Size segregation of spherical nickel pellets in the surface flow of a packed bed: Experiments and Discrete Element Method simulations

The size segregation of binary mixtures of spherical nickel pellets flowing into a packed bed was investigated with Discrete Element Method (DEM) simulations and physical experiments in 30 cm and 60 cm wide rectangular test cells. Each test cell approximates a vertical slice of a cylindrical packed bed, with a rising feed tube on one side of the cell representing the stationary frame of reference in the packed bed. As the feed tube is raised, the pellets flow laterally into the test cell to form a sloping surface inclined to the horizontal by the angle of repose. The lateral flow of pellets is confined near the surface of the packed bed, and was intermittent in character (i.e. surging). Velocity vectors show the detailed flow field in the simulated test cells. The smaller pellets were found to be concentrated near the core of the granular assembly, and the larger pellets segregate to the outer wall farthest from the feed tube. The degree of segregation, or coefficient of variation (variance/mean), is proportional to the diameter ratio α of the pellets and the length of the surface, and inversely proportional to the mass fraction of the smaller pellets within the range of parameters studied. The DEM simulations had an average deviation in mass fraction of 0.07 and maximum deviation of 0.22 from the experimental data.     

NUMERICAL SIMULATION OF THE TRANSPORT OF VORTEX RINGS IN AN INITIALLY STRATIFIED FLUID

A numerical study of the formation and transport of vortex rings in a cylindrical tank initially occupied by a thermally stratified fluid is presented. A control-volume based finite-difference technique (Patankar's SIMPLER method) is used. The formation of a vortex ring was simulated by injecting a quantity of a fluid through a tube centrally located near the free surface. Initially, the fluid in the tank was stably thermally stratified, with a low density in the upper half and higher density in the lower half. Buoyancy effects were considered. Modelling aspects are discussed and the formation and transport of a vortex ring in the stratified fluid as well as the resulting mixing characteristics are presented.     

The effect of dyeing on the hygral expansion of worsted fabrics — Part 1

The influence of dyebath pH, dyeing time, levelling agents, dyestuff and anti-setting agent on hygral expansion has been investigated. The incorporation of an anti-setting agent in the dyebath minimised increases in hygral expansion resulting from the dyeing process.     

Absorption of ammonia in a batch bubble column

The batch neutralization of dilute hydrochloric acid by ammonia in a 10.16 cm i.d. bubble column has been examined. Experimental results agree with a diffusion-type mixing model when 5.08 cm disc baffles are present, but agreement is only approximate with 7.62 cm diameter baffles. Axial dispersion coefficients have been determined and compared with published data. A simple batchwise procedure for evaluating bubble columns is suggested.     

Nano- and meso-measurement methods in the study of dielectrics

Measurements of the physical form and morphology of dielectrics and of their chemical characteristics and physical properties have become increasingly important at micron and sub-micron length scales. Such length scales are accessible by different forms of microscopy but scanning probe methods allow both chemical and physical measurements to be made, in addition to morphological and typographical imaging, at nanoscopic and mesoscopic length scales. These measurements are described in this paper with examples drawn from many areas of dielectric research and applications, including polymer morphology, surface and subsurface charge measurements along with the dynamics of charge-decay, surface potential and work function, microelectronics, ferroelectrics, photocharging, dielectric response and nanoscopic relaxation and nanoscopic Raman spectroscopy. The future of scanning probe methods for dielectric measurements is assured but more effort is required to both develop and consolidate this form of nano-metrology.     

Positive DC Coronas with Arbitrary Outer-Conductor Geometries

The two-dimensional Poisson's equation for a positive dc corona in air with arbitrarily-shaped boundaries-is solved using numerical methods. Solutions to the problem give the potential, electric field, space charge density, ion trajectories, and current density for all points in the field of interest, and the corona current flowing to any boundary. Good agreement is obtained between the theory and experimental measurements.     

Issues in the design and use of wearable computers

Wearable computers are fully functional, self-powered, self-contained computers that allow the user to access information anywhere and at any time. In this paper, design issues for wearable computers are discussed, including power considerations, use of input devices, image registration, and the use of wearable computers for the design of smart spaces. Application areas for wearable computers are presented, including medicine, manufacturing, maintenance, and as personal assistants. Finally, future research directions for wearable computers are indicated.     

Evaluating the effectiveness of augmented reality displays for a manual assembly task

The focus of this research was to examine how effectively augmented reality displays, generated with a wearable computer, could be used for aiding an operator performing a manual assembly task. Fifteen subjects were asked to assemble a computer motherboard using four types of instructional media: paper manual, computer-aided, opaque augmented reality display, and see-through augmented reality display. The time of assembly and assembly errors were measured for each type of instructional media, and a questionnaire focusing on usability was administered to each subject at the end of each condition. The results of the experiment indicated that the augmented reality conditions were more effective instructional aids for the assembly task than either the paper instruction manual or the computer-aided instruction. The see-through augmented reality display resulted in the fastest assembly times, followed by the opaque augmented reality display, the computer-aided instruction, and the paper instructions respectively. In addition, subjects made fewer errors using the augmented reality conditions compared to the computer-aided and paper instructional media. However, while the two augmented reality conditions were a more effective instructional media when time for assembly was the response measure, there were still some important usability issues associated with the augmented reality technology that were not present in the non-augmented reality conditions.     

Deep Scalogram Representations for Acoustic Scene Classification

Spectrogram representations of acoustic scenes have achieved competitive performance for acoustic scene classification. Yet, the spectrogram alone does not take into account a substantial amount of time-frequency information. In this study, we present an approach for exploring the benefits of deep scalogram representations, extracted in segments from an audio stream. The approach presented firstly transforms the segmented acoustic scenes into bump and morse scalograms, as well as spectrograms; secondly, the spectrograms or scalograms are sent into pre-trained convolutional neural networks; thirdly, the features extracted from a subsequent fully connected layer are fed into (bidirectional) gated recurrent neural networks, which are followed by a single highway layer and a softmax layer; finally, predictions from these three systems are fused by a margin sampling value strategy. We then evaluate the proposed approach using the acoustic scene classification data set of 2017 IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE). On the evaluation set, an accuracy of 64.0% from bidirectional gated recurrent neural networks is obtained when fusing the spectrogram and the bump scalogram, which is an improvement on the 61.0% baseline result provided by the DCASE 2017 organisers. This result shows that extracted bump scalograms are capable of improving the classification accuracy, when fusing with a spectrogram-based system.      

An analytical framework for local feedforward networks

Interference in neural networks occurs when learning in one area of the input space causes unlearning in another area. Networks that are less susceptible to interference are referred to as spatially local networks. To obtain a better understanding of these properties, a theoretical framework, consisting of a measure of interference and a measure of network localization, is developed. These measures incorporate not only the network weights and architecture but also the learning algorithm. Using this framework to analyze sigmoidal, multilayer perceptron (MLP) networks that employ the backpropagation learning algorithm on the quadratic cost function, we address a familiar misconception that single-hidden-layer sigmoidal networks are inherently nonlocal by demonstrating that given a sufficiently large number of adjustable weights, single-hidden-layer sigmoidal MLPs exist that are arbitrarily local and retain the ability to approximate any continuous function on a compact domain.