Transportation and coherent structures in MHD turbulent channel flow subject to uniform streamwise and spanwise magnetic fields

2021 ◽  
Vol 6 (9) ◽  
Author(s):  
Olivier Doche ◽  
Sedat Tardu ◽  
Jonathan Schillings ◽  
Amandine Capogna
2019 ◽  
Vol 863 ◽  
pp. 1190-1203 ◽  
Author(s):  
Sabarish B. Vadarevu ◽  
Sean Symon ◽  
Simon J. Illingworth ◽  
Ivan Marusic

We study the evolution of velocity fluctuations due to an isolated spatio-temporal impulse using the linearized Navier–Stokes equations. The impulse is introduced as an external body force in incompressible channel flow at $Re_{\unicode[STIX]{x1D70F}}=10\,000$. Velocity fluctuations are defined about the turbulent mean velocity profile. A turbulent eddy viscosity is added to the equations to fix the mean velocity as an exact solution, which also serves to model the dissipative effects of the background turbulence on large-scale fluctuations. An impulsive body force produces flow fields that evolve into coherent structures containing long streamwise velocity streaks that are flanked by quasi-streamwise vortices; some of these impulses produce hairpin vortices. As these vortex–streak structures evolve, they grow in size to be nominally self-similar geometrically with an aspect ratio (streamwise to wall-normal) of approximately 10, while their kinetic energy density decays monotonically. The topology of the vortex–streak structures is not sensitive to the location of the impulse, but is dependent on the direction of the impulsive body force. All of these vortex–streak structures are attached to the wall, and their Reynolds stresses collapse when scaled by distance from the wall, consistent with Townsend’s attached-eddy hypothesis.


Author(s):  
Atsushi Nagamachi ◽  
Takahiro Tsukahara

Abstract We tested Artificial Neural Networks (ANNs) to predict a fully-developed turbulent channel flow of a viscoelastic fluid in preparation for elucidating flow phenomenon and solving the difficulty in DNS (Direct Numerical Simulation) due to numerical instability of the viscoelastic fluid. Two kinds of ANNs (multi-layer perceptron (MLP) and U-Net) were trained using DNS data to predict conformation stress from given instantaneous field. The MLP showed accurate predictions and predictions got better with z-score normalization. ANN predicted accurately in near-wall region having coherent structures. In addition, we demonstrated that ANN get the nonlinear relationship between velocity gradient and viscoelastic stress partially.


2020 ◽  
Vol 888 ◽  
Author(s):  
S. Le Clainche ◽  
D. Izbassarov ◽  
M. Rosti ◽  
L. Brandt ◽  
O. Tammisola


2020 ◽  
Vol 85 ◽  
pp. 108662 ◽  
Author(s):  
Leandra I. Abreu ◽  
André V.G. Cavalieri ◽  
Philipp Schlatter ◽  
Ricardo Vinuesa ◽  
Dan S. Henningson

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