(322b) Bits and Pieces of the Turbulence Puzzle: A new length scale, deep structural similarity and a mechanism underlying diverse drag reduction strategies. | AIChE

(322b) Bits and Pieces of the Turbulence Puzzle: A new length scale, deep structural similarity and a mechanism underlying diverse drag reduction strategies.

Authors 

Adrian, R. - Presenter, Iowa State University
Being a multi-scale random process, the length and velocity scales of turbulence are essential parts of the conceptualization and analysis of turbulent fluid flow. Quantities such as the integral length scale, the Taylor micro- scale and the Kolmogorov micro-scale are well defined for homogeneous turbulence, but inhomogeneity, finite domain size and finite Reynolds number introduce additional scales and make interpretation of the classical scale tricky. The vortex micro-scale is a new new quantity that clarifies the link between the micro-scales of Taylor and Kolmogorov at finite Reynolds number and provide a physically reasonable measure of viscous vortex diameters. It’s derivation and interpretation are presented. The concept of deep similarity between different types of wall-bounded turbulent flows states that the structures associated with different length scales in seemingly disparate flows occur in analogous ways. This analogy makes it possible to predict the existence and general character of unrecognized scales in one flow based on a more complete understanding of another flow. Lastly, while turbulent drag reduction can be accomplished by many different strategies, i.e. addition of dilute polymer solution, acceleration, riblets and patterns on the wall, transverse wall motion, etc., we have found that there is an underlying mechanism that is common to each of them. This mechanism is the suppression of hairpin packet formation by reducing the amplitude of the wall-normal velocity fluctuations in the buffer layer below the threshold needed for auto generation of packets. Suppression leads to many fewer turbulent stress producing eddies in the logarithmic layer.