An inverse identification of eddy influence kernels from DNS moments yields a minimal hairpin vortex model that predicts mean velocity and streamwise variance across high Reynolds numbers.
Journal of fluid mechanics 774 , 395--415
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The η-grid sets Δy+ and Δz+ proportional to local Kolmogorov scale η, delivering <1% error versus Cartesian grids but with grid count scaling as Re_τ^2.5 (smooth) or Re_τ^2.0 (riblets) instead of Re_τ^3.
citing papers explorer
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The Minimal Attached Eddy in Wall Turbulence: Statistical Foundations, Inverse Identification and Influence Kernels
An inverse identification of eddy influence kernels from DNS moments yields a minimal hairpin vortex model that predicts mean velocity and streamwise variance across high Reynolds numbers.
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Leveraging unstructured grids for direct numerical simulations of wall turbulence
The η-grid sets Δy+ and Δz+ proportional to local Kolmogorov scale η, delivering <1% error versus Cartesian grids but with grid count scaling as Re_τ^2.5 (smooth) or Re_τ^2.0 (riblets) instead of Re_τ^3.