On frozen-RANS approaches in data-driven turbulence modeling: Practical relevance of turbulent scale consistency during closure inference and application

Abstract

This paper addresses a consistency problem in data-driven turbulence modeling, which arises as the hypotheses are inferred from high-fidelity data but evaluated within a low-fidelity RANS solver. After elaborating on its origin, which is the systematic difference of the turbulent scales predicted by a low- and a high-fidelity solver, the frozen-RANS concept is thoroughly discussed as one possible mitigation strategy. Different variations of this concept are proposed varying in the way to incorporate the turbulent kinetic energy correction in the RANS solver and also in their degree to which they fulfill scale consistency. Applying these concepts to the neuralSST model, which is introduced in the companion paper (Mandler and Weigand, 2022), confirms the importance of scale consistency for an improved mean flow field prediction and that the latter can be achieved by a k-correction. This becomes particularly evident as the complexity of the test cases, which are different types of separated channel flows, increases. However, which particular strategy is employed to augment the low-fidelity prediction of the turbulent kinetic energy is of minor importance and the decision is mainly driven by the trade-off between the flexibility of the solver to modify the scale equations and the numerical stability of the resulting model.