Toward Predictive RANS and SRS Computations of Turbulent External Flows of Practical Interest

• Published in: Archives of computational methods in engineering Vol. 28; no. 5; pp. 3953 - 4029
• Main Authors: Pereira, Filipe S., Eça, Luís, Vaz, Guilherme, Girimaji, Sharath S.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01-08-2021; Springer Nature B.V; Springer Nature
• Subjects: Commutation; Computational fluid dynamics; ENGINEERING; Literature reviews; Mathematical and Computational Engineering; Mathematical models; numerical prediction; RANS modeling; Review Article; Robustness (mathematics); SRS modeling; Steady flow; Turbulence modeling; Turbulence modeling, prediction; Unsteady flow; V&V
• ISSN: 1134-3060; 1886-1784
• DOI: 10.1007/s11831-021-09563-0

We investigate the main challenges to prediction of turbulent external flows of practical interest with Reynolds-Averaged Navier–Stokes equations (RANS) and Scale-Resolving Simulation (SRS) models. This represents a crucial step toward further developing and establishing these formulations so they can be confidently utilized in engineering problems without reference data. The study initiates by identifying the major challenges to prediction. A literature review is performed to illustrate their effects in RANS and SRS computations. Afterward, we evaluate the impact of the challenges to prediction by analyzing representative statistically steady and unsteady flows with prominent RANS and SRS methods. These include multiple turbulent viscosity and second-moment RANS closures, and hybrid and bridging SRS models. The results demonstrate the potential of the selected SRS models to predict engineering flows. Yet, they also show the importance of considering the challenges to prediction during the setup and conduction of numerical experiments. These can suppress the advantages of using SRS formulations. The data also indicate that only SRS models can confidently predict statistically unsteady flows. In contrast, the results demonstrate that mean-flow quantities of statistically steady flows can be efficiently calculated with RANS closures, especially second-moment closures. Among the selected SRS methods, bridging models reveal better suited for prediction due to their ability to prevent commutation errors and enable the robust evaluation of numerical and modeling errors. This last property allows the use of a new validation technique that does not require reference data.