Study on forcing schemes in the thermal lattice Boltzmann method for simulation of natural convection flow problems

The present study addresses the effect of various schemes for applying an external force term on the accuracy and performance of the thermal lattice Boltzmann method (LBM) for simulation of free convection problems. Herein, the forcing schemes of Luo, shifted velocity method, Guo, and exact difference method are applied by considering three velocity discrete models of D2Q4, D2Q5, and D2Q9. The accuracy and performance of these schemes are evaluated with the simulation of three natural convection problems, namely, free convection in a closed cavity, in a square enclosure with a hot obstacle inside, and the Rayleigh-Benard problem. The obtained results based on the present thermal LBM with different forcing schemes and velocity discrete models are compared with the existing experimental and numerical data in the literature. This comparison study indicates that imposing all employed forcing schemes leads to similar performance for the simulation of free convection problems studied at the middle range of Rayleigh numbers. It is found that the Luo forcing scheme is simple for implementation in comparison with the other three forcing schemes and provides the results with acceptable accuracy at moderate Rayleigh numbers. At higher Rayleigh numbers, however, the Guo scheme is not only numerically stable but a more precise forcing scheme in comparison with the other three methods. It is illustrated that employing the discrete velocity model of D2Q4 has more appropriate numerical stability along with less computational cost in comparison with two other discrete velocity models for simulation of natural convection heat transfer.     

Design,Synthesis, and Fabrication of Chitosan/Hydroxyapatite Composite Scaffold for Use as Bone Replacement Tissue by Sol–Gel Method

Journal of Inorganic and Organometallic Polymers and Materials - Injuries or bone defects are phenomena that are harmful to human health. In the field of bone scaffold tissue engineering,...     

Effect of carbon nanotube network morphology on thin film transistor performance

The properties of electronic devices based on carbon nanotube networks (CNTNs) depend on the carbon nanotube (CNT) deposition method used, which can yield a range of network morphologies. Here, we synthesize single-walled CNTs using an aerosol (floating catalyst) chemical vapor deposition process and deposit CNTs at room temperature onto substrates as random networks with various morphologies. We use four CNT deposition techniques: electrostatic or thermal precipitation, and filtration through a filter followed by press transfer or dissolving the filter. We study the mobility using pulsed measurements to avoid hysteresis, the on/off ratio, and the electrical noise properties of the CNTNs, and correlate them to the network morphology through careful imaging. Among the four deposition methods thermal precipitation is found to be a novel approach to prepare high-performance, partially aligned CNTNs that are dry-deposited directly after their synthesis. Our results provide new insight into the role of the network morphologies and offer paths towards tunable transport properties in CNT thin film transistors.      

Thermally Activated Aromatic Ionic Dopants (TA-AIDs) Enabling Stable Doping,Orthogonal Processing and Direct Patterning

Doping plays a critical role in organic electronics, and dopant design has been central in the development of functional and stable doping. In this study, there is departure from conventional molecular dopants and a new class of dopants are reported – aromatic ionic dopants (AIDs). AIDs consist of a pair of aromatic cation and anion that are responsible for molecular doping reaction and charge balancing separately. It is shown that the first AID made from cycloheptatrienyl (tropylium) cation and pentacyanocyclopentadienide anion (PCCp), abbreviated as T-PCCp, can function as an effective p-type dopant to dope polydioxythiophenes. Here, tropylium cation induces the doping reaction while the PCCp anion stabilizes the generated polarons and bipolarons. With T-PCCp, a highly doped (≈120 S/cm) and stable system is achieved up to 150 °C, an orthogonal (sequential)solution processing resulting from the immiscibility of the dopant and the polymer host, and a high-resolution direct micropatterning with laser writing resulted from a thermally activated doping process.     

A new nitrite ionic liquid (IL-ONO) as a nitrosonium source for the efficient diazotization of aniline derivatives and in-situ synthesis of azo dyes

A new task-specific nitrite containing ionic liquid derived from the O-nitrosation of N-methyl-N-hydroxybutylimidazolinium chloride was synthesized and used as a source of nitrosonium ion to affect the efficient diazotization of arylamines. The diazonium salts thus obtained were coupled, using standard experimental procedures, to a range of tertiary anilines, phenols and naphthols to afford the requisite azo dyes in good yield. The diazotization and subsequent azo-coupling generated the related azo dyes at 0-5 °C in short reaction times with a simple experimental procedure.     

On the self-scheduling of a power producer in uncertain trading environments

This work presents a scenario-based approach to the self-scheduling problem of a price taker power producer in a DA market. It concentrates on three categories of uncertainty including price, forced outage and generation reallocation and analyses their effects on the producer revenue. To tackle the uncertainties a set of price scenarios are so generated that their means and covariance matrix are the same as the base-case scenario. Forced outage and generation reallocation of generator for each price scenario are appropriately modeled through a probabilistic methodology. In this work Downside Risk (DR) is employed as the risk measure which quantifies the downside violations from a specified target. A risk-constrained self-scheduling problem is therefore formulated and solved as a mixed integer linear programming problem. Numerical results for a case study are discussed.     

A hardware/software platform for QoS bridging over multi-chip NoC-based systems

Recent embedded systems integrate a growing number of intellectual property cores into increasingly large designs. Implementation, prototyping, and verification of such large systems has become very challenging. One of the reasons is that chips/FPGAs resources are limited and therefore it is not always possible to implement the whole design in the traditional system-on-a-chip solutions. The state-of-the-art is to partition such systems into smaller sub-systems to implement each on a separate chip. Consequently, it requires interconnecting separate chips/FPGAs. Since Networks-on-Chip (NoCs) have become common interconnection solutions in embedded designs, we propose to bridge NoC-based SoCs enabling a generic multi-chip systems interconnection. In this context, the contribution of this paper is threefold, (i) we explore the NoC protocol stack to determine the best layer for implementing the off-chip bridge, (ii) we propose a generic hardware architecture for the bridge, and (iii) we develop a new software architecture enabling seamless configuration and communication of multi-chip NoC-based SoCs. Finally, we demonstrate performance, i.e., bandwidth and latency, of the bridge in a multi-FPGA platform, while the bridge guarantees QoS of traffic. The synthesis results indicate the implementation area cost of the bridge is only 1% of Xilinx Virtex6 FPGA.     

Learning to predict ice accretion on electric power lines

Ice accretion on power transmission and distribution lines is one of the major causes of power grid outages in northern regions. While such icing events are rare, they are very costly. Thus, it would be useful to predict how much ice will accumulate. Many current ice accretion forecasting systems use precipitation-type prediction and physical ice accretion models. These systems are based on expert knowledge and experimentations. An alternative strategy is to learn the patterns of ice accretion based on observations of previous events. This paper presents two different forecasting systems that are obtained by applying the learning algorithm of Support Vector Machines to the outputs of a Numerical Weather Prediction model. The first forecasting system relies on an icing model, just as the previous algorithms do. The second system learns an effective forecasting model directly from meteorological features. We use a rich data set of eight different icing events (from 2002 to 2008) to empirically compare the performance of the various ice accretion forecasting systems. Several experiments are conducted to investigate the effectiveness of the forecasting algorithms. Results indicate that the proposed forecasting system is significantly more accurate than other state-of-the-art algorithms.