A new data-driven energy-stable Evolve-Filter-Relax model for turbulent flow simulation

TL;DR

This paper introduces a data-driven, energy-stable Evolve-Filter-Relax model for turbulent flow simulation that optimizes filtering based on DNS data, improving accuracy and efficiency over traditional models.

Contribution

It proposes a novel data-driven filter optimization within the EFR framework, enhancing turbulence simulation accuracy and computational efficiency.

Findings

Learned filter outperforms standard differential filter and Smagorinsky model

Significantly improves energy spectra and energy/enstrophy evolution

Enforces stability and reduces numerical wiggles in simulations

Abstract

We present a novel approach to define the filter and relax steps in the evolve-filter-relax (EFR) framework for simulating turbulent flows. The EFR main advantages are its ease of implementation and computational efficiency. However, as it only contains two parameters (one for the filter step and one for the relax step) its flexibility is rather limited. In this work, we propose a data-driven approach in which the optimal filter is found based on DNS data in the frequency domain. The optimization step is computationally efficient and only involves one-dimensional least-squares problems for each wavenumber. Across both decaying turbulence and Kolmogorov flow, our learned filter decisively outperforms the standard differential filter and the Smagorinsky model, yielding significantly improved accuracy in energy spectra and in the temporal evolution of both energy and enstrophy. In addition, the relax parameter is determined by requiring energy and/or enstrophy conservation, which enforces stability of the method and reduces the appearance of numerical wiggles, especially when the filter is built in scarce data regimes. Applying the learned filter is also more computationally efficient compared to traditional differential filters, as it circumvents solving a linear system.