The non-local nature of structure functions

Kolmogorov's two-thirds, ((Δv) 2) ∼ e 2/ 3r 2/ 3, and five-thirds, E ∼ e 2/ 3k -5/ 3, laws are formally equivalent in the limit of vanishing viscosity, v → 0. However, for most Reynolds numbers encountered in laboratory scale experiments, or numerical simulations, it is invariably easier to observe the five-thirds law. By creating artificial fields of isotropic turbulence composed of a random sea of Gaussian eddies whose size and energy distribution can be controlled, we show why this is the case. The energy of eddies of scale, s, is shown to vary as s 2/ 3, in accordance with Kolmogorov's 1941 law, and we vary the range of scales, γ = s max/s min, in any one realisation from γ = 25 to γ = 800. This is equivalent to varying the Reynolds number in an experiment from R λ = 60 to R λ = 600. While there is some evidence of a five-thirds law for g > 50 (R λ > 100), the two-thirds law only starts to become apparent when g approaches 200 (R λ ∼ 240). The reason for this discrepancy is that the second-order structure function is a poor filter, mixing information about energy and enstrophy, and from scales larger and smaller than r. In particular, in the inertial range, ((Δv) 2) takes the form of a mixed power-law, a 1+a 2r 2+a 3r 2/ 3, where a 2r 2 tracks the variation in enstrophy and a 3r 2/ 3 the variation in energy. These findings are shown to be consistent with experimental data where the polution of the r 2/ 3 law by the enstrophy contribution, a 2r 2, is clearly evident. We show that higherorder structure functions (of even order) suffer from a similar deficiency.

Construction of nonparametric Bayesian models from parametric Bayes equations

We consider the general problem of constructing nonparametric Bayesian models on infinite-dimensional random objects, such as functions, infinite graphs or infinite permutations. The problem has generated much interest in machine learning, where it is treated heuristically, but has not been studied in full generality in non-parametric Bayesian statistics, which tends to focus on models over probability distributions. Our approach applies a standard tool of stochastic process theory, the construction of stochastic processes from their finite-dimensional marginal distributions. The main contribution of the paper is a generalization of the classic Kolmogorov extension theorem to conditional probabilities. This extension allows a rigorous construction of nonparametric Bayesian models from systems of finite-dimensional, parametric Bayes equations. Using this approach, we show (i) how existence of a conjugate posterior for the nonparametric model can be guaranteed by choosing conjugate finite-dimensional models in the construction, (ii) how the mapping to the posterior parameters of the nonparametric model can be explicitly determined, and (iii) that the construction of conjugate models in essence requires the finite-dimensional models to be in the exponential family. As an application of our constructive framework, we derive a model on infinite permutations, the nonparametric Bayesian analogue of a model recently proposed for the analysis of rank data.

Geometry and interaction of structures in homogeneous isotropic turbulence

A strategy to extract turbulence structures from direct numerical simulation (DNS) data is described along with a systematic analysis of geometry and spatial distribution of the educed structures. A DNS dataset of decaying homogeneous isotropic turbulence at Reynolds number Reλ = 141 is considered. A bandpass filtering procedure is shown to be effective in extracting enstrophy and dissipation structures with their smallest scales matching the filter width, L. The geometry of these educed structures is characterized and classified through the use of two non-dimensional quantities, planarity' and filamentarity', obtained using the Minkowski functionals. The planarity increases gradually by a small amount as L is decreased, and its narrow variation suggests a nearly circular cross-section for the educed structures. The filamentarity increases significantly as L decreases demonstrating that the educed structures become progressively more tubular. An analysis of the preferential alignment between the filtered strain and vorticity fields reveals that vortical structures of a given scale L are most likely to align with the largest extensional strain at a scale 3-5 times larger than L. This is consistent with the classical energy cascade picture, in which vortices of a given scale are stretched by and absorb energy from structures of a somewhat larger scale. The spatial distribution of the educed structures shows that the enstrophy structures at the 5η scale (where η is the Kolmogorov scale) are more concentrated near the ones that are 3-5 times larger, which gives further support to the classical picture. Finally, it is shown by analysing the volume fraction of the educed enstrophy structures that there is a tendency for them to cluster around a larger structure or clusters of larger structures. Copyright © 2012 Cambridge University Press.

Nucleation, growth, and control of ferroelectric-ferroelastic domains in thin polycrystalline films

The unique response of ferroic materials to external excitations facilitates them for diverse technologies, such as nonvolatile memory devices. The primary driving force behind this response is encoded in domain switching. In bulk ferroics, domains switch in a two-step process: nucleation and growth. For ferroelectrics, this can be explained by the Kolmogorov-Avrami-Ishibashi (KAI) model. Nevertheless, it is unclear whether domains remain correlated in finite geometries, as required by the KAI model. Moreover, although ferroelastic domains exist in many ferroelectrics, experimental limitations have hindered the study of their switching mechanisms. This uncertainty limits our understanding of domain switching and controllability, preventing thin-film and polycrystalline ferroelectrics from reaching their full technological potential. Here we used piezoresponse force microscopy to study the switching mechanisms of ferroelectric-ferroelastic domains in thin polycrystalline Pb 0.7Zr0.3TiO3 films at the nanometer scale. We have found that switched biferroic domains can nucleate at multiple sites with a coherence length that may span several grains, and that nucleators merge to form mesoscale domains, in a manner consistent with that expected from the KAI model. © 2012 American Physical Society.

In situ intercalation dynamics in inorganic-organic layered perovskite thin films.

The properties of layered inorganic semiconductors can be manipulated by the insertion of foreign molecular species via a process known as intercalation. In the present study, we investigate the phenomenon of organic moiety (R-NH3I) intercalation in layered metal-halide (PbI2)-based inorganic semiconductors, leading to the formation of inorganic-organic (IO) perovskites [(R-NH3)2PbI4]. During this intercalation strong resonant exciton optical transitions are created, enabling study of the dynamics of this process. Simultaneous in situ photoluminescence (PL) and transmission measurements are used to track the structural and exciton evolution. On the basis of the experimental observations, a model is proposed which explains the process of IO perovskite formation during intercalation of the organic moiety through the inorganic semiconductor layers. The interplay between precursor film thickness and organic solution concentration/solvent highlights the role of van der Waals interactions between the layers, as well as the need for maintaining stoichiometry during intercalation. Nucleation and growth occurring during intercalation matches a Johnson-Mehl-Avrami-Kolmogorov model, with results fitting both ideal and nonideal cases.