Analysis on numerical stability and convergence of RANS turbulence models from the perspective of coupling modes

TL;DR

This paper investigates how different coupling modes affect the stability and convergence of RANS turbulence models, highlighting that mutual coupling improves stability over frozen coupling in iterative simulations.

Contribution

It introduces a comparative analysis of coupling modes in RANS turbulence models, revealing the advantages of mutual coupling for stability and convergence in data-driven approaches.

Findings

Frozen coupling mode may cause divergence and instability.

Mutual coupling mode maintains good convergence and stability.

Provides new insights into coupling strategies for machine learning turbulence models.

Abstract

Reynolds-averaged Navier-Stokes simulations are still the main method to study complex flows in engineering. However, traditional turbulence models cannot accurately predict flow fields with separations. In such situation, machine learning methods provide an effective way to build new data-driven turbulence closure models. Nevertheless, a bottleneck that the data-driven turbulence models encounter is how to ensure the stability and convergence of the RANS equations in posterior iterations. This paper studies the effects of different coupling modes on the convergence and stability between the RANS equations and turbulence models. Numerical results demonstrate that the frozen coupling mode, commonly used in machine learning turbulence models, may lead to divergence and instability in posterior iterations; while the mutual coupling mode can maintain good convergence and stability in the process of iterations. This research can provide a new perspective to the coupling mode for machine learning turbulence models with RANS equations in posterior iterations.