Fluttering energy harvesters in the wind: A review

The growing area of harvesting energy by aerodynamically induced flutter in a fluid stream is reviewed. Numerous approaches were found to understand, demonstrate and [sometimes] optimise harvester performance based on Movement-Induced or Extraneously Induced Excitation. Almost all research was conducted in smooth, unidirectional flow domains; either experimental or computational. The power outputs were found to be very low when compared to conventional wind turbines, but potential advantages could be lower noise levels. A consideration of the likely outdoor environment for fluttering harvesters revealed that the flow would be highly turbulent and having a mean flow angle in the horizontal plane that could approach a harvester from any direction. Whilst some multiple harvester systems in smooth, well-aligned flow found enhanced efficiency (due to beneficial wake interaction) this would require an invariant flow approach angle. It was concluded that further work needs to be performed to find a universally accepted metric for efficiency and to understand the effects of the realities of the outdoors, including the highly variable and turbulent flow conditions likely to be experienced

Occurrences of pathogenic Vibrio parahaemolyticus from Vellar estuary and shrimp ponds

Vibrio parahaemolyticus is the predominant seafood pathogen associated with human gastroenteritis. Samples were collected from Vellar estuary, shrimp ponds and shrimp for characterization of V. parahaemolyticus. A total of 26 blue green centre (BG) Vibrio strains were isolated and characterized through biochemical tests, toxR gene and 16S rRNA gene sequencing. Based on pathogenic characteristics, six strains were confirmed as pathogenic V. parahaemolyticus. This report implies that preventative measure must be taken before consumption of fish and shrimp.Keywords: Estuary, shrimp pond, Vibrio parahaemolyticus, protease, haemolysis, toxR geneAfrican Journal of Biotechnology Vol. 12(14), pp. 1624-162

A review of passive wireless sensors for structural health monitoring

Wireless sensors for Structural Health Monitoring (SHM) is an emerging new technology that promises to overcome many disadvantages pertinent to conventional, wired sensors. The broad field of SHM has experienced significant growth over the past two decades, with several notable developments in the area of sensors such as piezoelectric sensors and optical fibre sensors. Although significant improvements have been made on damage monitoring techniques using these smart sensors, wiring remains a significant challenge to the practical implementation of these technologies. Wireless SHM has recently attracted the attention of researchers towards un-powered and more effective passive wireless sensors. This article presents a review of some of the underlying technologies in the field of wireless sensors for SHM - with a focus on the research progress towards the development of simple, powerless, yet effective and robust wireless damage detection sensors. This review examines the development of passive wireless sensors in two different categories: (1) use of oscillating circuits with the help of inductors, capacitors and resistors for damage detection; and (2) use of antennas, Radio Frequency Identification (RFID) tags and metamaterial resonators as strain sensors for wireless damage monitoring. An assessment of these electromagnetic techniques is presented and the key issues involved in their respective design configurations are discussed

Vibrio alginolytigus causing shell disease in the mud crab Scylla serrata (Forskal 1775)

1359-1363Scylla serrata is one of the most cultured mud crab species in the aquaculture which is also susceptible to shell disease. In the present study, Vibrio alginolyticus MF680287.1 caused by shell disease and isolated from infected mud crab S. serrata grow out pond located at Mahendrapalli, Nagapattinam District, Tamil Nadu, India. Further, gross observation of infected mud crab showed shell lesion on the dorsal carapace. The histological examination of normal and diseased mud crab S. serrata carapace and gills was conducted. The shell lesion affected in the S. serrata carapace layers showed loss of membrenous layer and epithilium. The bacterial colonies were abundant in the cuticle. The gill lamellae showed cuticlar damage in the formation of haemocyte nodules and eosinophilic granular cells

Rab46 integrates Ca2+ and histamine signaling to regulate selective cargo release from Weibel-Palade bodies

Endothelial cells selectively release cargo stored in Weibel-Palade bodies (WPBs) to regulate vascular function, but the underlying mechanisms are poorly understood. Here we show that histamine evokes the release of the proinflammatory ligand, P-selectin, while diverting WPBs carrying non-inflammatory cargo away from the plasma membrane to the microtubule organizing center. This differential trafficking is dependent on Rab46 (CRACR2A), a newly identified Ca2+-sensing GTPase, which localizes to a subset of P-selectin–negative WPBs. After acute stimulation of the H1 receptor, GTP-bound Rab46 evokes dynein-dependent retrograde transport of a subset of WPBs along microtubules. Upon continued histamine stimulation, Rab46 senses localized elevations of intracellular calcium and evokes dispersal of microtubule organizing center–clustered WPBs. These data demonstrate for the first time that a Rab GTPase, Rab46, integrates G protein and Ca2+ signals to couple on-demand histamine signals to selective WPB trafficking

