Specific Current Value Research Articles

Design and application of optimum toroidal shaped electromagnetic energy harvesters for unmanned aerial vehicles

AbstractThis study presents the design and implementation of electromagnetic energy harvesters for the purpose of charging unmanned aerial vehicles (UAVs) battery. In the study, the designed harvesters are analyzed through finite element method (FEM) simulations. In the FEM analysis, common and self‐inductance values, as well as magnetic flux density values of the harvesters, are calculated at specific current values. Inductance values are also theoretically calculated for comparison. Subsequently, an experimental setup is established to test the designed harvesters. After winding the core, the induced voltage and the power transferred to the load by the harvesters are measured. Curve fitting is performed after the measurements with different load resistances to find the maximum power transferred to the load. Through curve fitting, the maximum power obtained at each current value and at which load resistance this power is harvested are determined. Considering the intention of using the designed cores to charge UAVs and the importance of weight in UAV flight, the weights of each core, both without winding and after winding, are measured, and their costs are calculated. Taking all these criteria into account, the performance of the harvesters is demonstrated, and those among the used cores that are the most suitable for UAVs are identified in the study.

On the analysis of monovalent-ion selectivity of anion-exchange membranes

The scientific community has shown great interest in the selectivity of anion-exchange membranes (AEMs), particularly regarding their ability to monovalent-ions selective transport. In this work, we have studied the selectivity of an original anion-exchange membrane and a layer-by-layer (LbL) surface-modified membrane in the electrodialysis (ED) desalination of sodium chloride and sodium sulfate mixture in a lab-scale cell. The effect of various experimental factors on the desalination process and the selectivity of the separation of ions with different charges, such as the composition of the concentrate compartment, electrolyte concentrations in the concentrate and diluate compartments (0.004–0.5 M), the specific current value (0.82–9.0 mA/cm2), the desalination time, and the stirring of solution were considered. To explain the observed patterns, diffusion experiments and numerical simulations using the COMSOL® software package were carried out. Our findings demonstrate that the method chosen for conducting the benchmark desalination of a mixture of chlorides and sulfates can significantly affect the values of the selectivity coefficients and the accuracy of their determination for both standard and surface-modified membranes. For the latter, varying the desalination conditions leads to improved P(Cl/SO4) due to modification of up to 40 %.

Inferring network structure and local dynamics from neuronal patterns with quenched disorder

Abstract An inverse procedure is proposed and tested which aims at recovering the a priori unknown functional and structural information from global signals of living brains activity. To this end, we consider a Leaky-Integrate and Fire (LIF) model with short term plasticity neurons, coupled via a directed network. Neurons are assigned a specific current value, which is heterogenous across the sample, and sets the firing regime in which the neuron is operating in. The aim of the method is to recover the distribution of incoming network degrees, as well as the distribution of the assigned currents, from global field measurements. The proposed approach to the inverse problem implements the reductionist Heterogenous Mean-Field approximation. This amounts in turn to organizing the neurons in different classes, depending on their associated degree and current. When tested against synthetic data, the method returns accurate estimates of the sought distributions, while managing to reproduce and interpolate almost exactly the time series of the supplied global field. Finally, we also applied the proposed technique to longitudinal wide-field fluorescence microscopy data of cortical functionality in awake Thy1-GCaMP6f mice. Mice are induced a photothrombotic stroke in the primary motor cortex and their recovery monitored in time. An all-to-all LIF model which accommodates for currents heterogeneity allows to adequately explain the recorded patterns of activation. Altered distributions in neuron excitability are in particular detected, compatible with the phenomenon of hyperexcitability in the penumbra region after stroke.

Hydrogen Embrittlement and Oxide Layer Effect in the Cathodically Charged Zircaloy-2.

The present paper is aimed at determining the less investigated effects of hydrogen uptake on the microstructure and the mechanical behavior of the oxidized Zircaloy-2 alloy. The specimens were oxidized and charged with hydrogen. The different oxidation temperatures and cathodic current densities were applied. The scanning electron microscopy, X-ray electron diffraction spectroscopy, hydrogen absorption assessment, tensile, and nanoindentation tests were performed. At low oxidation temperatures, an appearance of numerous hydrides and cracks, and a slight change of mechanical properties were noticed. At high-temperature oxidation, the oxide layer prevented the hydrogen deterioration of the alloy. For nonoxidized samples, charged at different current density, nanoindentation tests showed that both hardness and Young’s modulus revealed the minims at specific current value and the stepwise decrease in hardness during hydrogen desorption. The obtained results are explained by the barrier effect of the oxide layer against hydrogen uptake, softening due to the interaction of hydrogen and dislocations nucleated by indentation test, and hardening caused by the decomposition of hydrides. The last phenomena may appear together and result in hydrogen embrittlement in forms of simultaneous hydrogen-enhanced localized plasticity and delayed hydride cracking.

Current Characteristics Estimation of Si PV Modules Based on Artificial Neural Network Modeling.

