Nonequilibrium scalings of turbulent wakes

Nonequilibrium turbulent wake scalings are not the preserve of irregular (fractal-like/multiscale) plates but appear to be universal, as they also hold for regular plates over a very substantial downstream distance

Direct numerical simulation of a turbulent wake: the non-equilibrium dissipation law

A Direct Numerical Simulation (DNS) study of an axisymmetric turbulent wake generated by a square plate placed normal to the incoming flow is presented. It is shown that the new axisymmetric turbulent wake scalings obtained recently for a fractal-like wake generator (Dairay et al., 2015), specifically a plate with irregular multiscale periphery placed normal to the incoming flow, are also present in an axisymmetric turbulent wake generated by a regular square plate. These new scalings are therefore not caused by the multiscale nature of the wake generator but have more general validity

Physical scaling of numerical dissipation for LES

In this work, we are interested in an alternative way to perform LES using a numerical substitute of a subgrid-scale model with a calibration based on physical inputs

Numerical dissipation vs. subgrid-scale modelling for large eddy simulation

This study presents an alternative way to perform large eddy simulation based on a targeted numerical dissipation introduced by the discretization of the viscous term. It is shown that this regularisation technique is equivalent to the use of spectral vanishing viscosity. The flexibility of the method ensures high-order accuracy while controlling the level and spectral features of this purely numerical viscosity. A Pao-like spectral closure based on physical arguments is used to scale this numerical viscosity a priori. It is shown that this way of approaching large eddy simulation is more efficient and accurate than the use of the very popular Smagorinsky model in standard as well as in dynamic version. The main strength of being able to correctly calibrate numerical dissipation is the possibility to regularise the solution at the mesh scale. Thanks to this property, it is shown that the solution can be seen as numerically converged. Conversely, the two versions of the Smagorinsky model are found unable to ensure regularisation while showing a strong sensitivity to numerical errors. The originality of the present approach is that it can be viewed as implicit large eddy simulation, in the sense that the numerical error is the source of artificial dissipation, but also as explicit subgrid-scale modelling, because of the equivalence with spectral viscosity prescribed on a physical basis

Non-equilibrium scaling laws in axisymmetric turbulent wakes

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3D Taylor-Green vortex Direct Numerical Simulation statistics from Re=1250 to Re=20000

Statistical data for the 3D Taylor Green flow from Re=1250 to Re=20000 obtained with the flow solver Incompact3d. # =========================================================================================== # When publishing results using this data, the following paper should be cited as the source: # Thibault Dairay, Eric Lamballais, Sylvain Laizet and John Christos Vassilicos # Numerical dissipation vs. subgrid-scale modelling for large eddy simulation # Journal of Computational Physics 337 (2017) 252–274 # https://doi.org/10.1016/j.jcp.2017.02.035 # =========================================================================================== # Column 1 : time t # Column 2 : kinetic energy E_k [=(u^2+v^2+w^2)/2] # Column 3 : dissipation epsilon_t [=-dE_k/dt] # Column 4 : dissipation epsilon [= nu ((du/dx)^2+(du/dy)^2+(du/dz)^2+(dv/dx)^2+(dv/dy)^2+(dv/dz)^2+(dw/dx)^2+(dw/dy)^2+ dw/dz)^2)] # Column 5 : enstrophy Dzeta [=2 nu epsilon] # Column 6 : mean square u^2 # Column 7 : mean square v^2 # Column 8 : mean square w^2 # Column 9 : mean square (du/dx)^2 # Column 10 : mean square (du/dy)^2 # Column 11 : mean square (du/dz)^2 # Column 12 : mean square (dv/dx)^2 # Column 13 : mean square (dv/dy)^2 # Column 14 : mean square (dv/dz)^2 # Column 15 : mean square (dw/dx)^2 # Column 16 : mean square (dw/dy)^2 # Column 17 : mean square (dw/dz)^2Statistical data for the 3D Taylor Green flow from Re=1250 to Re=20000 obtained with the flow solver Incompact3d. # =========================================================================================== # When publishing results using this data, the following paper should be cited as the source: # Thibault Dairay, Eric Lamballais, Sylvain Laizet and John Christos Vassilicos # Numerical dissipation vs. subgrid-scale modelling for large eddy simulation # Journal of Computational Physics 337 (2017) 252–274 # https://doi.org/10.1016/j.jcp.2017.02.035 # =========================================================================================== # Column 1 : time t # Column 2 : kinetic energy E_k [=(u^2+v^2+w^2)/2] # Column 3 : dissipation epsilon_t [=-dE_k/dt] # Column 4 : dissipation epsilon [= nu ((du/dx)^2+(du/dy)^2+(du/dz)^2+(dv/dx)^2+(dv/dy)^2+(dv/dz)^2+(dw/dx)^2+(dw/dy)^2+ dw/dz)^2)] # Column 5 : enstrophy Dzeta [=2 nu epsilon] # Column 6 : mean square u^2 # Column 7 : mean square v^2 # Column 8 : mean square w^2 # Column 9 : mean square (du/dx)^2 # Column 10 : mean square (du/dy)^2 # Column 11 : mean square (du/dz)^2 # Column 12 : mean square (dv/dx)^2 # Column 13 : mean square (dv/dy)^2 # Column 14 : mean square (dv/dz)^2 # Column 15 : mean square (dw/dx)^2 # Column 16 : mean square (dw/dy)^2 # Column 17 : mean square (dw/dz)^