Dynamic mixed turbulence modeling using a super-resolution generative adversarial approach

TL;DR

This paper introduces a novel data-driven turbulence modeling approach that combines super-resolution GANs with dynamic mixed models to improve the accuracy and physical realism of large-eddy simulations across different flow conditions.

Contribution

The paper presents a new dynamic mixed super-resolution model (DMSRM) that integrates SR-GANs for enhanced turbulence closure modeling in LES, outperforming traditional models in accuracy and robustness.

Findings

DMSRM accurately reconstructs fine-scale turbulence features.

DMSRM outperforms traditional DMM in energy spectrum prediction.

DMSRM produces realistic backscatter and energy dissipation.

Abstract

A dynamic mixed super-resolution model (DMSRM) for large-eddy simulations (LESs) is proposed, which combines the traditional dynamic mixed model (DMM) formulation with the generation of super-resolved velocity fields from which the subfilter-scale (SFS) stress tensor can be computed. A data-driven super-resolution generative adversarial network (SR-GAN) is employed to upsample the grid-filtered velocity fields by a factor of two, enabling the evaluation of both scale-similarity and the dynamic Smagorinsky contributions. A priori analyses of forced homogeneous isotropic turbulence show that the SR-GAN accurately reconstructs fine-scale flow features and generalizes well across different filter sizes and higher Reynolds number flow configurations, even for unseen input fields. The DMSRM reproduces SFS stresses and energy dissipation more accurately than the traditional DMM. A posteriori LES calculations further confirm that DMSRM predicts the energy spectrum and intermittency more accurately than DMM, even for different LES grid-scale resolutions and for higher Reynolds numbers than those used for training. Unlike DMM, DMSRM yields realistic backscatter and physically consistent SFS energy dissipation. These improvements arise from the physically accurate super-resolved fields generated by the SR-GAN, from which SFS stresses are directly computed. The result is a closure that accurately reproduces stress magnitudes and dissipation while reducing reliance on additional dissipation from the dynamic term. The DMSRM formulation achieves a balance of physical fidelity, robustness, and computational efficiency, offering a promising alternative to traditional DMMs for turbulence LES modeling.