Search Results (160)

It is well known that for low-intensity incident light within a certain frequency range, the stopping voltage of the photoelectric effect is independent of the intensity but dependent on the frequency of the light, which is described by the equation $V=h\nu /e-W_0/e$, where V is the stopping voltage, h is the Planck constant, $\nu$ is the frequency of incident light, e is the basic charge, and $W_0$ is the work function. This implies that the stopping voltage increases with the frequency of the incident light. However, our experiments reveal that for non-monochromatic incident light, the stopping voltage is not determined by the maximum frequency component of the incident light, but by the maximum center frequency among all wave train components (with different center frequencies) involved in the incident light; that is to say, in the photon energy expression $h\nu$, the physical quantity $\nu$ does not refer to the frequency of monochromatic light, but represents the center frequency of a wave train spectrum. The spectral bandwidth of a wave train component can be as large as 122 nm in the visible and near-infrared regions. These findings highlight the need for greater attention to such effects in photoelectric detection and the study of energy exchange between light and matter. Full article

Rolling Bearings are crucial components for induction motors and generators in electric vehicles (EVs), as their performance considerably influences the system’s operational reliability and safety. However, the commercial greases used for bearing lubrication in EV motors pose a detrimental impact on the environment. In addition, they are ineffective in mitigating the effect of electric discharges on rolling surfaces leading to premature bearing failures. This study investigates the viability of a developed eco-friendly grease from palm olein as the base oil and glycerol monostearate as the thickener, enhanced with conductive multi-walled carbon nanotubes (MWCNTs) for EV motor bearings prone to electrical currents. Chemical–physical, tribological, and electrical tests were conducted on the developed grease samples without and with MWCNTs at 1 wt.%, 2 wt.%. and 3 wt.% concentrations and results were compared to lithium and sodium greases. Palm grease samples demonstrated a lower EDM voltage range reaching 1.0–2.2 V in case of 3 wt.% MWCNTs blends, indicating better electrical conductivity and protecting the bearing surfaces from electric-related faults. These findings were further confirmed using vibrations measurement and SEM-EDX analysis of the electrically worn bearings. Bearings lubricated with palm grease blends exhibited lower vibration levels. Palm grease with 2 wt.% MWCNTs reduced vibration amplitudes by 28.4% (vertical) and 32.3% (horizontal). Analysis of bearing damaged surfaces revealed enhanced damaged surface morphology for MWCNT-enhanced palm grease as compared to surface lubricated by commercial greases. The results of this work indicate that the proposed bio-grease is a promising candidate for future application in the field of next-generation electric mobility systems. Full article

Integrated circuits are the core of a cyber-physical system, where tens of billions of components are integrated into a tiny silicon chip to conduct complex functions. To maximize utilities, the design and manufacturing life cycle of integrated circuits rely on numerous untrustworthy third parties, forming a global supply chain model. At the same time, this model produces unpredictable and catastrophic issues, threatening the security of individuals and countries. As for guaranteeing the security of ultra-highly integrated chips, detecting slight abnormalities caused by malicious behavior in the current and voltage is challenging, as is achieving computability within a reasonable time and obtaining a golden reference chip; however, artificial intelligence can make everything possible. For the first time, this paper presents a systematic review of artificial-intelligence-based integrated circuit security approaches, focusing on the latest attack and defense strategies. First, the security threats of integrated circuits are analyzed. For one of several key threats to integrated circuits, hardware Trojans, existing attack models are divided into several categories and discussed in detail. Then, for summarizing and comparing the numerous existing artificial-intelligence-based defense strategies, traditional and advanced artificial-intelligence-based approaches are listed. Finally, open issues on artificial-intelligence-based integrated circuit security are discussed from three perspectives: in-depth exploration of hardware Trojans, combination of artificial intelligence, and security strategies involving the entire life cycle. Based on the rapid development of artificial intelligence and the initial successful combination with integrated circuit security, this paper offers a glimpse into their intriguing intersection, aiming to draw greater attention to these issues. Full article

For the precise description of gas physical processes in high-voltage direct current (HVDC) transmission, an advanced and robust numerical framework for the simulation of transient particle densities in the course of corona discharges is developed in this work. The aim is the scalable and consistent modeling of the space charge density under realistic conditions. The core component of the framework is a discontinuous Galerkin method that ensures the conservative properties of the underlying hyperbolic problem. The space charge density at the electrode surface is imposed as a dynamic boundary condition via Lagrange multipliers. To increase the numerical stability and convergence rate, a homotopy approach is also integrated. For the experimental validation, a measurement concept was realised that uses a subtraction method to specifically remove the displacement current component in the signal and thus enables an isolated recording of the transient ion current with superimposed voltage stresses. The experimental results on a small scale agree with the numerical predictions and prove the quality of the model. On this basis, the framework is transferred to hybrid HVDC overhead line systems with a bipolar design. In the event of a fault, significant transient space charge densities can be seen there, especially when superimposed with new types of voltage waveforms. The framework thus provides a reliable contribution to insulation coordination in complex HVDC systems and enables the realistic analysis of electrohydrodynamic coupling effects on an industrial scale. Full article

