Random Energy Research Articles

Round-trip oscillation triboelectric nanogenerator with high output response and low wear to harvest random wind energy

Round-trip oscillation triboelectric nanogenerator with high output response and low wear to harvest random wind energy

Variational methods for a singular SPDE yielding the universality of the magnetization ripple

AbstractThe magnetization ripple is a microstructure formed in thin ferromagnetic films. It can be described by minimizers of a nonconvex energy functional leading to a nonlocal and nonlinear elliptic SPDE in two dimensions driven by white noise, which is singular. We address the universal character of the magnetization ripple using variational methods based on Γ‐convergence. Due to the infinite energy of the system, the (random) energy functional has to be renormalized. Using the topology of Γ‐convergence, we give a sense to the law of the renormalized functional that is independent of the way white noise is approximated. More precisely, this universality holds in the class of (not necessarily Gaussian) approximations to white noise satisfying the spectral gap inequality, which allows us to obtain sharp stochastic estimates. As a corollary, we obtain the existence of minimizers with optimal regularity.

Non-self-averaging of current in a totally asymmetric simple exclusion process with quenched disorder.

We investigate the current properties in the totally asymmetric simple exclusion process (TASEP) on a quenched random energy landscape. In low- and high-density regimes, the properties are characterized by single-particle dynamics. In the intermediate one, the current becomes constant and is maximized. Based on the renewal theory, we derive accurate results for the maximum current. The maximum current significantly depends on a disorder realization, i.e., non-self-averaging (SA). We demonstrate that the disorder average of the maximum current decreases with the system size, and the sample-to-sample fluctuations of the maximum current exceed those of current in the low- and high-density regimes. We find a significant difference between single-particle dynamics and the TASEP. In particular, the non-SA behavior of the maximum current is always observed, whereas the transition from non-SA to SA for current in single-particle dynamics exists.

Sample-to-sample fluctuations of transport coefficients in the totally asymmetric simple exclusion process with quenched disorder.

We consider the totally asymmetric simple exclusion processes on quenched random energy landscapes. We show that the current and the diffusion coefficient differ from those for homogeneous environments. Using the mean-field approximation, we analytically obtain the site density when the particle density is low or high. As a result, the current and the diffusion coefficient are described by the dilute limit of particles or holes, respectively. However, in the intermediate regime, due to the many-body effect, the current and the diffusion coefficient differ from those for single-particle dynamics. The current is almost constant and becomes the maximal value in the intermediate regime. Moreover, the diffusion coefficient decreases with the particle density in the intermediate regime. We obtain analytical expressions for the maximal current and the diffusion coefficient based on the renewal theory. The deepest energy depth plays a central role in determining the maximal current and the diffusion coefficient. As a result, the maximal current and the diffusion coefficient depend crucially on the disorder, i.e., non-self-averaging. Based on the extreme value theory, we find that sample-to-sample fluctuations of the maximal current and diffusion coefficient are characterized by the Weibull distribution. We show that the disorder averages of the maximal current and the diffusion coefficient converge to zero as the system size is increased and quantify the degree of the non-self-averaging effect for the maximal current and the diffusion coefficient.

Experimentation and Numerical Modeling of Peak Temperature in the Weld Joint during Rotary Friction Welding of Dissimilar Plastic Rods.

Rotary friction welding (RFW) could result in lower welding temperature, energy consumption, or environmental effects as compared with fusion welding processes. RFW is a green manufacturing technology with little environmental pollution in the field of joining methods. Thus, RFW is widely employed to manufacture green products. In general, the welding quality of welded parts, such as tensile strength, bending strength, and surface hardness is affected by the peak temperature in the weld joint during the RFW of dissimilar plastic rods. However, hitherto little is known about the domain knowledge of RFW of acrylonitrile butadiene styrene (ABS) and polycarbonate (PC) polymer rods. To prevent random efforts and energy consumption, a green method to predict the peak temperature in the weld joint of dissimilar RFW of ABS and PC rods was proposed. The main objective of this work is to investigate the peak temperature in the weld joint during the RFW using COMSOL multiphysics software for establishing an empirical technical database of RFW of dissimilar polymer rods under different rotational speeds. The main findings include that the peak temperature affecting the mechanical properties of RFW of PC and ABS can be determined by the simulation model proposed in this work. The average error of predicting the peak temperature using COMSOL software for five different rotational speeds is about 15 °C. The mesh element count of 875,688 is the optimal number of meshes for predicting peak temperature in the weld joint. The bending strength of the welded part (y) using peak welding temperature (x) can be predicted by the equation of y = -0.019 x2 + 5.081x - 200.75 with a correlation coefficient of 0.8857. The average shore A surface hardness, impact energy, and bending strength of the welded parts were found to be increased with increasing the rotational speed of RFW.

