New interpretation for error propagation of data-driven Reynolds stress closures via global stability analysis

In light of the challenges surrounding convergence and error propagation encountered in Reynolds-averaged Navier–Stokes (RANS) equations with data-driven Reynolds stress closures, researchers commonly attribute these issues to ill-conditioning through conditional number analysis. This paper delves into an additional factor, numerical instability, contributing to these challenges. We conduct a global stability analysis for the RANS equations, closed by the Reynolds stress and decomposition method. Our findings reveal that, for turbulent channel flow at high Reynolds numbers, significant ill-conditioning exists, yet the system remains stable. Conversely, for separated flow over periodic hills, notable ill-conditioning is absent, but unstable eigenvalues are present, indicating that error propagation arises from the mechanism of numerical instability. Furthermore, the effectiveness of the decomposition method employing eddy viscosity is compared, and the results show that the spatial distribution and amplitude of eddy viscosity influence the numerical stability.