Microstructure characterization,mechanical properties,compressibility and sintering behavior of Al-B4C nanocomposite powders

In the present work, Al-xB₄C nanocomposite (x = 0, 1, 2, 3, 4 and 5 in wt%, having the average B₄C size of 50 nm) were prepared using a high-energy ball mill. The milling times up to 16 h were applied. Then, the microstructural evolutions, mechanical properties, compressibility and sintering behavior of nanocomposites were investigated. The changes in powders morphology and microstructure during the milling process were characterized by laser diffraction particle size analyzer (LDA), SEM, XRD, EDS and TEM techniques. Compressibility and sintering behavior of milled powders compacted under different pressures (100–900 MPa) and at different sintering temperatures (500, 550 and 600 °C) were also studied. The pressing behavior of the nanocomposites was analyzed using linear compaction equations developed by Heckel, Panelli-Filho and Ge. The results showed the significant effects of B₄C amounts and sintering temperatures on the compressibility and sintering behavior of nanocomposites. The increase in the B₄C amount led to a decrease in both the compressibility rate and the sinterability of specimens. The maximum compression strength of 265 MPa and Vickers hardness of 165 VHN were obtained for Al-5 wt.% B₄C nanocomposite milled for 16 h followed by sintering at 600 °C.     

Investigating the flow structures in semi-cylindrical bubbling fluidized bed using pressure fluctuation signals

Hydrodynamic characteristics of a gas-solid semi-cylindrical fluidized bed was experimentally investigated and compared with that of a cylindrical bed by analysis of pressure fluctuations. Pressure fluctuations were analyzed in time and frequency domains using standard deviation, power spectral density function and discrete wavelet transform methods. Experiments were carried out in two semi-cylindrical and cylindrical fluidized beds of 14?cm in diameter each, operating in the bubbling fluidization regime at ambient pressure and temperature. Both beds were filled with glass beads of various sizes (120, 290 and 450?µm). The superficial gas velocity was varied in the range of 0.2–0.8?m/s. Results showed that although the minimum fluidization velocity is influenced by the particle size, it is not affected by the geometry of the bed. It was shown that the hydrodynamics of both beds are very similar and the difference is negligible. Number of large bubbles is slightly larger in the semi-cylindrical bed as compared with the cylindrical bed. Also, increase in the particle size and superficial gas velocity result in a greater difference between the number of large bubbles in both beds and the number of large bubbles in the semi-cylindrical bed increases slightly faster than in the cylindrical bed.     

Optimal power flow of HVDC system using teaching–learning-based optimization algorithm

In this paper, a solution to the optimal power flow (OPF) problem in electrical power networks is presented considering high voltage direct current (HVDC) link. Furthermore, the effect of HVDC link converters on the active and reactive power is evaluated. An objective function is developed for minimizing power loss and improving voltage profile. Gradient-based optimization techniques are not viable due to high number of OPF equations, their complexity and equality and inequality constraints. Hence, an efficient global optimization method is used based on teaching–learning-based optimization (TLBO) algorithm. The performance of the suggested method is evaluated on a 5-bus PJM network and compared with other algorithms such as particle swarm optimization, shuffled frog-leaping algorithm and nonlinear programming. The results are promising and show the effectiveness and robustness of TLBO method.

     

Loss estimation and control mechanism in bufferless optical packet-switched networks based on multilayer perceptron

Artificial neural networks (ANNs) are well-known estimators for the output of broad range of complex systems and functions. In this paper, a common ANN architecture called multilayer perceptron (MLP) is used as a fast optical packet loss rate (OPLR) estimator for bufferless optical packet-switched (OPS) networks. Considering average loads at the ingress switches of an OPS network, the proposed estimator estimates total OPLR as well as ingress OPLRs (the OPLR of optical packets sent from individual ingress switches). Moreover, a traffic policing algorithm called OPLRC is proposed to control ingress OPLRs in bufferless slotted OPS networks with asymmetric loads. OPLRC is a centralized greedy algorithm which uses estimated ingress OPLRs of a trained MLP to tag some optical packets at the ingress switches as eligible for drop at the core switches in case of contention. This will control ingress OPLRs of un-tagged optical packets within the specified limits while giving some chance for tagged optical packets to reach their destinations. Eventually, the accuracy of the proposed estimator along with the performance of the proposed algorithm is evaluated by extensive simulations. In terms of the algorithm, the results show that OPLRC is capable of controlling ingress OPLRs of un-tagged optical packets with an acceptable accuracy.     

