A rationale for implicit turbulence modelling

By Len G. Margolin, W. Rider. 2001

233 citations recorded by the source corpus.

Subfields: Atmospheric Science, Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/a34bd45bd9f38ab50a39c864c336f7e5c8724ff0

How researchers see this paper

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

In Atmospheric Science

1. Andrew W. Cook
2. Stefan Hickel
3. Nikolaus A. Adams
4. J. Andrzej Domaradzki
5. Dimitris Drikakis
6. Christophe Bogey
7. Christophe Bailly
8. Sergio Pirozzoli

In Computational Mechanics

1. Andrew W. Cook
2. Stefan Hickel
3. Nikolaus A. Adams
4. Dimitris Drikakis
5. J. Andrzej Domaradzki
6. Sergio Pirozzoli
7. Christophe Bogey
8. Christophe Bailly

Citations in the corpus

• Advances in turbulent flow computations using high-resolution methods (2003, methodology): “gains from truncation error analysis and its possible connection to turbulent flow properties [22], and finally attempt to stimulate further research that will address in a more systematic framework the relation of computational methods and physics of turbulent flow.”
• Artificial Fluid Properties for Large-Eddy Simulation of Compressible Turbulent Mixing (2007, methodology): “Such is the approach of Implicit Large-Eddy Simulation (ILES) [Schumann, 1975, Fureby and Grinstein, 2002, Margolin and Rider, 2002, 2005, Hickel et al., 2006].”
• An adaptive local deconvolution method for implicit LES (2005, methodology): “An important tool is the modified-differential equation analysis (MDEA) [13–15].”