Logarithmic scaling in the near-dissipation range of turbulence

A logarithmic scaling for structure functions, in the form $S_p \sim [\ln (r/\eta)]^{\zeta_p}$, where $\eta$ is the Kolmogorov dissipation scale and $\zeta_p$ are the scaling exponents, is suggested for the statistical description of the near-dissipation range for which classical power-law scaling does not apply. From experimental data at moderate Reynolds numbers, it is shown that the logarithmic scaling, deduced from general considerations for the near-dissipation range, covers almost the entire range of scales (about two decades) of structure functions, for both velocity and passive scalar fields. This new scaling requires two empirical constants, just as the classical scaling does, and can be considered the basis for extended self-similarity

Beyond scaling and locality in turbulence

An analytic perturbation theory is suggested in order to find finite-size corrections to the scaling power laws. In the frame of this theory it is shown that the first order finite-size correction to the scaling power laws has following form $S(r) \cong cr^{\alpha_0}[\ln(r/\eta)]^{\alpha_1}$, where $\eta$ is a finite-size scale (in particular for turbulence, it can be the Kolmogorov dissipation scale). Using data of laboratory experiments and numerical simulations it is shown shown that a degenerate case with $\alpha_0 =0$ can describe turbulence statistics in the near-dissipation range $r > \eta$, where the ordinary (power-law) scaling does not apply. For moderate Reynolds numbers the degenerate scaling range covers almost the entire range of scales of velocity structure functions (the log-corrections apply to finite Reynolds number). Interplay between local and non-local regimes has been considered as a possible hydrodynamic mechanism providing the basis for the degenerate scaling of structure functions and extended self-similarity. These results have been also expanded on passive scalar mixing in turbulence. Overlapping phenomenon between local and non-local regimes and a relation between position of maximum of the generalized energy input rate and the actual crossover scale between these regimes are briefly discussed.Comment: extended versio

Non-specific cellular uptake of surface-functionalized quantum dots

We report a systematic empirical study of nanoparticle internalization into cells via non-specific pathways. The nanoparticles were comprised of commercial quantum dots (QDs) that were highly visible under a fluorescence confocal microscope. Surface-modified QDs with basic biologically-significant moieties, e.g. carboxyl, amino, streptavidin were used, in combination with the surface derivatization with polyethylene glycol (PEG) in a range of immortalized cell lines. Internalization rates were derived from image analysis and a detailed discussion about the effect of nanoparticle size, charge and surface groups is presented. We find that PEG-derivatization dramatically suppresses the non-specific uptake while PEG-free carboxyl and amine functional groups promote QD internalization. These uptake variations displayed a remarkable consistency across different cell types. The reported results are important for experiments concerned with cellular uptake of surface-functionalized nanomaterials, both when non-specific internalization is undesirable and also when it is intended for material to be internalized as efficiently as possible. Published article at: http://iopscience.iop.org/0957-4484/21/28/285105/Comment: 14 pages 7 figure

Biological Principles in Self-Organization of Young Brain - Viewed from Kohonen Model

Variants of the Kohonen model are proposed to study biological principles of self-organization in a model of young brain. We suggest a function to measure aquired knowledge and use it to auto-adapt the topology of neuronal connectivity, yielding substantial organizational improvement relative to the standard model. In the early phase of organization with most intense learning, we observe that neural connectivity is of Small World type, which is very efficient to organize neurons in response to stimuli. In analogy to human brain where pruning of neural connectivity (and neuron cell death) occurs in early life, this feature is present also in our model, which is found to stabilize neuronal response to stimuli

A Minimalist Turbulent Boundary Layer Model

We introduce an elementary model of a turbulent boundary layer over a flat surface, given as a vertical random distribution of spanwise Lamb-Oseen vortex configurations placed over a non-slip boundary condition line. We are able to reproduce several important features of realistic flows, such as the viscous and logarithmic boundary sublayers, and the general behavior of the first statistical moments (turbulent intensity, skewness and flatness) of the streamwise velocity fluctuations. As an application, we advance some heuristic considerations on the boundary layer underlying kinematics that could be associated with the phenomenon of drag reduction by polymers, finding a suggestive support from its experimental signatures.Comment: 5 pages, 10 figure

Probability density function of turbulent velocity fluctuations in rough-wall boundary layer

The probability density function of single-point velocity fluctuations in turbulence is studied systematically using Fourier coefficients in the energy-containing range. In ideal turbulence where energy-containing motions are random and independent, the Fourier coefficients tend to Gaussian and independent of each other. Velocity fluctuations accordingly tend to Gaussian. However, if energy-containing motions are intermittent or contaminated with bounded-amplitude motions such as wavy wakes, the Fourier coefficients tend to non-Gaussian and dependent of each other. Velocity fluctuations accordingly tend to non-Gaussian. These situations are found in our experiment of a rough-wall boundary layer.Comment: 6 pages, to appear in Physical Review

Asteroseismic detection of latitudinal differential rotation in 13 Sun-like stars

The differentially rotating outer layers of stars are thought to play a role in driving their magnetic activity, but the underlying mechanisms that generate and sustain differential rotation are poorly understood. We report the measurement of latitudinal differential rotation in the convection zones of 40 Sun-like stars using asteroseismology. For the most significant detections, the stars' equators rotate approximately twice as fast as their mid-latitudes. The latitudinal shear inferred from asteroseismology is much larger than predictions from numerical simulations.Comment: 45 pages, 11 figures, 4 tables, published in Scienc

Butterfly diagram of a Sun-like star observed using asteroseismology

Stellar magnetic fields are poorly understood but are known to be important for stellar evolution and exoplanet habitability. They drive stellar activity, which is the main observational constraint on theoretical models for magnetic field generation and evolution. Starspots are the main manifestation of the magnetic fields at the stellar surface. In this study we measure the variation of their latitude with time, called a butterfly diagram in the solar case, for the solar analogue HD 173701 (KIC 8006161). To that effect, we use Kepler data, to combine starspot rotation rates at different epochs and the asteroseismically determined latitudinal variation of the stellar rotation rates. We observe a clear variation of the latitude of the starspots. It is the first time such a diagram is constructed using asteroseismic data.Comment: 8 pages, 4 figures, accepted in A&A Letter