Reynolds-Averaged Navier–Stokes Equations for Turbulence Modeling

By Giancarlo Alfonsi. 2009

386 citations recorded by the source corpus.

Subfields: Computational Mechanics, Environmental Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/ee741065f9c58865be194aec0a0754fa44a6c10d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. B. Galperin
2. Cheng Soon Ong
3. Alexander Patrick Mathews
4. Lexing Xie
5. Alasdair Tran
6. A. Spiga

In Environmental Engineering

1. B. Galperin
2. Cheng Soon Ong
3. Alexander Patrick Mathews
4. Lexing Xie
5. Alasdair Tran

In Mechanical Engineering

1. Cheng Soon Ong
2. Alexander Patrick Mathews
3. Lexing Xie
4. Alasdair Tran

Citations in the corpus

• Factorized Fourier Neural Operators (2021, methodology): “They can even be based on existing simulation methods such as the operator designed by Wang et al. (2020) that uses learned filters in both Reynolds-averaged Navier-Stokes and Large Eddy Simulation before combining the predictions using U-Net.”
• THE MARTIAN ATMOSPHERIC BOUNDARY LAYER (2011, background): “…has been used extensively in engineering, environmental, and geophysical sciences, and its foundations have been outlined in numerous review articles and books [e.g., Tennekes and Lumley, 1972; Mellor and Herring, 1973; Monin and Yaglom, 1975; Mellor and Yamada, 1982; Pope, 2005; Alfonsi, 2009].”
• Machine learning–accelerated computational fluid dynamics (2021, neutral): “Instead, it is traditional to use smoothed versions of the Navier– Stokes equations (10, 11) that allow coarser grids while sacrificing accuracy, such as Reynolds averaged Navier–Stokes (12, 13) and large-eddy simulation (LES) (14, 15).”