Simulation of power frequency high power electromagnetic coupling heater and analysis of influencing factors

Aiming at the problems existing in current electric energy replacement, such as the random energy fluctuation of the power grid, the energy storage of the power grid and the consumption of new energy, electromagnetic coupling heating technology and methods based on power frequency high power is proposed. According to the theory of electromagnetic induction heating, Maxwell’s equations are used to establish a three-dimensional electromagnetic field simulation model, determining the size of the core and the windings, and completing the structural design of the heater. Based on the simulation model of electromagnetic coupling heater, the back electromotive force of the primary winding, the induced current of the secondary winding, the B–H curve field diagram of the core, and the J-field diagram of the secondary winding are analyzed to explore heating efficiency of the heater and verify the correctness of electromagnetic coupling heating mechanism. The relationship between the coupling gap, the number of heat dissipation rings, and the air gap and heating efficiency of the heater was further explored, and the model structure of the heater was optimized according to the variation rules so that heating efficiency of the heater reached more than 99%. The electromagnetic coupling heater achieves high efficiency, cleanliness and low carbon, and power frequency high power, which is of great significance to the renewal of electric heater in the future.

Analysis of wind power output characteristics and output prediction

A fuzzy clustering method and a prediction correction method based on the Copula function are used to establish an analytical model of wind power generation output characteristics. It also predicts the expected and corrected values of its power generation to alleviate the pressure on the grid caused by the high proportion of random new energy sources connected to the grid. Based on the natural properties of wind, a new characteristic index based on the traditional load characteristic index and the new energy output curve is proposed to quantify and evaluate the characteristics of new energy power generation. The results show that the method proposed in this paper can effectively reduce the influence of climate change on output prediction; in comparison to the traditional index, the new index is more refined and instructive.

Enhancing the Output Performance of a Triboelectric Nanogenerator Based on Modified Polyimide and Sandwich-Structured Nanocomposite Film

Recently, scientists have been facing major obstacles in terms of improving the performances of dielectric materials for triboelectric nanogenerators. The triboelectric nanogenerator (TENG) is one of the first green energy technologies that can convert random mechanical kinetic energy into electricity. The surface charge density of TENGs is a critical factor speeding up their commercialization, so it is important to explore unique methods to increase the surface charge density. The key to obtaining a high-performance TENG is the preparation of dielectric materials with good mechanical properties, thermal stability and output performance. To solve the problem of the low output performance of PI-based triboelectric nanogenerators, we modified PI films by introducing nanomaterials and designed a new type of sandwich-shaped nanocomposite film. Herein, we used polyimide (PI) with ideal mechanical properties, excellent heat resistance and flexibility as the dielectric material, prepared an A-B-A sandwich structure with PI in the outer layer and modified a copper calcium titanate/polyimide (CCTO/PI) storage layer in the middle to improve the output of a TENG electrode. The doping amount of the CCTO was tailored. The results showed that at 8 wt% CCTO content, the electrical output performance was the highest, and the open-circuit voltage of CCTO/PI was 42 V. In the TENG, the open-circuit voltage, short-circuit current and transfer charge of the prepared sandwich-structured film were increased by 607%, 629% and 672% compared to the TENG with the PI thin film, respectively. This study presents a novel strategy of optimizing dielectric materials for triboelectric nano-generators and has great potential for the future development of high output-performance TENGs.

Flexible, Wash-Resistant Human Mechanical Energy Harvesting and Storage System for Monitoring Human Movement.

