Numerical thermal analysis of nanofluid flow through the cooling channels of a polymer electrolyte membrane fuel cell filled with metal foam

Improvement in the cooling system performance by making the temperature distribution uniform is an essential part in design of polymer electrolyte membrane fuel cells. In this paper, we proposed to use water-CuO nanofluid as the coolant fluid and to fill the flow field in the cooling plates of the fuel cell stack by metal foam. We numerically investigated the effect of using nanofluid at different porosities, pore sizes, and thicknesses of metal foam, on the thermal performance of polymer electrolyte membrane fuel cell. The accuracy of present computations is increased by applying a three-dimensional modeling based on finite-volume method, a variable thermal heat flux as the thermal boundary condition, and a two-phase approach to obtain the distribution of nanoparticles volume fraction. The obtained results indicated that at low Reynolds numbers, the role of nanoparticles in improvement of temperature uniformity is more dominant. Moreover, metal foam can reduce the maximum temperature for about 16.5 K and make the temperature distribution uniform in the cooling channel, whereas increase in the pressure drop is not considerable.     

Novel CPG algorithm for tracking fast sudden changes based on tangent rule

ABSTRACT

This paper proposes a novel tracking method to deal with fast changes of solar irradiation and power limit change in order to increase tracking efficiency. This goal is achieved by estimating (i) next operating mode and (ii) next step point with the use of tangent rule in triangle. After every fast response to sudden changes, accurate response phase starts in order to track desired power in each operating mode by adaptive step size. Drift problem in defining next operating mode is eliminated in proposed method by estimating next operating mode, and settling time is decreased to about 25 percent of settling time of other methods by estimating next step point. Simulation and experimental results show the performance of proposed method.     

New potentially antihypertensive peptides liberated in milk during fermentation with selected lactic acid bacteria and kombucha cultures

Compounds with the ability to inhibit angiotensin-converting enzyme (ACE) are used medically to treat human hypertension. The presence of such compounds naturally in food is potentially useful for treating the disease state. The goal of this study was to screen lactic acid bacteria, including species commonly used as dairy starter cultures, for the ability to produce new potent ACE-inhibiting peptides during milk fermentation. Strains of Lactobacillus acidophilus, Lactobacillus casei, Lactobacillus delbrueckii ssp. bulgaricus, Lactobacillus helveticus, Lactobacillus paracasei, Lactococcus lactis, Leuconostoc mesenteroides, and Pediococcus acidilactici were tested in this study. Additionally, a symbiotic consortium of yeast and bacteria, used commercially to produce kombucha tea, was tested. Commercially sterile milk was inoculated with lactic acid bacteria strains and kombucha culture and incubated at 37°C for up to 72 h, and the liberation of ACE-inhibiting compounds during fermentation was monitored. Fermented milk was centrifuged and the supernatant (crude extract) was subjected to ultrafiltration using 3- and 10-kDa cut-off filters. Crude and ultrafiltered extracts were tested for ACE-inhibitory activity. The 10-kDa filtrate resulting from L. casei ATCC 7469 and kombucha culture fermentations (72 h) showed the highest ACE-inhibitory activity. Two-step purification of these filtrates was done using HPLC equipped with a reverse-phase column. Analysis of HPLC-purified fractions by liquid chromatography–mass spectrometry/mass spectrometry identified several new peptides with potent ACE-inhibitory activities. Some of these peptides were synthesized, and their ACE-inhibitory activities were confirmed. Use of organisms producing these unique peptides in food fermentations could contribute positively to human health.     

Preparation of Electrolytic Quasi-Solid-State Nanofibers for Dye-Sensitized Solar Cells

JOM - Nanofibers were prepared from a poly(acrylonitrile-co-vinyl acetate) [P(AN-co-VAC)]/polyvinylidene fluoride (PVDF) blend and used as a quasi-solid (QS) electrolyte for dye-sensitized solar...     

Fabricating and robust artificial neural network modeling nanoscale polyurethane fiber using electrospinning method

With regard to the fact that currently there is no comprehensive method to predict diameter of polyurethane/solvent fiber from electrospinning, in this study, diameter prediction of polyurethane/solvent fiber was conducted using neural networks and an error of 166 nm was observed. This error shows that artificial neural networks (ANNs) can predict diameter of electrospinning polyurethane fibers well. Then, considering weak repeatability nature of electrospinning in fabricating fibers with desired diameter, least mean square is used to improve stability of neural network model that shows an error of 113 nm, which represented better results compared to common ANN. To investigate the effect of each one of parameters affecting fiber diameter, sensitivity analysis was conducted. Along with this predicting model, sensitivity analysis can be used to reduce parameters space before conducting future studies. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45116.     

CEO观点：在新兴市场中，微型蜂窝基站制造业者须保持弹性!

无线营运业者的目标，是提供充分的移动能力及无缝式的漫游，以为使用者确保能永久存取宽带连接功能。目前这个目标可透过移动电话网络达成．但其涵盖率于室内和偏远地区具有局限性，这也意味着并非所有使用者都能得到想要的无缝式涵盖率。     

The Emotional Wardrobe

Since the industrial revolution, fashion and technology have been linked through the textile and manufacturing industries, a relationship that has propelled technical innovation and aesthetic and social change. Today, a new alliance is emerging through the integration of electronic technology and smart materials on the body. This study addresses the integration of technology with clothing from a fashion perspective, and examines its expressive and interactive potential. It proposes the concept of The Emotional Wardrobe: clothing that represents and stimulates emotional response through the interface of technology. It asks if fashion can offer a more personal and provocative definition of self, which actively involves the wearer in a mutable aesthetic identity. A multi-disciplinary framework combines fashion, material science and the real-time, affective computing platform, called AffectiveWare. By merging technology and fashion, The Emotional Wardrobe becomes a poetic interface, shifting emphasis from human–computer interaction to computer-aided, human–human communication.     

Parameter identification of deep-bar induction motors using genetic algorithm

In recent years, the use of three-phase deep-bar induction motors in power systems has increased. Proper modeling and precise parameter identification of the model are essential for motors’ operating analysis. In this paper, among the proposed models of deep-bar induction motors, a model based on two-axis theory is discussed and developed to improve precision. A real coded genetic algorithm estimates the parameters of the model. The accuracy and validity of the model and its identified parameters are verified with the help of a 5.5 kW, 380 V, 50 Hz, 1,450 rpm deep-bar induction motor.