Multi-Flow Generalization in Data-Driven Turbulence Modeling: An Exploratory Study

Abstract

Computational fluid dynamics (CFD) is an important tool in design involving fluid flow. Scale-resolving CFD methods exist, but they are too computationally expensive for practical design. Instead, the relatively cheap Reynolds-averaged Navier-Stokes (RANS) approach is the industry standard, specifically models based on the Boussinesq hypothesis, which are unable to represent the effect of turbulence anisotropy. Development of RANS models based purely on physical arguments has stagnated; however, data-driven turbulence modeling presents a paradigm shift for improved predictions. Though this technique has produced successful models tailored to specific flows, it has yet to produce a successful general turbulence model, which is the focus of this work.

In this work, models consist of corrections to the classical k-omega SST turbulence model; bijDelta to correct the Reynolds stress anisotropy and R to correct the turbulent kinetic energy. High fidelity data combined with the k-corrective-frozen technique is used to obtain exact correction fields, which are validated. Then, the SpaRTA framework is used to regress symbolic expressions for the corrections. Using a newly developed solver, models are injected into a full RANS solver to assess a-posteriori performance for various test cases. SpaRTA identifies a good R model, but only after a-posteriori optimization of coefficients, this model holds promise for generalization. Meanwhile, SpaRTA is unable to find a good bijDelta model due to its reliance on linear regression. A new framework based on non-linear regression is introduced which identifies a much better bijDelta model, though this model is Reynolds number dependent.