The rapid development of personal portable electronic devices has brought an increasingly urgent need for flexible and portable power sources. Herein, a low-cost, wearable, efficient, sustainable energy harvesting and storage system for human motion detection has been developed, based on a supercapacitor (SC) and triboelectric nanogenerator (TENG). Carbon cloth (CC)-loaded ZnO/ZnS nanoarrays and a PVD-treated polyurethane conductive sponge are employed as positive and negative triboelectric friction layers, respectively. Besides, flexible and robust silicone rubber provides stable output performance and enables the TENG to harvest mechanical energy from human motion even under complex conditions. As a result, it shows excellent electrical output performance in terms of the open-circuit voltage, short-circuit current, and average power density, reaching 175 V, 12 μA, and 816.7 mW m-2, respectively. These outstanding performances enable the TENG to effectively charge an all-solid-state symmetrical SC (MnO2/LiMn2O4@CC//MnO2/LiMn2O4@CC) and subsequently store it as electrochemical energy for sustainable power supply. Because of the flexible all-texture-type structure of the entire system, it is capable of monitoring the human body's movement. This work has a promising future in random mechanical energy harvesting and storage, as well as human motion tracking.

Multi-layered triboelectric nanogenerator incorporated with self-charge excitation for efficient water wave energy harvesting

As one of the promising renewable and clean energy sources, water waves have attracted intensive attention and are expected to convert into electricity to alleviate energy demand. The emergence of triboelectric nanogenerator (TENG) provides a new idea for harvesting and utilizing this low-frequency and random energy, but the low output performance of TENG hinders its widespread application. In this work, an effective strategy that combines multi-layered structure design and external circuit optimization is proposed to efficiently scavenge the water wave energy. Consequently, an average power density up to 27.8 W m−3 Hz−1 (corresponding output power: 194.5 μW) has been achieved on the multi-layered TENG. Meanwhile, this strategy also demonstrated that the average power density can be effectively improve to almost 9 times that of the multi-layered TENG without external circuit when triggered by water wave. This work not only provides an efficient strategy for harvesting water wave energy, but also lays a foundation for advancing the practical application of TENG in renewable and clean energy.

Trunk Borer Identification Based on Convolutional Neural Networks

The trunk borer is a great danger to forests because of its strong concealment, long lag and great destructiveness. In order to improve the early monitoring ability of trunk borers, the representative Agrilus planipennis Fairmaire was selected as the research object. The convolutional neural network named TrunkNet was designed to identify the activity sounds of Agrilus planipennis Fairmaire larvae. The activity sounds were recorded as vibration signals in audio form. The detector was used to collect the activity sounds of Agrilus planipennis Fairmaire larvae in the wood segments and some typical outdoor noise. The vibration signal pulse duration is short, random and high energy. TrunkNet was designed to train and identify vibration signals of Agrilus planipennis Fairmaire. Over the course of the experiment, the test accuracy of TrunkNet was 96.89%, while MobileNet_V2, ResNet18 and VGGish showed 84.27%, 79.37% and 70.85% accuracy, respectively. TrunkNet based on the convolutional neural network can provide technical support for the automatic monitoring and early warning of the stealthy tree trunk borers. The work of this study is limited to a single pest. The experiment will further focus on the applicability of the network to other pests in the future.

Ethernet Information Security Protocols Based on Industrial Control Wireless Sensor Networks

This paper provides an in‐depth study and analysis of information security protocols for industrial Ethernet using wireless sensor networks. The optimal number of cluster heads for nonuniform subclustering is derived based on the sensor energy consumption model, and then, the EEUC contention radius formula is optimized to select candidate cluster heads with random values and energy as weights. A multihop approach based on the shortest offset is also proposed for intercluster information transmission. Experimental results show that the EEUC‐based improved cluster routing protocol proposed in this paper balances the node energy consumption and extends the network lifetime. In response to the problem that the coarsened hop value and average hop distance of the DV‐Hop localization algorithm cannot reflect the network topology, the improved DV‐Hop algorithm based on multicommunication radius and hop distance correction is proposed. Simulation experiments show that the improved algorithm based on multiple communication radius and hop count correction can significantly reduce the localization error and improve the accuracy of the algorithm. Aiming at the shortcomings of MRP with excessive risk concentration and transmission medium limitation, this paper proposes a fast self‐healing mechanism of industrial Ethernet with a multiexpert strategy. The PCP‐AP common platform architecture for openSAFETY sites is designed on the base sleeve of the implementation of the industrial Ethernet protocol Ethernet POWERLINK; the main communication part of POWERLINK is implemented through an FPGA hardware solution, and the openSAFETY site is implemented using AM335X high‐performance processor to implement openSAFETY security application functions. Finally, the article conducts field tests on the wireless signal information transmission, WSN data transmission, network connection, and power supply system in the system and compares and analyzes the data collected by the system with the monitoring data of the national control site. The data obtained by the system has real reliability. The communication module used is inexpensive, lightweight, and easy to operate. It can realize the collection of multiple pollution sources, and compared with traditional monitoring equipment, it avoids the difficulties of complicated wiring, difficult positioning of pollution sources, and restricted monitoring areas and largely reduces the investment in human and material resources.

