Ensemble-variational assimilation of statistical data in large eddy simulation

TL;DR

This paper introduces a non-intrusive ensemble-variational data assimilation method to improve large-eddy simulation predictions of turbulent flows by adjusting subgrid models to match reference statistics.

Contribution

It develops a novel EnVar-based framework for data assimilation in LES, effectively tuning subgrid models to enhance statistical accuracy and robustness across conditions.

Findings

Outperforms baseline subgrid models like dynamic and mixed models.

Accurately recovers flow statistics in turbulent channel flow.

Provides uncertainty quantification of the assimilated model.

Abstract

A non-intrusive data assimilation methodology is developed to improve the statistical predictions of large-eddy simulations (LES). The ensemble-variational (EnVar) approach aims to minimize a cost function that is defined as the discrepancy between LES predictions and reference statistics from experiments or, in the present demonstration, independent direct numerical simulations (DNS). This methodology is applied to adjust the Smagorinsky subgrid model and obtain data assimilated LES (DA-LES) which accurately estimate the statistics of turbulent channel flow. To separately control the mean and fluctuations of the modeled subgrid tensor, and ultimately the first- and second-order flow statistics, two types of model corrections are considered. The first one optimizes the wall-normal profile of the Smagorinsky coefficient, while the second one introduces an adjustable steady forcing in the momentum equations to independently act on the mean flow. Using these two elements, the data assimilation procedure can satisfactorily modify the subgrid model and accurately recover reference flow statistics. The retrieved subgrid model significantly outperforms more elaborate baseline models such as dynamic and mixed models, in a posteriori testing. The robustness of the present data assimilation methodology is assessed by changing the Reynolds number and considering grid resolutions that are away from usual recommendations. Taking advantage of the stochastic formulation of EnVar, the developed framework also provides the uncertainty of the retrieved model.