Microstructure evolution in hot worked and Annealed Magnesium Alloy AZ31

The present work examines the microstructure that evolves during the annealing of hot worked magnesium alloy AZ31. First, the influences of deformation and annealing conditions on the microstructures are assessed. It is found that the annealing behaviour is consistent with what one would expect for a recrystallization type reaction. Whilst both the deformation and annealing conditions influence the time required to reach a stable annealed microstructure, the grain size attained is governed solely by the prior deformation conditions employed. At the highest temperature and strain rate examined, the rate of recrystallization is quite high and the grain size was found to be approximately double when annealed for only 1 s prior to quenching. Finally, semi-empirical equations are developed to predict the kinetics of recrystallization, as well as the evolution of grain size, during annealing.

Solar photocatalytic oxidation system for colour removal in dyeing effluent

The effectiveness of various photocatalysts including titanium dioxide and other oxidants was investigated in a solar powered UV photocatalytic oxidation system for colour removal in dyeing effluent. A semi-empirical constant model and guidelines were developed to assist the design and to evaluate the full scale of the system.

Optimization of mixing parameters through a water model for metal matrix composites synthesis

The influence of the mixing parameters on the synthesis of Al–SiCp reinforced metal matrix composites (MMCs) by the stir casting technique is investigated through a water model. The effects of some important mixing parameters such as impeller blade angle, rotating speed, direction of impeller rotation and effect of baffles are investigated and optimized. The results have shown that the axial concentration variation of natural graphite during stirring in the presence of four vertical baffles is 1.0 wt% against in the absence of baffles it is increased to 2.3 wt%. The variations observed in natural graphite concentration in water during mixing are in close agreement with the earlier modeling and limited experimental studies reported on the real molten aluminum–SiC system. Semi-empirical correlations arrived at between the dimensionless numbers for stirred water – natural graphite slurries are Po = Re−0.0545 Fr−1.099 and Po = Re−0.0219 Fr−1.0382 for clockwise and counter clockwise rotation respectively.

A new solution method for homogenization of effective properties of electromagnetic honeycombs

In this paper, an analytical model and its new numerical solution using the homogenization method are developed to determine the effective electromagnetic characteristics of honeycombs. Based on the proposed solution method, the electromagnetic properties are obtained by employing the multi-scale homogenization theory and periodical electric (magnetic) potential boundary conditions. Further, the effect of geometry of honeycomb's unit cell on effective electromagnetic properties is investigated with the use of the proposed method. The numerical results are compared with analytic results using the Smith-Scarpa's semi-empirical formula.

Mathematical modelling of deep bed filtration

Numerous mathematical models have been developed to evaluate both initial and transient stage removal efficiency of deep bed filters. Microscopic models either using trajectory analysis or convective-diffusion equations were used to compute the initial removal efficiency. These models predicted the removal efficiency under favorable filtration conditions quantitatively, but failed to predict the removal efficiency under unfavorable conditions. They underestimated the removal efficiency under unfavorable conditions. Thus, semi-empirical formulations were developed to compute initial removal efficiencies under unfavorable conditions. Also, correction for the adhesion of particles onto filter grains improved the results obtained for removal efficiency from the trajectory analysis. Macroscopic models were used to predict the transient stage removal efficiency of deep bed filters. O’Melia and Ali’s model assumed that the particle removal is due to filter grains as well as the particles that are already deposited onto the filter grain. Thus, semi-empirical models were used to predict the ripening of filtration. Several modifications were made to the model developed by O’Melia and Ali to predict the deterioration of particle removal during the transient stages of filtration. Models considering the removal of particles under favorable conditions and the accumulation of charges on the filter grains during the transient stages were also developed. This paper evaluates those models and their applicability under different operating conditions of filtration.

Mathematical modelling of downflow floating medium filter (DFF) with in-line flocculation arrangement

A laboratory-scale set-up consisting of rapid mixing device and floating medium filter was used to study the use of a downflow floating medium filter (DFF) with an in-line flocculation arrangement as a static flocculator and a prefilter. The semi-empirical mathematical model formulated incorporates flocculation within the filter, particle/floc attachment onto the filter and the detachment of flocs from the medium. The mathematical model for filtration takes into account the expansion of the filter bed. The removal efficiency of DFF and headless development were successfully simulated for different conditions of filtration velocity, filter depth and influent suspended solids (SS). The values of attachment coefficient a(p)β and headless coefficient β1 were found to be independent of filtration velocity, filter depth and influent SS concentration.

Deep bed filtration : mathematical models and observations

Numerous mathematical models have been developed to evaluate both initial and transient stage removal efficiency of deep bed filters. Microscopic models either using trajectory analysis or convective diffusion equations were used to compute the initial removal efficiency. These models predicted the removal efficiency under favorable filtration conditions quantitatively, but failed to predict the removal efficiency under unfavorable conditions. They underestimated the removal efficiency under unfavorable conditions. Thus, semi-empirical formulations were developed to compute initial removal efficiencies under unfavorable conditions. Also, correction for the adhesion of particles onto filter grains improved the results obtained for removal efficiency from the trajectory analysis. Macroscopic models were used to predict the transient stage removal efficiency of deep bed filters. The O’Melia and Ali1 model assumed that the particle removal is due to filter grains as well as the particles that are already deposited onto the filter grain. Thus, semi-empirical models were used to predict the ripening of filtration. Several modifications were made to the model developed by O’Melia and Ali to predict the deterioration of particle removal during the transient stages of filtration. Models considering the removal of particles under favorable conditions and the accumulation of charges on the filter grains during the transient stages were also developed. This article evaluates those models and their applicability under different operating conditions of filtration.

