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Abstract We consider the motion of an electromagnetic vibrational energy harvester (EMVEH) as function of the initial position and velocity and show that this displays a classical chaotic dynamical behavior. The EMVEH considered consists of three coaxial cylindrical permanent magnets and two coaxial coils. The polarities of the three magnets are chosen in such a way that the central magnet floats, with its lateral motion being prevented by enclosion in a hollow plastic tube. The motion of the floating magnet, caused by e.g. environmental vibrations, induces a current in the coils allowing electrical energy to be harvested. We analyze the behavior of the system using a numerical model employing experimentally verified expressions of the force between the magnets and the damping force between the floating magnet and the coils. We map out the phase space of the motion of the system with and without gravity, and show that this displays a fractal-like behavior and that certain driving frequencies and initial conditions allow a large power to be harvested, and that more stable states than two exists. Finally, we show that at leasts fifth order polynomial approximation is necessary to approximate the magnet-magnet force and correctly predict the system behavior.

Extraordinary magnetoresistance (EMR) is a geometric magnetoresistance emerging in hybrid systems typically comprising a high-mobility material and a metal. Due to a field-dependent redistribution of electrical currents in these devices, the electrical resistance at room temperature can increase by 10 ^7 % when applying a magnetic field of 5 T. Although EMR holds considerable potential for realizing sensitive, all-electronic magnetometers, this potential is largely unmet. A key challenge is that the performance of EMR devices depends very sensitively on variations in a vast parameter space where changes in the device geometry and material properties produce widely different EMR performances. The challenge of navigating in the large parameter space is further amplified by the poor understanding of the interplay between the device geometry and material properties. By systematically varying the material parameters in four key EMR geometries using diffusive transport simulations, we here elucidate this interplay with the aim of finding universal guidelines for designing EMR devices. Common to all geometries, we find that the sensitivity scales inversely with the carrier density, while the MR reaches saturation at low carrier densities. Increasing the mobility beyond 20 000 cm ^2 Vs ^−1 is required to observe strong EMR effects at 1 T with the optimal magnetoresistance observed for mobilities between 100 000–500 000 cm ^2 Vs ^−1 . An interface resistance below $\rho_c = 10^{-4}\ \Omega$ cm ^2 between the constituent materials in the hybrid devices was also found to be a prerequisite for very high magnetoresistances in all geometries. By further simulating several high-mobility materials at room and cryogenic temperatures, we conclude that encapsulated graphene and InSb are amongst the most promising candidates for EMR devices showing high magnetoresistance exceeding 10 ^7 % below 1 T at room temperature. This study paves the way for understanding how to realize EMR devices with record-high magnetoresistance and high sensitivity for detecting magnetic fields.

Internet-of-thing (IoT) is an assembly of devices that collect and share data with other devices and communicate via the internet. This massive network of devices, generates and communicates data and is the key to the value in IoT, allowing access to raw information, gaining insight, and making an intelligent decisions. Today, there are billions of IoT devices such as sensors and actuators deployed. Many of these applications are easy to connect, but those tucked away in hard-to-access spots will need to harvest ambient energy. Therefore, the aim is to create devices that are self-report in real-time. Efforts are underway to install a self-powered unit in IoT devices that can generate sufficient power from environmental conditions such as light, vibration , and heat . In this review paper, we discuss the recent progress made in materials and device development in power- and, storage units, and power management relevant for IoT applications. This review paper will give a comprehensive overview for new researchers entering the field of IoT and a collection of challenges as well as perspectives for people already working in this field.

The efficiency of the magnetic refrigeration process strongly depends on the heat transfer performance of the regenerator. As a potential way to improve the heat transfer performance of a regenerator, the design of sub-millimeter hydraulic diameter porous structures is realized by freeze-cast structures. Four freeze-cast regenerators with different pore widths are characterized experimentally and numerically. Empirical parameters are determined for the correlations of heat transfer and flow resistance via a 1D model. Thermal effectiveness and pressure drop are measured for thermal-hydraulic evaluations. Temperature span and specific cooling capacity are obtained to compare the magnetocaloric potential based on the material La0.66Ca0.27Sr0.06Mn1.05O3. The stability of freeze-cast regenerators is validated by comparing the performance during, before and after oscillatory flow and periodic magnetic field tests. Smaller pore design obtain the better heat transfer performance and required mechanical strength, while pore design with significant dendrites provides the worst tradeoff between heat transfer performance and flow resistance.

This study aims to use the convolutional neural network (CNN) to build a new model for detecting the faults of solar PV module performance under the temperature and shading effects. Two sets of data were used based on solar data from Mendeley, and experimental data from the University of Sharjah. The transfer learning model was also used in this study. Both CNN and transfer learning models were trained and tested for Mendeley and experimental data under different data and epoch numbers. The accuracy of both models was tested under different numbers of data and epochs. The accuracy was higher than 96 % for 50 epochs when half and full data were used for both models. In comparison, the transfer learning model still shows 90 % for all data when the number of epochs is reduced to 10 epochs. The transfer learning model in this study can be used to detect faults in other renewable energy applications, such as wind energy systems.