In the photovoltaic (PV) field, the outdoor evaluation of a PV system is quite complex, due to the variations of temperature and irradiance. In fact, the diagnosis of the PV modules is extremely required in order to maintain the optimum performance. In this paper, an artificial neural network (ANN) is proposed to build and train the model, and evaluate the PV module performance by mean bias error, mean square error and the regression analysis. We take temperature, irradiance and a specific voltage for input, and a specific current value for output, repeat several times in order to obtain an I-V curve. The main feature lies to the data-driven black-box method, with the ignorance of any analytical equations and hence the conventional five parameters (serial resistance, shunt resistance, non-ideal factor, reverse saturation current, and photon current). The ANN is able to predict the I-V curves of the Si PV module at arbitrary irradiance and temperature. Finally, the proposed algorithm has proved to be valid in terms of comparison with the testing dataset.

Modelling and control of vanadium redox flow battery for profile based charging applications

Abstract Batteries are the crucial link in accelerating the penetration of renewables in distributed grid systems. With multiple sources (most of which are intermittent), there is a need to optimize the usage of these energy storage systems. It is vital to have a charging algorithm to intelligently charge the battery, considering the losses and state of health. Several battery charging techniques exist for most of the mainstream batteries such as lithium ion and lead acid batteries. These charging algorithms are developed for specific battery chemistries to optimize the charging operation by controlling the charging current. There has been no specific charging methodology proposed, considering the uniqueness of flow batteries. In this paper a flow battery model is developed to accommodate the implementation of charging protocols by controlling the current and flow rate in real time. The aim is to demonstrate a methodology where different user requirements can be programmed for efficient operation of the battery to perform a given task. The control is established by an intuitive fuzzy logic controller for changing the input variables based on the intrinsic losses, pump power loss and the capacity decay due to the aging of the battery. The need for such a controller to handle different scenarios of operation arises from the vast applicability of large scale energy storage. A simple example of such a system is proposed with demonstrating three operator selectable modes namely Autonomous (A), Energy bias (E) and Time bias (T). In each case, a specific current value and flow rate value is selected by the controller and is fine-tuned during the charging process. The values are tuned for specific modes of operation and different aspects of the battery performance are studied for these modes. This work demonstrates a method to leverage the use of a physical model based system in a high level user interface for controlling flow batteries.

Tunable supercapacitance of electrospun Mn3O4 beaded chains via charge- discharge cycling and control parameters

Here we report on the tunable supercapacitance of the Mn3O4 beaded chains synthesized by a simple and low cost electro-spinning process. Tuning is achieved by controlled phase transformation of surface spinel Mn3O4 beaded chains to layered-birnessite MnO2 nanoflakes through galvanostatic charge-discharge cycling. Phase transformation rate is optimized to get maximum capacitance by controlling the parameters such as applied specific current value, number of galvanostatic charge-discharge cycles, micro-structure of working electrode material and the selection of potential range. A maximum specific capacitance of ∼445Fg−1 and areal capacitance of ∼495mFcm−2 are obtained at current densities of 0.5Ag−1 and 0.125mAcm−2 respectively. The superior performance in case of layered-spinel composites among similar nanostructures is due to high surface to volume ratio of the MnO2 nanoflakes formed from the Mn3O4 beaded chains which in turn give rise to large number of surface active sites for the redox reaction to take place. About 100% of capacity retention and coulombic efficiency are observed for ∼1000 cycles even at a higher current density of 7Ag−1. Morphological dependence of the phase transformation rate is investigated by preparing two different morphologies of Mn3O4viz., octahedrons and spherical nanoparticles.

High cycling stability of anodes for lithium-ion batteries based on Fe3O4 nanoparticles and poly(acrylic acid) binder

Fe3O4 nanoparticles synthesized by a base catalyzed method are tested as anode material for Li-ion batteries. The pristine nanoparticles are morphologically characterized showing an average size of 11 nm. Electrodes are prepared using high-molecular weight Poly (acrylic acid) as improved binder and ethanol as low cost and environmentally friendly solvent. The evaluation of electrochemical properties shows high specific capacity values of 857 mA hg−1 after 200 cycles at a specific current of 462 mAg−1, as well as an excellent rate capability with specific current values up to 18480 mAg−1. To the best of our knowledge, this is the first report of Fe3O4 nanoparticles cycling with PAA as binder.

Superconductivity in single crystalline Pb nanowires contacted by normal metal electrodes

The transport properties of superconducting single crystal Pb nanowires that are 55 and 70 nm in diameter were studied by the standard four-electrode method. With normal metal electrodes, resistance-temperature and resistance-magnetic field scans show a series of resistance steps with increasing temperature and magnetic field as the wires are brought toward the normal state. The resistance-current ($R$-$I$) scans at different temperatures and magnetic fields show that the increase in $R$ with $I$ is punctuated with sharp steps at specific current values. A large residual resistance is observed down to 2 K. The origin of these phenomena is related to the inhomogeneity and proximity effect from the normal electrodes.