In this work, 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.
Pereira, Filipe S., et al. "Toward Predictive RANS and SRS Computations of Turbulent External Flows of Practical Interest." Archives of Computational Methods in Engineering, vol. 28, Feb. 2021. https://doi.org/10.1007/s11831-021-09563-0
Pereira, Filipe S., Eça, Luís, Vaz, Guilherme, & Girimaji, Sharath S. (2021). Toward Predictive RANS and SRS Computations of Turbulent External Flows of Practical Interest. Archives of Computational Methods in Engineering, 28. https://doi.org/10.1007/s11831-021-09563-0
Pereira, Filipe S., Eça, Luís, Vaz, Guilherme, et al., "Toward Predictive RANS and SRS Computations of Turbulent External Flows of Practical Interest," Archives of Computational Methods in Engineering 28 (2021), https://doi.org/10.1007/s11831-021-09563-0
@article{osti_1783530,
author = {Pereira, Filipe S. and Eça, Luís and Vaz, Guilherme and Girimaji, Sharath S.},
title = {Toward Predictive RANS and SRS Computations of Turbulent External Flows of Practical Interest},
annote = {In this work, 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.},
doi = {10.1007/s11831-021-09563-0},
url = {https://www.osti.gov/biblio/1783530},
journal = {Archives of Computational Methods in Engineering},
issn = {ISSN 1134-3060},
volume = {28},
place = {United States},
publisher = {Springer Nature},
year = {2021},
month = {02}}
ASME 2005 24th International Conference on Offshore Mechanics and Arctic Engineering, 24th International Conference on Offshore Mechanics and Arctic Engineering: Volume 3https://doi.org/10.1115/OMAE2005-67044
Vaz, Guilherme; Mabilat, Christophe; van der Wal, Remmelt
ASME 2007 26th International Conference on Offshore Mechanics and Arctic Engineering, Volume 3: Pipeline and Riser Technology; CFD and VIVhttps://doi.org/10.1115/OMAE2007-29275
Vaz, Guilherme; Jaouen, Frederick; Hoekstra, Martin
ASME 2009 28th International Conference on Ocean, Offshore and Arctic Engineering, Volume 5: Polar and Arctic Sciences and Technology; CFD and VIVhttps://doi.org/10.1115/OMAE2009-79398