The effect of the configuration of the amplification device on the power output of a piezoelectric strip

We investigated the behaviour of a polyvinylidene-fluoride piezoelectric strip ('stalk') clamped at the leading edge, and hinged to an amplification device ('leaf') at the trailing edge

An investigation of fluttering piezoelectric energy harvesters in off-axis and turbulent flows

The response of a fluttering piezoelectric energy harvester was studied in smooth flow and in aspects of replicated Atmospheric Boundary Layer turbulence (12.7% intensity, 310-mm longitudinal integral length scale). The harvester was yawed and pitched, and the effects on the power output were examined. Key findings were the following: (1) off-axis flow conditions rapidly degraded the mean output power of the harvester; (2) turbulence, for this specific harvester, acted similarly to a dynamic damping mechanism; (3) for on-axis flow, turbulence degraded the power outputs relative to smooth flow and for off-axis flow, the turbulence enhanced the power outputs relative to smooth flow. Future challenges include determination of harvester efficiencies, and analysis of fatigue-induced performance degradation

Structural health monitoring of composite t-joints for assessing the integrity of damage zones

This paper uses one category of Structural Health Monitoring (SHM) which uses strain variation across a structure as the key to damage detection. The structure used in this study was made from Glass Fibre Reinforced Plastic (GFRP). This paper discusses a technique developed called "Global Neural network Architecture Incorporating Sequential Processing of Internal sub Networks (GNAISPIN)" to predict the presence of multiple damage zones, determine their positions and also predict the extent of damage. Finite Element (FE) models of T-joints, used in ship structures, were created using MSC Patran(R) . These FE models were created with delaminations embedded at various locations across the bond-line of the structure. The resulting strain variation across the surface of the structure was observed. The validity of the Finite Element model was then verified experimentally. GNAISPIN was then used in tandem with the Damage Relativity Analysis Technique to predict and estimate the presence of multiple delaminations

Damage detection in T-joint composite structures

The use of composite structures in engineering applications has proliferated over the past few decades. This is mainly due to their distinct advantages of high structural performance, high corrosion resistance, and high strength/weight ratio. They are however prone to fibre breakage, matrix cracking and delaminations which are often invisible. Although there are systems to detect such damage, the characterisation of the damage is often much more difficult to achieve. A study is presented of the strain distribution of a GFRP T-joint structure under tensile pull-out loads and the determination of the presence and the extent of disbonds. Finite element analysis (FEA) has been conducted by placing delaminations of different sizes at various locations along the structure. The FEA results are also validated experimentally. The resulting strain distribution from the FEA is pre-processed by a method developed called the damage relativity assessment technique (DRAT). Artificial neural networks (ANNs) were used to determine the extent of damage. A real-time system has been developed which detects the presence, location and extent of damage from the longitudinal strains obtained from a set of sensors placed on the surface of the structure. The system developed is also independent of the magnitude of load acting on the structure

Damage criticality assessment in complex geometric structures using static stream response-based signal processing techniques

The use of glass-reinforced plastics (GFRP) as a structural material is widespread because of their high strength and stiffness, low mass, excellent durability and ability to be formed into complex shapes. However, GFRP composite structures are prone to delaminations which can lead to a significant degradation in structural integrity. A number of non-destructive inspection methods have been devised to inspect such structures. One class of SHM system relies on the examination of the strain distribution of the structure due to its operational loads. This paper considers the strain distribution in a GFRP structure subject to loading. The strain distribution due to delaminations of various sizes and locations along the bondline of the structure has been determined by finite element analysis (FEA). A technique called the Damage Relativity Assessment Technique (DRAT) has been developed and implemented to process the data in order to amplify the damage detection process. An Artificial Neural Network (ANN) has been trained to relate this strain distribution to damage size and location. This ANN has been shown to predict the size and location of damage for a number of simulated cases. The extension of this technique is to detect multiple cracks in a complex structure with multiple loading sets. These studies will also be carried over for structures subjected to impulse loading. A major aspect of this effort will include the pseudo-automated assessment of the criticality of the damage. Results from computational and experimental work, in this regard will be presented and used in conjunction with the DRAT and the ANN techniques described above