Continuous data assimilation of large eddy simulation by lattice Boltzmann method and local ensemble transform Kalman filter (LBM-LETKF)

TL;DR

This paper demonstrates that the local ensemble transform Kalman filter (LETKF) provides robust and accurate data assimilation for large eddy simulations based on the lattice Boltzmann method, even with sparse and noisy observations.

Contribution

The study introduces the application of LETKF to LBM-based large eddy simulations, showing its advantages over nudging in accuracy and stability with sparse, noisy data.

Findings

LETKF reduces velocity error below observation noise with minimal observation points.

LETKF maintains energy spectrum amplitude better than nudging.

Increasing ensemble size suppresses filter divergence and improves stability.

Abstract

We investigate the applicability of the data assimilation (DA) to large eddy simulations (LESs) based on the lattice Boltzmann method (LBM). We carry out the observing system simulation experiment of a two-dimensional (2D) forced isotropic turbulence, and examine the DA accuracy of the nudging and the local ensemble transform Kalman filter (LETKF) with spatially sparse and noisy observation data of flow fields. The advantage of the LETKF is that it does not require computing spatial interpolation and/or an inverse problem between the macroscopic variables (the density and the pressure) and the velocity distribution function of the LBM, while the nudging introduces additional models for them. The numerical experiments with $256\times256$ grids and $10\%$ observation noise in the velocity showed that the root mean square error of the velocity in the LETKF with $8\times 8$ observation points ($\sim 0.1\%$ of the total grids) and 64 ensemble members becomes smaller than the observation noise, while the nudging requires an order of magnitude larger number of observation points to achieve the same accuracy. Another advantage of the LETKF is that it well keeps the amplitude of the energy spectrum, while only the phase error becomes larger with more sparse observation. We also see that a lack of observation data in the LETKF produces a spurious energy injection in high wavenumber regimes, leading to numerical instability. Such numerical instability is known as the catastrophic filter divergence problem, which can be suppressed by increasing the number of ensemble members. From these results, it was shown that the LETKF enables robust and accurate DA for the 2D LBM with sparse and noisy observation data.