The electrorheology of suspensions consisting of Na-Fluorohectorite synthetic clay particles in silicon oil

Under application of an electric field greater than a triggering electric field $E_c \sim 0.4$ kV/mm, suspensions obtained by dispersing particles of the synthetic clay fluoro-hectorite in a silicon oil, aggregate into chain- and/or column-like structures parallel to the applied electric field. This micro-structuring results in a transition in the suspensions' rheological behavior, from a Newtonian-like behavior to a shear-thinning rheology with a significant yield stress. This behavior is studied as a function of particle volume fraction and strength of the applied electric field, $E$. The steady shear flow curves are observed to scale onto a master curve with respect to $E$, in a manner similar to what was recently found for suspensions of laponite clay [42]. In the case of Na-fluorohectorite, the corresponding dynamic yield stress is demonstrated to scale with respect to $E$ as a power law with an exponent $\alpha \sim 1.93$, while the static yield stress inferred from constant shear stress tests exhibits a similar behavior with $\alpha \sim 1.58$. The suspensions are also studied in the framework of thixotropic fluids: the bifurcation in the rheology behavior when letting the system flow and evolve under a constant applied shear stress is characterized, and a bifurcation yield stress, estimated as the applied shear stress at which viscosity bifurcation occurs, is measured to scale as $E^\alpha$ with $\alpha \sim 0.5$ to 0.6. All measured yield stresses increase with the particle fraction $\Phi$ of the suspension. For the static yield stress, a scaling law $\Phi^\beta$, with $\beta = 0.54$, is found. The results are found to be reasonably consistent with each other. Their similarities with-, and discrepancies to- results obtained on laponite-oil suspensions are discussed

Low-degree multi-spectral p-mode fitting

We combine unresolved-Sun velocity and intensity observations at multiple wavelengths from the Helioseismic and Magnetic Imager and Atmospheric Imaging Array onboard the Solar Dynamics Observatory to investigate the possibility of multi-spectral mode-frequency estimation at low spherical harmonic degree. We test a simple multi-spectral algorithm using a common line width and frequency for each mode and a separate amplitude, background and asymmetry parameter, and compare the results with those from fits to the individual spectra. The preliminary results suggest that this approach may provide a more stable fit than using the observables separately

Intercalation-enhanced electric polarization and chain formation of nano-layered particles

Microscopy observations show that suspensions of synthetic and natural nano-layered smectite clay particles submitted to a strong external electric field undergo a fast and extended structuring. This structuring results from the interaction between induced electric dipoles, and is only possible for particles with suitable polarization properties. Smectite clay colloids are observed to be particularly suitable, in contrast to similar suspensions of a non-swelling clay. Synchrotron X-ray scattering experiments provide the orientation distributions for the particles. These distributions are understood in terms of competing (i) homogenizing entropy and (ii) interaction between the particles and the local electric field; they show that clay particles polarize along their silica sheet. Furthermore, a change in the platelet separation inside nano-layered particles occurs under application of the electric field, indicating that intercalated ions and water molecules play a role in their electric polarization. The resulting induced dipole is structurally attached to the particle, and this causes particles to reorient and interact, resulting in the observed macroscopic structuring. The macroscopic properties of these electro-rheological smectite suspensions may be tuned by controlling the nature and quantity of the intercalated species, at the nanoscale.Comment: 7 pages, 5 figure

Estimation of Subspace Arrangements with Applications in Modeling and Segmenting Mixed Data

In recent years, subspace arrangements have become an increasingly popular class of mathematical objects to be used for modeling a multivariate mixed data set that is (approximately) piecewise linear. A subspace arrangement is a union of multiple subspaces. Each subspace can be conveniently used to model a homogeneous subset of the data. Hence, all the subspaces together can capture the heterogeneous structures within the data set. In this paper, we give a comprehensive introduction to one new approach for the estimation of subspace arrangements, known as generalized principal component analysis. We provide a comprehensive summary of important algebraic properties and statistical facts that are crucial for making the inference of subspace arrangements both efficient and robust, even when the given data are corrupted with noise or contaminated by outliers. This new method in many ways improves and generalizes extant methods for modeling or clustering mixed data. There have been successful applications of this new method to many real-world problems in computer vision, image processing, and system identification. In this paper, we will examine a couple of those representative applications.National Science Foundation / NSF CAREER IIS-0347456, NSF CRS-EHS-0509151, NSF CCF-TF-0514955, and NSF CAREER DMS-034901ONR YIP N00014-05-1-0633Ope

Scattering statistics of rock outcrops: Model-data comparisons and Bayesian inference using mixture distributions

The probability density function of the acoustic field amplitude scattered by the seafloor was measured in a rocky environment off the coast of Norway using a synthetic aperture sonar system, and is reported here in terms of the probability of false alarm. Interpretation of the measurements focused on finding appropriate class of statistical models (single versus two-component mixture models), and on appropriate models within these two classes. It was found that two-component mixture models performed better than single models. The two mixture models that performed the best (and had a basis in the physics of scattering) were a mixture between two K distributions, and a mixture between a Rayleigh and generalized Pareto distribution. Bayes' theorem was used to estimate the probability density function of the mixture model parameters. It was found that the K-K mixture exhibits significant correlation between its parameters. The mixture between the Rayleigh and generalized Pareto distributions also had significant parameter correlation, but also contained multiple modes. We conclude that the mixture between two K distributions is the most applicable to this dataset.Comment: 15 pages, 7 figures, Accepted to the Journal of the Acoustical Society of Americ

Hilbert Functions and Applications to the Estimation of Subspace Arrangements

This paper develops a new mathematical framework for studying the subspace-segmentation problem. We examine some important algebraic properties of subspace arrangements that are closely related to the subspace-segmentation problem. More specifically, we introduce an important class of invariants given by the Hilbert functions. We show that there exist rich relations between subspace arrangements and their corresponding Hilbert functions. We propose a new subspace- segmentation algorithm, and showcase two applications to demonstrate how the new theoretical revelation may solve subspace segmentation and model selection problems under less restrictive conditions with improved results.National Science Foundation / CAREER IIS-0347456, CRS-EHS-0509151, and CCF-TF-0514955ONR YIP N00014-05-1-0633Ope