Chaos, multistability and coexisting behaviours in small-scale grid: Impact of electromagnetic power, random wind energy, periodic load and additive white Gaussian noise

Chaos, multistability and coexisting behaviours in small-scale grid: Impact of electromagnetic power, random wind energy, periodic load and additive white Gaussian noise

Statistical linearization for random vibration energy harvesting with piezoelectric material nonlinearity

Due to piezoelectric softening and dissipative nonlinearities, the piezoelectric cantilever energy harvester exhibits nonlinear hysteresis when subjected to large excitation. These nonlinearities have brought significant challenges to the modeling and response prediction of randomly excited piezoelectric energy harvesting systems. In this study, the voltage responses of the nonlinear piezoelectric cantilever energy harvester under random excitation are initially assumed to follow the Gaussian distribution which is experimentally validated later. The equivalent linear transfer function are derived from the approximate linearization of the stiffness and damping using the statistical linearization (SL) technique. The mathematical expectations of the voltage responses are calculated from the multivariate normal distributions. Frequency sweep experiments are conducted to a cantilever energy harvester to identify the nonlinear piezoelectric material properties. The statistically linearized model was experimentally validated under random base acceleration excitation by comparing the probability density function of the predicted voltage responses and average power against the experimental measurements. The advantage of the SL technique lies in allowing one to use an iterative procedure to estimate the equivalent linear terms while the analytical expressions are unattainable because of the complex nonlinearity in the governing equations. The results show that the prediction of the SL model to the random base acceleration excitation agrees with experimental measurements with a broadband frequency range, although only the fundamental mode of the beam is considered.

Trajectory phase transitions in non-interacting systems: all-to-all dynamics and the random energy model.

We study the fluctuations of time-additive random observables in the stochastic dynamics of a system of [Formula: see text] non-interacting Ising spins. We mainly consider the case of all-to-all dynamics where transitions are possible between any two spin configurations with uniform rates. We show that the cumulant generating function of the time-integral of a normally distributed quenched random function of configurations, i.e. the energy function of the random energy model (REM), has a phase transition in the large [Formula: see text] limit for trajectories of any time extent. We prove this by determining the exact limit of the scaled cumulant generating function. This is accomplished by connecting the dynamical problem to a spectral analysis of the all-to-all quantum REM. We also discuss finite [Formula: see text] corrections as observed in numerical simulations. This article is part of the theme issue 'Quantum annealing and computation: challenges and perspectives'.

Monte-Carlo based random passive energy beamforming for reconfigurable intelligent surface assisted wireless power transfer

Reconfigurable intelligent surface (RIS) employs passive beamforming to control the wireless propagation channel, which benefits the wireless communication capacity and the received energy efficiency of wireless power transfer (WPT) systems. Such beamforming schemes are classified as discrete and non-convex integer programming problems. In this paper, we propose a Monte-Carlo (MC) based random energy passive beamforming of RIS to achieve the maximum received power of electromagnetic (EM) WPT systems. Generally, the Gibbs sampling and re-sampling methods are employed to generate phase shift vector samples. And the sample with the maximum received power is considered the optimal solution. In order to adapt to the application scenarios, we develop two types of passive beamforming algorithms based on such MC sampling methods. The first passive beamforming uses an approximation of the integer programming as the initial sample, which is calculated based on the channel information. And the second one is a purely randomized algorithm with the only total received power feedback. The proposed methods present several advantages for RIS control, e.g., fast convergence, easy implementation, robustness to the channel noise, and limited feedback requirement, and they are applicable even if the channel information is unknown. According to the simulation results, our proposed methods outperform other approximation and genetic algorithms. With our methods, the WPT system even significantly improves the power efficiency in the nonline-of-sight (NLOS) environment.

