Non-Linear Super-Stencils for Turbulence Model Corrections

TL;DR

This paper introduces a neural network-based correction method for RANS turbulence models, improving accuracy by learning from high-fidelity data and generalizing across different flow scenarios.

Contribution

It presents a novel Non-Linear Super-Stencil approach that enhances RANS simulations with learned corrections, demonstrating improved accuracy and generalization.

Findings

NLSS-corrected RANS outperforms standard models.

Method generalizes to different geometries and Reynolds numbers.

Significant accuracy improvements shown in turbulent flow simulations.

Abstract

Accurate simulation of turbulent flows remains a challenge due to the high computational cost of direct numerical simulations (DNS) and the limitations of traditional turbulence models. This paper explores a novel approach to augmenting standard models for Reynolds-Averaged Navier-Stokes (RANS) simulations using a Non-Linear Super-Stencil (NLSS). The proposed method introduces a fully connected neural network that learns a mapping from the local mean flow field to a corrective force term, which is added to a standard RANS solver in order to align its solution with high-fidelity data. A procedure is devised to extract training data from reference DNS and large eddy simulations (LES). To reduce the complexity of the non-linear mapping, the dimensionless local flow data is aligned with the local mean velocity, and the local support domain is scaled by the turbulent integral length scale. After being trained on a single periodic hill case, the NLSS-corrected RANS solver is shown to generalize to different periodic hill geometries and different Reynolds numbers, producing significantly more accurate solutions than the uncorrected RANS simulations.