Promotion of Science, Japan Atomic Energy Agency

Awards: G3978147871, G6530602596, G6856085930, Development of data assimilation method for urban wind LES with higher resolution than that of measurement