RANS Equations with Explicit Data-Driven Reynolds Stress Closure Can Be Ill-Conditioned

TL;DR

This paper introduces a local condition number metric to evaluate the conditioning of RANS equations with data-driven Reynolds stress models, revealing potential ill-conditioning issues that impact turbulence modeling accuracy.

Contribution

It proposes a novel local condition number metric for a priori assessment of RANS equation conditioning, aiding the development of robust data-driven turbulence models.

Findings

The metric explains deterioration of model conditioning with increasing Reynolds number.

Implicit Reynolds stress treatment shows better conditioning than explicit methods.

The metric can guide future turbulence model development to ensure well-conditioned RANS equations.

Abstract

Reynolds-averaged Navier--Stokes (RANS) simulations with turbulence closure models continue to play important roles in industrial flow simulations. However, the commonly used linear eddy viscosity models are intrinsically unable to handle flows with non-equilibrium turbulence. Reynolds stress models, on the other hand, are plagued by their lack of robustness. Recent studies in plane channel flows found that even substituting Reynolds stresses with errors below 0.5% from direct numerical simulation (DNS) databases into RANS equations leads to velocities with large errors (up to 35%). While such an observation may have only marginal relevance to traditional Reynolds stress models, it is disturbing for the recently emerging data-driven models that treat the Reynolds stress as an explicit source term in the RANS equations, as it suggests that the RANS equations with such models can be ill-conditioned. So far, a rigorous analysis of the condition of such models is still lacking. As such, in this work we propose a metric based on local condition number function for a priori evaluation of the conditioning of the RANS equations. We further show that the ill-conditioning cannot be explained by the global matrix condition number of the discretized RANS equations. Comprehensive numerical tests are performed on turbulent channel flows at various Reynolds numbers and additionally on two complex flows, i.e., flow over periodic hills and flow in a square duct. Results suggest that the proposed metric can adequately explain observations in previous studies, i.e., deteriorated model conditioning with increasing Reynolds number and better conditioning of the implicit treatment of Reynolds stress compared to the explicit treatment. This metric can play critical roles in the future development of data-driven turbulence models by enforcing the conditioning as a requirement on these models.