Volumetric quantification of atherosclerotic plaque in CT considering partial volume effect

Coronary artery calcification (CAC) is quantified based on a computed tomography (CT) scan image. A calcified region is identified. Modified expectation maximization (MEM) of a statistical model for the calcified and background material is used to estimate the partial calcium content of the voxels. The algorithm limits the region over which MEM is performed. By using MEM, the statistical properties of the model are iteratively updated based on the calculated resultant calcium distribution from the previous iteration. The estimated statistical properties are used to generate a map of the partial calcium content in the calcified region. The volume of calcium in the calcified region is determined based on the map. The experimental results on a cardiac phantom, scanned 90 times using 15 different protocols, demonstrate that the proposed method is less sensitive to partial volume effect and noise, with average error of 9.5% (standard deviation (SD) of 5-7mm(3)) compared with 67% (SD of 3-20mm(3)) for conventional techniques. The high reproducibility of the proposed method for 35 patients, scanned twice using the same protocol at a minimum interval of 10 min, shows that the method provides 2-3 times lower interscan variation than conventional techniques.     

J-integral evaluation of austenitic-martensitic functionally graded steel in plates weakened by U-notches

In this paper, a numerical method for J-integral evaluation of plates weakened by U-notches for brittle or quasi-brittle functionally graded steel (FGS) has been proposed. The material contains austenite phase in addition to martensite layer produced by electroslag remelting (ESR). The Young’s modulus and the Poisson’s ratio have been assumed to be constant, while other mechanical properties vary exponentially along the specimen width. The effect of notch depth on the J-integral and the critical fracture load has been studied. A comparison of the J-integral between functionally graded and homogeneous steels was made, where the notch tip in the functionally graded steel is situated in a layer with same mechanical properties as the homogeneous steel.     

Effects of pre-heating temperature and formulation on porosity,moisture content,and fat content of fried batters

The effect of pre-heating temperatures on moisture and fat contents, and porosity of fried batters was studied. Batter pre-heated at 60 °C showed higher moisture content, lower fat content and lower porosity than non-pre-heated batter and batters pre-heated at 70 and 80 °C. Moisture content, fat content, and porosity at 4 min frying for batters with different pre-heating treatments ranged from 35.08 to 39.37, 3.92 to 5.16, and 13.14 to 45.31 %, respectively. Because of significant reduction in fat content, 60 °C pre-heating temperature was chosen to study the effect of batter formulations on moisture and fat contents, and porosity. Different wheat to rice flour ratios were prepared, and then each batter was pre-heated at 60 °C. Batters with higher wheat flour content showed higher moisture content, and lower fat content and porosity than batters with higher rice flour.     

Kinetics study of CO hydrogenation on a precipitated iron catalyst

The kinetics of the gas–solid Fischer–Tropsch synthesis over a precipitated Fe/Cu/La/SiO₂ catalyst was studied in a well mixed, continuous spinning basket reactor. A wide range of synthesis gas conversions have been obtained by varying experimental conditions. Several Langmuir–Hinshelwood–Hougen–Watson type rate equations were derived based on detailed sets of possible reaction mechanisms originating from the carbide, enolic and combined enol/carbide mechanisms. Three models for the Fischer–Tropsch reaction rate were fitted to the experimental reaction rates. Kinetic parameters of models are determined using the genetic algorithm approach (GA), followed by the Levenberg–Marquardt (LM) method to make refined optimization, and are validated by means of statistical analysis. Simulations using the optimal kinetic models derived showed good agreement with experimental data.     

Signal Synchronization Using a Flicker Reduction and Denoising Algorithm for Video‐Signal Optical Interconnect

A video signal through a high‐density optical link has been demonstrated to show the reliability of optical link for high‐data‐rate transmission. To reduce optical point‐to‐point links, an electrical link has been utilized for control and clock signaling. The latency and flicker with background noise occurred during the transferring of data across the optical link due to electrical‐to‐optical with optical‐to‐electrical conversions. The proposed synchronization technology combined with a flicker and denoising algorithm has given good results and can be applied in high‐definition serial data interface (HD‐SDI), ultra‐HD‐SDI, and HD multimedia interface transmission system applications.     

Simulated Micromechanical Models Using Artificial Neural Networks

A new method, termed simulated micromechanical models using artificial neural networks (MMANN), is proposed to generate micromechanical material models for nonlinear and damage behavior of heterogeneous materials. Artificial neural networks (ANN) are trained with results from detailed nonlinear finite-element (FE) analyses of a repeating unit cell (UC), with and without induced damage, e.g., voids or cracks between the fiber and matrix phases. The FE simulations are used to form the effective stress-strain response for a unit cell with different geometry and damage parameters. The FE analyses are performed for a relatively small number of applied strain paths and damage parameters. It is shown that MMANN material models of this type exhibit many interesting features, including different tension and compression response, that are usually difficult to model by conventional micromechanical approaches. MMANN material models can be easily applied in a displacement-based FE for nonlinear analysis of composite structures. Application examples are shown where micromodels are generated to represent the homogenized nonlinear multiaxial response of a unidirectional composite with and without damage. In the case of analysis with damage growth, thermodynamics with irreversible processes (TIP) is used to derive the response of an equivalent homogenized damage medium with evolution equations for damage. The proposed damage formulation incorporates the generalizations generated by the MMANN method for stresses and other possible responses from analysis results of unit cells with fixed levels of damage.