Quasi-Wavelet Model of Von Kármán Spectrum of Turbulent Velocity Fluctuations

2004 ◽  
Vol 112 (1) ◽  
pp. 33-56 ◽  
Author(s):  
G. H. Goedecke ◽  
Vladimir E. Ostashev ◽  
D. Keith Wilson ◽  
Harry J. Auvermann
2008 ◽  
Vol 601 ◽  
pp. 339-364 ◽  
Author(s):  
FLORENT RAVELET ◽  
ARNAUD CHIFFAUDEL ◽  
FRANÇOIS DAVIAUD

We study the transition from laminar flow to fully developed turbulence for an inertially driven von Kármán flow between two counter-rotating large impellers fitted with curved blades over a wide range of Reynolds number (102–106). The transition is driven by the destabilization of the azimuthal shear layer, i.e. Kelvin–Helmholtz instability, which exhibits travelling/drifting waves, modulated travelling waves and chaos before the emergence of a turbulent spectrum. A local quantity – the energy of the velocity fluctuations at a given point – and a global quantity – the applied torque – are used to monitor the dynamics. The local quantity defines a critical Reynolds number Rec for the onset of time-dependence in the flow, and an upper threshold/crossover Ret for the saturation of the energy cascade. The dimensionless drag coefficient, i.e. the turbulent dissipation, reaches a plateau above this finite Ret, as expected for ‘Kolmogorov’-like turbulence for Re→∞. Our observations suggest that the transition to turbulence in this closed flow is globally supercritical: the energy of the velocity fluctuations can be considered as an order parameter characterizing the dynamics from the first laminar time-dependence to the fully developed turbulence. Spectral analysis in the temporal domain, moreover, reveals that almost all of the fluctuation energy is stored in time scales one or two orders of magnitude slower than the time scale based on impeller frequency.


2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Ming Li ◽  
Wei Zhao

von Karman originally deduced his spectrum of wind speed fluctuation based on the Stokes-Navier equation. Taking into account, the practical issues of measurement and/or computation errors, we suggest that the spectrum can be described from the point of view of the golden ratio. We call it the golden ratio phenomenon of the von Karman spectrum. To depict that phenomenon, we derive the von Karman spectrum based on fractional differential equations, which bridges the golden ratio to the von Karman spectrum and consequently provides a new outlook of random data following the von Karman spectrum in turbulence. In addition, we express the fractal dimension, which is a measure of local self-similarity, using the golden ratio, of random data governed by the von Karman spectrum.


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