|
🐢
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Physics-informed neural networks for solving Reynolds-averaged Navier-Stokes equations
Hamidreza Eivazi,
M. Tahani,
P. Schlatter,
R. Vinuesa
|
7 |
2021 |
7
🐢
|
|
🐢
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Predictions of turbulent shear flows using deep neural networks
P. A. Srinivasan,
L. Guastoni,
Hossein Azizpour,
P. Schlatter,
R. Vinuesa
|
7 |
2019 |
7
🐢
|
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🐢
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Convolutional-network models to predict wall-bounded turbulence from wall quantities
7 auth.
L. Guastoni,
A. Güemes,
A. Ianiro,
S. Discetti,
P. Schlatter,
Hossein Azizpour,
...
R. Vinuesa
|
7 |
2020 |
7
🐢
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🐬
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History effects and near equilibrium in adverse-pressure-gradient turbulent boundary layers
A. Bobke,
R. Vinuesa,
R. Örlü,
P. Schlatter
|
7 |
2017 |
7
🐬
|
|
🐬
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Direct numerical simulation of the flow around a wing section at moderate Reynolds number
S. Hosseini,
R. Vinuesa,
P. Schlatter,
A. Hanifi,
D. Henningson
|
6 |
2016 |
6
🐬
|
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🦁
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Aspect ratio effects in turbulent duct flows studied through direct numerical simulation
7 auth.
R. Vinuesa,
A. Noorani,
A. Lozano-Durán,
G. K. Khoury,
P. Schlatter,
P. Fischer,
...
H. Nagib
|
6 |
2014 |
6
🦁
|
|
🦁
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On determining characteristic length scales in pressure gradient turbulent boundary layers
R. Vinuesa,
R. Örlü,
P. Schlatter
|
6 |
2016 |
6
🦁
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🦁
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Turbulent boundary layers around wing sections up to Rec=1,000,000
R. Vinuesa,
P. Negi,
M. Atzori,
A. Hanifi,
D. Henningson,
P. Schlatter
|
6 |
2018 |
6
🦁
|
|
🐢
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Obtaining accurate mean velocity measurements in high Reynolds number turbulent boundary layers using Pitot tubes
15 auth.
S. Bailey,
M. Hultmark,
J. Monty,
P. Alfredsson,
M. S. Chong,
R. Duncan,
J. Fransson,
N. Hutchins,
I. Marusic,
B. McKeon,
...
H. Nagib,
R. Örlü,
A. Segalini,
A. Smits,
R. Vinuesa
|
6 |
2013 |
6
🐢
|
|
🦁
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Convergence of numerical simulations of turbulent wall-bounded flows and mean cross-flow structure of rectangular ducts
R. Vinuesa,
C. Prus,
P. Schlatter,
H. Nagib
|
6 |
2016 |
6
🦁
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