Solubility Enhancement of Fe in ZnO Nanoparticles Prepared by Co-Precipitation Method

Journal of Superconductivity and Novel Magnetism - Crystalline ZnO offers an excellent host matrix to create a dilute magnetic semiconductor (DMS) owing to its facile Zn-atom substitution with the...     

Optimal design of drainage channel geometry parameters in vane demister liquid–gas separators

Vane liquid–gas demisters are widely used as one of the most efficient separators. To achieve higher liquid disposal and to avoid flooding, vanes are enhanced with drainage channels. In this research, the effects of drainage channel geometry parameters on the droplet removal efficiency have been investigated applying CFD techniques. The observed parameters are channel angle, channel height and channel length. The gas phase flow field was determined by the Eulerian method and the droplet flow field and trajectories were computed applying the Lagrangian method. The turbulent dispersion of the droplets was modeled using the discrete random walk (DRW) approach. The CFD simulation results indicate that by applying DRW model, the droplet separation efficiency predictions for small droplets are closer to the corresponding experimental data. The CFD simulation results showed that in the vane, enhanced with drainage channels, fewer low velocity sectors were observed in the gas flow field due to more turbulence. Consequently, the droplets had a higher chance of hitting the vane walls leading to higher separation efficiency. On the other hand, the parameters affect the liquid droplet trajectory leading to the changes in separation efficiency and hydrodynamic characteristic of the vane. To attain the overall optimum geometry of the drainage channel, all three geometry parameters were simultaneously studied employing 27 CFD simulation cases. To interpolate the overall optimal geometry a surface methodology method was used to fit the achieved CFD simulation data and finally a polynomial equation was proposed.     

3D FEM analysis,dynamic modeling,and performance assessment of transverse flux PMSG for small‐scale gearless wind energy conversion systems

The transverse flux permanent magnet synchronous generator has a great potential for use in direct‐drive wind energy conversion systems due to its large pole numbers, high torque, and power density. This research work develops dynamic model of a single‐side transverse flux permanent magnet synchronous generator for use in a small‐scale gearless wind energy conversion system. For acquiring the parameters of the considered generator, required for dynamic modeling, 3D finite element model of the machine is developed and analyzed in both magneto‐static and transient modes. Field‐oriented control approach is employed for tracking maximum power point from the variable wind speed. The simulation results illustrate an accurate response of the system to the wind speed variation and proper performance of the developed dynamic model and control approach of the system. Copyright © 2015 John Wiley & Sons, Ltd.     

Deep Multimodal Fusion: A Hybrid Approach

We propose a novel hybrid model that exploits the strength of discriminative classifiers along with the representation power of generative models. Our focus is on detecting multimodal events in time varying sequences as well as generating missing data in any of the modalities. Discriminative classifiers have been shown to achieve higher performances than the corresponding generative likelihood-based classifiers. On the other hand, generative models learn a rich informative space which allows for data generation and joint feature representation that discriminative models lack. We propose a new model that jointly optimizes the representation space using a hybrid energy function. We employ a Restricted Boltzmann Machines (RBMs) based model to learn a shared representation across multiple modalities with time varying data. The Conditional RBMs (CRBMs) is an extension of the RBM model that takes into account short term temporal phenomena. The hybrid model involves augmenting CRBMs with a discriminative component for classification. For these purposes we propose a novel Multimodal Discriminative CRBMs (MMDCRBMs) model. First, we train the MMDCRBMs model using labeled data by training each modality, followed by training a fusion layer. Second, we exploit the generative capability of MMDCRBMs to activate the trained model so as to generate the lower-level data corresponding to the specific label that closely matches the actual input data. We evaluate our approach on ChaLearn dataset, audio-mocap, as well as the Tower Game dataset, mocap-mocap as well as three multimodal toy datasets. We report classification accuracy, generation accuracy, and localization accuracy and demonstrate its superiority compared to the state-of-the-art methods.     

A 0.5–1 GHz single stage linear-in-decibel VGA with 80 dB gain range in 0.18 μm CMOS

Communication systems require a wide gain range. For example the code-division multiple access system (CDMA) requires more than 80 dB of gain range so that, many variable gain amplifiers (VGAs) must be used, resulting in high power consumption and low linearity because of VGA non-linearity factors. In this paper, a one-stage VGA in 0.18 μm technology is presented. The VGA based on the class AB power amplifier is designed and simulated for a high linearity and an 80 dB tuning range. For the linear-in-decibel tuning range, transistors in sub-threshold region is used. The current control circuit of the VGA changes gain continuously from ?68 to 18 dB at 0.5 GHz and ?60 to 20 dB at 1 GHz with gain error of less than 2 dB. The power consumption enjoys a highest value about 13.5 mW in the maximum gain and P1dB is also about ?3.4 dBm at 0.5 GHz and 2.2 dBm at 1 GHz.     

High-performance low-leakage regions of nano-scaled CMOS digital gates under variations of threshold voltage and mobility

We propose a modeling methodology for both leakage power consumption and delay of basic CMOS digital gates in the presence of threshold voltage and mobility variations. The key parameters in determining the leakage and delay are OFF and ON currents, respectively, which are both affected by the variation of the threshold voltage. Additionally, the current is a strong function of mobility. The proposed methodology relies on a proper modeling of the threshold voltage and mobility variations, which may be induced by any source. Using this model, in the plane of threshold voltage and mobility, we determine regions for different combinations of performance (speed) and leakage. Based on these regions, we discuss the trade-off between leakage and delay where the leakage-delay-product is the optimization objective. To assess the accuracy of the proposed model, we compare its predictions with those of HSPICE simulations for both basic digital gates and ISCAS85 benchmark circuits in 45-, 65-, and 90-nm technologies.     

Modeling gas oil spray coalescence and vaporization in gas solid riser reactor

The sprayed feed droplet behavior, including coalescence and vaporization into gas–solid flow, is complex especially near the atomizer region in fluid catalytic cracking (FCC) riser reactor. A three dimensional CFD model of the riser reactor has been developed, which takes into the account three phase hydrodynamics, heat transfer and evaporation of the liquid droplets into a gas–solid flow as well as phase interactions. A hybrid Eulerian–Lagrangian approach was applied to numerically simulate the collision and vaporization of gas oil droplets in the gas–solid fluidized bed. This numerical simulation accounts the possibility of coalescence of feed spray droplets in computing the trajectories and its impact on droplet penetration in the reactor. The modeling result shows that droplet coalescence mainly occurs at the initial part of the atomizing region and where three phase flow hits the reactor wall and bounces back. The model has the ability of inspecting the effects of feed injector geometry on the overall reactor hydrodynamic and heat transfer. The CFD simulation results showed that the evaporated droplet gas caused higher local velocities of the gas and solid particles and gas–solid flow temperature reduction.     

A connectivity based clustering algorithm with application to VLSI circuit partitioning

Circuit partitioning is a fundamental problem in very large-scale integration (VLSI) physical design automation. In this brief, we present a new connectivity-based clustering algorithm for VLSI circuit partitioning. The proposed clustering method focuses on capturing natural clusters in a circuit, i.e., the groups of cells that are highly interconnected in a circuit. Therefore, the proposed clustering method can reduce the size of large-scale partitioning problems without losing partitioning solution qualities. The performance of the proposed clustering algorithm is evaluated on a standard set of partitioning benchmarks-ISPD98 benchmark suite. The experimental results show that by applying the proposed clustering algorithm, the previously reported best partitioning solutions from state-of-the-art partitioners are further improved.