A durable triboelectric nanogenerator with a coaxial counter-rotating design for efficient harvesting of random mechanical energy

Harvesting ambient mechanical energy is a crucial technology for realizing self-powered electronics, which are widely used in wireless sensing networks, implantable devices, and portable electronics. However, mechanical motions in the environment are random, low-frequency, and limited in amplitude, making it challenging to recycle these energies efficiently and stably. Herein, inspired by counter-rotating propellers, a triboelectric nanogenerator based on a coaxial counter-rotating unit (CCR-TENG) was designed and fabricated. Specifically, the CCR-TENG consists of a coaxial counter-rotating unit and a free-rotating disk triboelectric nanogenerator (FRD-TENG). The coaxial counter-rotating unit plays a primary role in converting random ambient mechanical motions into high-speed relative rotation motion. Accordingly, the generated electronic energy of CCR-TENG is improved by more than 15 times compared with conventional devices through the efficient motion mode conversion. Moreover, by introducing a soft nylon fur layer into the FRD-TENG, the surface triboelectric charges are replenished in soft contact mode under the mechanical excitation. Thereby, the output performance and durability of the CCR-TENG are further improved. Remarkably, 180 LEDs and three typical portable electronics were continuously powered using CCR-TENG to harvest energy from random human motions. This work provides a promising strategy for the efficient and stable harvesting of ambient mechanical energy.

Heterogeneity and Memory Effect in the Sluggish Dynamics of Vacancy Defects in Colloidal Disordered Crystals and Their Implications to High-Entropy Alloys.

Vacancy dynamics of high-density 2D colloidal crystals with a polydispersity in particle size are studied experimentally. Heterogeneity in vacancy dynamics is observed. Inert vacancies that hardly hop to other lattice sites and active vacancies that hop frequently between different lattice sites are found within the same samples. The vacancies show high probabilities of first hopping from one lattice site to another neighboring lattice site, then staying at the new site for some time, and later hopping back to the original site in the next hop. This back-returning hop probability increases monotonically with the increase in packing fraction, up to 83%. This memory effect makes the active vacancies diffuse sluggishly or even get trapped in local regions. Strain-induced vacancy motion on a distorted lattice is also observed. New glassy properties in the disordered crystals are discovered, including the dynamical heterogeneity, the presence of cooperative rearranging regions, memory effect, etc. Similarities between the colloidal disordered crystals and the high-entropy alloys (HEAs) are also discussed. Molecular dynamics simulations further support the experimental observations. These results help to understand the microscopic origin of the sluggish dynamics in materials with ordered structures but in random energy landscapes, such as high-entropyalloys.

Power management using AI-based IOT systems

The Internet of Things (IoT) concept is expected to evolve the interest of each industry, medicine, and others. The main forces behind the huge data gathering were the IoT devices built into the sensor. One of the biggest challenges has been the maintenance of those huge data sets. A massive IoT (mIoT) refers to the continuous collection of huge quantities of data with sensors. Therefore, self-adaptive algorithms based on AI are required to aggregate, evaluate and effectively understand all program objects. It is imperative to control the energy carefully due to the increase in large datasets and power-hungry IoT gadgets. It is critical to combine mIoT using AI-based approaches to fairly distribute power levels to small portable devices. The connection between the lifetime of the mIoT and the information flow found that as information rates increase, more energy is lost, reducing the service life of mIoT networks. With more sensor nodes, the power would be appropriately distributed across the transverse layers. By analyzing key characteristics and data sets, this research suggests a new Improved Random Energy Optimization Algorithm (IIRBEOA) for mIoT systems to address these issues. According to an experimental survey, the proposed IRBEOA exceeds its Baseline rival in terms of practical control and implementation of energy.

Alignment-invariant signal reality reconstruction in hyperspectral imaging using a deep convolutional neural network architecture

The energy resolution in hyperspectral imaging techniques has always been an important matter in data interpretation. In many cases, spectral information is distorted by elements such as instruments’ broad optical transfer function, and electronic high frequency noises. In the past decades, advances in artificial intelligence methods have provided robust tools to better study sophisticated system artifacts in spectral data and take steps towards removing these artifacts from the experimentally obtained data. This study evaluates the capability of a recently developed deep convolutional neural network script, EELSpecNet, in restoring the reality of a spectral data. The particular strength of the deep neural networks is to remove multiple instrumental artifacts such as random energy jitters of the source, signal convolution by the optical transfer function and high frequency noise at once using a single training data set. Here, EELSpecNet performance in reducing noise, and restoring the original reality of the spectra is evaluated for near zero-loss electron energy loss spectroscopy signals in Scanning Transmission Electron Microscopy. EELSpecNet demonstrates to be more efficient and more robust than the currently widely used Bayesian statistical method, even in harsh conditions (e.g. high signal broadening, intense high frequency noise).