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Turbulent boundary layers at moderate Reynolds numbers: inflow length and tripping effects
Turbulent boundary layers at moderate Reynolds numbers: inflow length and tripping effects
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Turbulent boundary layers at moderate Reynolds numbers: inflow length and tripping effects

Schlatter, Philipp ; Örlü, Ramis

Journal of fluid mechanics, 2012-11, Vol.710, p.5-34 [Revista revisada por pares]

Cambridge, UK: Cambridge University Press

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2
Dissipation, enstrophy and pressure statistics in turbulence simulations at high Reynolds numbers
Dissipation, enstrophy and pressure statistics in turbulence simulations at high Reynolds numbers
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Dissipation, enstrophy and pressure statistics in turbulence simulations at high Reynolds numbers

Yeung, P. K. ; Donzis, D. A. ; Sreenivasan, K. R.

Journal of fluid mechanics, 2012-06, Vol.700, p.5-15 [Revista revisada por pares]

Cambridge, UK: Cambridge University Press

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3
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
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Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

Ling, Julia ; Kurzawski, Andrew ; Templeton, Jeremy

Journal of fluid mechanics, 2016-11, Vol.807, p.155-166 [Revista revisada por pares]

Cambridge, UK: Cambridge University Press

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4
Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned
Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned
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Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned

Wu, Jinlong ; Xiao, Heng ; Sun, Rui ; Wang, Qiqi

Journal of fluid mechanics, 2019-06, Vol.869, p.553-586 [Revista revisada por pares]

Cambridge, UK: Cambridge University Press

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5
Dynamic behavior of fine particles in dry medium-intensity magnetic separator based on k-ε turbulence model and low reynolds number k-ε turbulence model
Dynamic behavior of fine particles in dry medium-intensity magnetic separator based on k-ε turbulence model and low reynolds number k-ε turbulence model
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Dynamic behavior of fine particles in dry medium-intensity magnetic separator based on k-ε turbulence model and low reynolds number k-ε turbulence model

Tang, Dongdong ; Wang, Feiwang ; Dai, Huixin

Separation science and technology, 2021-05, Vol.56 (8), p.1383-1396 [Revista revisada por pares]

Abingdon: Taylor & Francis

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6
Reynolds number scaling of the peak turbulence intensity in wall flows
Reynolds number scaling of the peak turbulence intensity in wall flows
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Reynolds number scaling of the peak turbulence intensity in wall flows

Chen, Xi ; Sreenivasan, Katepalli R.

Journal of fluid mechanics, 2021-02, Vol.908, Article R3 [Revista revisada por pares]

Cambridge, UK: Cambridge University Press

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7
Optimal eddy viscosity for resolvent-based models of coherent structures in turbulent jets
Optimal eddy viscosity for resolvent-based models of coherent structures in turbulent jets
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Optimal eddy viscosity for resolvent-based models of coherent structures in turbulent jets

Pickering, Ethan ; Rigas, Georgios ; Schmidt, Oliver T. ; Sipp, Denis ; Colonius, Tim

Journal of fluid mechanics, 2021-04, Vol.917, Article A29 [Revista revisada por pares]

Cambridge, UK: Cambridge University Press

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8
On grid-generated turbulence in the near- and far field regions
On grid-generated turbulence in the near- and far field regions
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On grid-generated turbulence in the near- and far field regions

Isaza, Juan C. ; Salazar, Ricardo ; Warhaft, Zellman

Journal of fluid mechanics, 2014-08, Vol.753, p.402-426 [Revista revisada por pares]

Cambridge, UK: Cambridge University Press

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9
Super-resolution reconstruction of turbulent flows with machine learning
Super-resolution reconstruction of turbulent flows with machine learning
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Super-resolution reconstruction of turbulent flows with machine learning

Fukami, Kai ; Fukagata, Koji ; Taira, Kunihiko

Journal of fluid mechanics, 2019-07, Vol.870, p.106-120 [Revista revisada por pares]

Cambridge, UK: Cambridge University Press

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10
Unsupervised deep learning for super-resolution reconstruction of turbulence
Unsupervised deep learning for super-resolution reconstruction of turbulence
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Unsupervised deep learning for super-resolution reconstruction of turbulence

Kim, Hyojin ; Kim, Junhyuk ; Won, Sungjin ; Lee, Changhoon

Journal of fluid mechanics, 2021-01, Vol.910, Article A29 [Revista revisada por pares]

Cambridge, UK: Cambridge University Press

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