The effects of forcing on a single stream shear layer and its parent boundary layer

Forcing and its effect on fluid flows has become an accepted tool in the study and control of flow systems. It has been used both as a diagnostic tool, to explore the development and interaction of coherent structures, and as a method of controlling the behavior of the flow. A number of forcing methods have been used in order to provide a perturbation to the flow; among these are the use of an oscillating trailing edge, acoustically driven slots, external acoustic forcing, and mechanical piston methods. The effect of a planar mechanical piston forcing on a single stream shear layer is presented; it can be noted that this is one of the lesser studied free shear layers. The single stream shear layer can be characterized by its primary flow velocity scale and the thickness of the separating boundary layer. The velocity scale is constant over the length of the flow field; theta (x) can be used as a width scale to characterize the unforced shear layer. In the case of the forced shear layer the velocity field is a function of phase time and definition of a width measure becomes somewhat problematic

Does gravity cause load-bearing bridges in colloidal and granular systems?

We study structures which can bear loads, "bridges", in particulate packings. To investigate the relationship between bridges and gravity, we experimentally determine bridge statistics in colloidal packings. We vary the effective magnitude and direction of gravity, volume fraction, and interactions, and find that the bridge size distributions depend only on the mean number of neighbors. We identify a universal distribution, in agreement with simulation results for granulars, suggesting that applied loads merely exploit preexisting bridges, which are inherent in dense packings

A neutron scattering study of the interplay between structure and magnetism in Ba(Fe1−x_{1-x}Cox_{x})2_2As2_2

Single crystal neutron diffraction is used to investigate the magnetic and structural phase diagram of the electron doped superconductor Ba(Fe$_{1-x}$Co$_x$)$_2$As$_2$. Heat capacity and resistivity measurements have demonstrated that Co doping this system splits the combined antiferromagnetic and structural transition present in BaFe$_2$As$_2$ into two distinct transitions. For $x$=0.025, we find that the upper transition is between the high-temperature tetragonal and low-temperature orthorhombic structures with ($T_{\mathrm{TO}}=99 \pm 0.5$ K) and the antiferromagnetic transition occurs at $T_{\mathrm{AF}}=93 \pm 0.5$ K. We find that doping rapidly suppresses the antiferromagnetism, with antiferromagnetic order disappearing at $x \approx 0.055$. However, there is a region of co-existence of antiferromagnetism and superconductivity. The effect of the antiferromagnetic transition can be seen in the temperature dependence of the structural Bragg peaks from both neutron scattering and x-ray diffraction. We infer from this that there is strong coupling between the antiferromagnetism and the crystal lattice

Preliminary Measurements of the Motion of Arcjet Current Channel Using Inductive Magnetic Probes

This paper covers the design and first measurements of non-perturbative, external inductive magnetic diagnostics for arcjet constrictors which can measure the motion of the arc current channel. These measurements of arc motion are motivated by previous simulations using the ARC Heater Simulator (ARCHeS), which predicted unsteady arc motion due to the magnetic kink instability. Measurements of the kink instability are relevant to characterizing motion of the enthalpy profile of the arcjet, the arcjet operational stability, and electrode damage due to associated arc detachment events. These first measurements indicate 4 mm oscillations at 0.5-2 kHz of the current profile

Optimisation of neural network with simultaneous feature selection and network prunning using evolutionary algorithm

Most advances on the Evolutionary Algorithm optimisation of Neural Network are on recurrent neural network using the NEAT optimisation method. For feed forward network, most of the optimisation are merely on the Weights and the bias selection which is generally known as conventional Neuroevolution. In this research work, a simultaneous feature reduction, network pruning and weight/biases selection is presented using fitness function design which penalizes selection of large feature sets. The fitness function also considers feature and the neuron reduction in the hidden layer. The results were demonstrated using two sets of data sets which are the cancer datasets and Thyroid datasets. Results showed backpropagation gradient descent error weights/biased optimisations performed slightly better at classification of the two datasets with lower misclassification rate and error. However, features and hidden neurons were reduced with the simultaneous feature/neurons switching using Genetic Algorithm. The number of features were reduced from 21 to 4 (Thyroid dataset) and 9 to 3 (cancer dataset) with only 1 hidden neuron in the processing layer for both network structures for the respective datasets. This research work will present the chromosome representation and the fitness function design

Dilatancy, Jamming, and the Physics of Granulation

Granulation is a process whereby a dense colloidal suspension is converted into pasty granules (surrounded by air) by application of shear. Central to the stability of the granules is the capillary force arising from the interfacial tension between solvent and air. This force appears capable of maintaining a solvent granule in a jammed solid state, under conditions where the same amount of solvent and colloid could also exist as a flowable droplet. We argue that in the early stages of granulation the physics of dilatancy, which requires that a powder expand on shearing, is converted by capillary forces into the physics of arrest. Using a schematic model of colloidal arrest under stress, we speculate upon various jamming and granulation scenarios. Some preliminary experimental results on aspects of granulation in hard-sphere colloidal suspensions are also reported.Comment: Original article intended for J Phys Cond Mat special issue on Granular Materials (M Nicodemi, Ed.