Influence of particle size and size distribution in deep bed filtration and dynamic microfiltration

Particle size and size distribution is an important parameter in solid liquid separation process especially in granular bed filtration and in dynamic microfiltration. This paper discusses their effects on the above processes from extensive experimental data obtained. In granular bed filtration, the experimental results showed that the initial efficiency follows the pattern reported by previous experimental and theoretical studies, i.e., lower efficiency for particles which fall in the range of critical size of 1 m. However, the particle removal during the transient stage increased with an increase in particle size for the range of sizes studied. An attempt was made to quantify these effects in granular bed filtration using semi-empirical approach. In dynamic membrane filtration also, the particle size plays a major role in the retention. However, despite the relative thickness of the membrane (compared to particle size) dynamic microfiltration appears more as a sieving process; the retention is mainly related to the largest pore size.

Investigation on decomposition behavior of austenite under continuous cooling in vanadium microalloyed steel (30MSV6)

In the present study, investigations are focused on microstructural evolution and the resulting hardness during continuous cooling transformation (CCT) in a commercial vanadium microalloyed steel (30MSV6). Furthermore, the effects of cooling rate and austenite grain size (AGS) on CCT behavior of the steel have been studied by employing high-resolution dilatometry. Quantitative metallography accompanied with scanning electron microscopy (SEM) has efficiently confirmed the dilatometric measurements of transformation kinetics and austenite decomposition products. A semi-empirical model has been proposed for prediction of microstructural development during austenite decomposition of the steel and the resultant hardness. The model consists of 8 sub-models including ferrite transformation start temperature, ferrite growth, pearlite start temperature, pearlite growth, bainite start temperature, bainite growth, martensite start temperature and hardness. The transformed fractions of ferrite, pearlite and bainite have been described using semi-empirical Johnson-Mehl-Avrami-Kolmogorov (JMAK) approach in combination with Scheil's equation of additivity. The JMAK rate parameter for bainite has been formulated using a diffusion-controlled model. Predictions of the proposed model were found to be in close agreement with the experimental measurements.

Mixing vector construction for single channel semi-blind source separation using empirical mode decomposition

The Empirical Mode Decomposition (EMD) method is a commonly used method for solving the problem of single channel blind source separation (SCBSS) in signal processing. However, the mixing vector of SCBSS, which is the base of the EMD method, has not yet been effectively constructed. The mixing vector reflects the weights of original signal sources that form the single channel blind signal source. In this paper, we propose a novel method to construct a mixing vector for a single channel blind signal source to approximate the actual mixing vector in terms of keeping the same ratios between signal weights. The constructed mixing vector can be used to improve signal separations. Our method incorporates the adaptive filter, least square method, EMD method and signal source samples to construct the mixing vector. Experimental tests using audio signal evaluations were conducted and the results indicated that our method can improve the similar values of sources energy ratio from 0.2644 to 0.8366. This kind of recognition is very important in weak signal detection.

Semi-supervised and compound classification of network traffic

This paper presents a new semi-supervised method to effectively improve traffic classification performance when few supervised training data are available. Existing semi supervised methods label a large proportion of testing flows as unknown flows due to limited supervised information, which severely affects the classification performance. To address this problem, we propose to incorporate flow correlation into both training and testing stages. At the training stage, we make use of flow correlation to extend the supervised data set by automatically labeling unlabeled flows according to their correlation to the pre-labeled flows. Consequently, the traffic classifier has better performance due to the extended size and quality of the supervised data sets. At the testing stage, the correlated flows are identified and classified jointly by combining their individual predictions, so as to further boost the classification accuracy. The empirical study on the real-world network traffic shows that the proposed method outperforms the state-of-the-art flow statistical feature based classification methods.

Semi-supervised classification based on subspace sparse representation

Graph plays an important role in graph-based semi-supervised classification. However, due to noisy and redundant features in high-dimensional data, it is not a trivial job to construct a well-structured graph on high-dimensional samples. In this paper, we take advantage of sparse representation in random subspaces for graph construction and propose a method called Semi-Supervised Classification based on Subspace Sparse Representation, SSC-SSR in short. SSC-SSR first generates several random subspaces from the original space and then seeks sparse representation coefficients in these subspaces. Next, it trains semi-supervised linear classifiers on graphs that are constructed by these coefficients. Finally, it combines these classifiers into an ensemble classifier by minimizing a linear regression problem. Unlike traditional graph-based semi-supervised classification methods, the graphs of SSC-SSR are data-driven instead of man-made in advance. Empirical study on face images classification tasks demonstrates that SSC-SSR not only has superior recognition performance with respect to competitive methods, but also has wide ranges of effective input parameters.

Semi-supervised and compound classification of network traffic

This paper presents a new semi-supervised method to effectively improve traffic classification performance when very few supervised training data are available. Existing semisupervised methods label a large proportion of testing flows as unknown flows due to limited supervised information, which severely affects the classification performance. To address this problem, we propose to incorporate flow correlation into both training and testing stages. At the training stage, we make use of flow correlation to extend the supervised data set by automatically labelling unlabelled flows according to their correlation to the pre-labelled flows. Consequently, a traffic classifier achieves excellent performance because of the enhanced training data set. At the testing stage, the correlated flows are identified and classified jointly by combining their individual predictions, so as to further boost the classification accuracy. The empirical study on the real-world network traffic shows that the proposed method significantly outperforms the state-of-the-art flow statistical feature based classification methods. Copyright © 2012 Inderscience Enterprises Ltd.