Partially-Averaged Navier-Stokes Simulations of Turbulence Within a High-Order Flux Reconstruction Framework

TL;DR

This study combines high-order flux reconstruction methods with the PANS turbulence model to improve the accuracy of scale-resolving simulations, demonstrating benefits over URDNS and reduced sensitivity to resolution parameters.

Contribution

It develops and analyzes the integration of high-order discretizations with the PANS turbulence model, highlighting improved accuracy and robustness in turbulence simulations.

Findings

High-order discretizations improve statistical and flow physics predictions.

Less sensitivity to resolution-control parameters with high-order methods.

High-order methods offer accuracy gains without significant computational cost.

Abstract

High-order methods and hybrid turbulence models have independently shown promise as means of decreasing the computational cost of scale-resolving simulations. The objective of this work is to develop the combination of these methods and analyze the effects of high-order discretizations on hybrid turbulence models, particularly with respect the optimal model parameters and the relative accuracy benefits compared to approaches such as under-resolved direct numerical simulation (URDNS). We employ the Partially-Averaged Navier-Stokes (PANS) approach using the flux reconstruction scheme on the flow around a periodic hill and the wake flow of a circular cylinder at a Reynolds number of 3900, the latter of which we provide direct numerical simulation results and novel statistical analysis. By increasing the order of the discretization while fixing the total degrees of freedom, it was observed that larger improvements in the prediction of the statistics and flow physics were generally seen with PANS than URDNS. Furthermore, less sensitivity to the resolution-control parameter was observed with a high-order discretization, indicating that high-order discretizations may be an effective approach for increasing the accuracy and reliability of hybrid turbulence models for scale-resolving simulations without a significant increase in computational effort.