Turbulent Injection assisted by Diffusion Models for Scale Resolving Simulations

TL;DR

This paper introduces a memory-efficient diffusion model-based method for injecting turbulent inflow conditions into LES and DNS, accurately reproducing turbulence statistics and detailed velocity fluctuations for various flow problems.

Contribution

A novel diffusion model approach for turbulence injection that is memory-efficient and maintains physical realism without increasing development distance.

Findings

Accurately reproduces turbulence energy spectrum and autocorrelation functions.

Generates detailed instantaneous velocity fields with realistic fluctuations.

Maintains physical representativeness without increasing development distance.

Abstract

The present research proposes a new memory-efficient method using diffusion models to inject turbulent inflow conditions into Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) for various flow problems. A guided diffusion model was trained on Decaying Homogeneous Isotropic Turbulence (DHIT) samples, characterized by different turbulent kinetic energy levels and integral length scales. Samples generated by the diffusion model accurately reproduce turbulence statistics, such as the energy spectrum and the two-point autocorrelation functions, while preserving the ability to generate instantaneous three-dimensional velocity fields with detailed fluctuations. Physical representativeness is also evaluated by injecting the \textit{synthetic} samples into a free domain (i.e., without any wall boundary) through an inlet boundary condition. The method demonstrates promising results regarding energy spectrum, spatial correlation, turbulent kinetic energy level, and integral length scales without increasing the development distance as compared to a library-based method. The following article has been submitted to/accepted by Physics of Fluid (AIP Publishing LLC). After publication, it will be available https://doi.org/10.1063/5.0278541.