Turbulence closure modeling with data-driven techniques: Investigation of generalizable deep neural networks-Reference-Cited by-同舟云学术

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Turbulence closure modeling with data-driven techniques: Investigation of generalizable deep neural networks

Published:2021-11 Issue:11 Volume:33 Page:115132
ISSN:1070-6631
Container-title:Physics of Fluids
language:en
Short-container-title:Physics of Fluids

Author:

Taghizadeh Salar¹^ORCID,Witherden Freddie D.²,Hassan Yassin A.¹³,Girimaji Sharath S.¹²

Affiliation:

1. J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, College Station, Texas 77843, USA

2. Department of Ocean Engineering, Texas A&M University, College Station, Texas 77843, USA

3. Department of Nuclear Engineering, Texas A&M University, College Station, Texas 77843, USA

Publisher

AIP Publishing

Subject

Condensed Matter Physics,Fluid Flow and Transfer Processes,Mechanics of Materials,Computational Mechanics,Mechanical Engineering

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0070890

Reference97 articles.

1. G. Blaisdell and K. Shari , in Proceedings of the Summer Program ( Citeseer, 1996), p. 1.

2. Pressure–strain correlation modelling of complex turbulent flows

3. Analytical Methods for the Development of Reynolds-Stress Closures in Turbulence

4. Pressure–Strain Correlation Modeling: Towards Achieving Consistency with Rapid Distortion Theory

5. Toward approximating non-local dynamics in single-point pressure–strain correlation closures

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