This study presents a comprehensive mathematical model of a semiconductor structure based on vanadium dioxide (VO₂), specifically in its conductive phase. The model was developed using the finite element method (FEM), enabling detailed simulation of the formation of a conductive channel under the influence of low-frequency alternating voltage (50 Hz). The VO₂ structure under investigation exhibits pronounced electric field concentration at the surface, where the field strength reaches approximately 5 × 10⁴ V/m, while maintaining a more uniform distribution of around 2 × 10⁴ V/m within the bulk of the material. The simulation results were validated experimentally using a test circuit. Minor deviations—no greater than 8%—were observed between the simulated and measured current values, attributed to magnetic core saturation and modeling assumptions. A distinctive feature of the model is its ability to incorporate the nonlinear dependencies of VO₂’s electrical properties on frequency. Analytical expressions were derived for the magnetic permeability and resistivity of VO₂, demonstrating excellent agreement with experimental data. The coefficients of determination (R²) for the frequency dependence of magnetic permeability and resistance were found to be 0.9976 and 0.9999, respectively. The current version of the model focuses exclusively on the conductive phase and does not include the thermally induced metal–insulator phase transition characteristic of VO₂. The study confirms that VO₂-based structures exhibit high responsiveness and nonlinear switching behavior, making them suitable for applications in electronic surge protection, current limiting, and switching elements. The developed model provides a reliable and physically grounded tool for the design and optimization components based on VO₂ in power electronics and protective circuitry. Full article

Adopting mobile robots for high voltage (HV) live-line operations can mitigate personnel casualties and enhance operational efficiency. However, conventional mechanical arms cannot inspect concealed spaces in the power cable tunnel because their joint integrates metallic motors or hydraulic serial-drive mechanisms, which limit the arm’s length and insulation performance. Therefore, this study proposes a 7-degree-of-freedom (7-DOF) bionic mechanical arm with rigid-flexible coupling, mimicking human arm joints (shoulder, elbow, and wrist) designed for HV live-line operations in concealed cable tunnels. The arm employs a tendon-driven mechanism to remotely actuate joints, analogous to human musculoskeletal dynamics, thereby physically isolating conductive components (e.g., motors) from the mechanical arm. The arm’s structure utilizes dielectric materials and insulation-optimized geometries to reduce peak electric field intensity and increase creepage distance, achieving intrinsic self-insulation. Furthermore, the mechanical design addresses challenges posed by concealed spaces (e.g., shield tunnels and multi-circuit cable layouts) through the analysis of joint kinematics, drive mechanisms, and dielectric performance. The workspace of the proposed arm is an oblate ellipsoid with minor and major axes measuring 1.25 m and 1.65 m, respectively, covering the concealed space in the cable tunnel, while the arm’s quality is 4.7 kg. The maximum electric field intensity is 74.3 kV/m under 220 kV operating voltage. The field value is less than the air breakdown threshold. The proposed mechanical arm design significantly improves spatial adaptability, operational efficiency, and reliability in HV live-line inspection, offering theoretical and practical advancements for intelligent maintenance in cable tunnel environments. Full article

Asymmetry in the supply voltage in three-phase circuits disrupts the flow of currents. This worsens the efficiency of the distribution system and increases the problems in determining the mathematical model of the energy system. Among many power theories, the most accurate is the Currents’ Physical Components (CPC) power theory, which tries to justify the physical essence of each component. Such knowledge can be used to improve efficiency and reduce transmission losses in the power system. This article discusses the method of mathematical decomposition of current components in the case of a three-wire line connecting an asymmetric power source with linear time-invariant (LTI) loads. Special cases where irregularities appear in the results of calculations according to the CPC theory are discussed. The problem of equivalent conductance in the case of a non-zero value of the constant voltage component is discussed. The method of determining symmetrical components for periodic non-sinusoidal waveforms is also discussed. These considerations are supported by numerical examples. Full article

The dynamic behavior of large-scale systems containing diverse devices is a crucial focus for system operators, particularly in power systems where grid-connected devices generate AC electrical signals through various modulated methods. One of the main objectives of power systems is to transmit power. To this end, time-varying amplitude/frequency rotating vectors are used to describe different AC voltage and current signals, and then the active/reactive power separation mechanisms and characteristics for different signal-modulated power devices are explored. These mechanisms and characteristics are analyzed and verified through time-domain simulations. The theoretical contribution of this paper is that it thoroughly clarifies the misconception in current power theories by demonstrating that active power and reactive power naturally arise as inherent physical quantities rather than being solely mathematically defined. In terms of practicality, this paper can provide physically grounded insights for the power calculation methods and offer guidance for the design of power measurement in actual power system dynamic processes. Through the analysis presented in this study, the analysis, measurement, and control of the active/reactive components in renewable energy equipment based on the instantaneous reactive power calculation method or the traditional sinusoidal steady-state power calculation method do not need to be updated. Full article

Defects are a direct cause of failure in ferromagnetic components, which can be evaluated via electromagnetic non-destructive testing (ENDT) methods. However, the existing studies exhibit several limitations (e.g., inaccurate quantification, over-reliance on algorithms, and non-intuitive result presentation, among others) in quantitative defect evaluation. To accurately describe the quantitative relationship between ENDT signals and defect dimensional parameters, the electromagnetic model and electromagnetic induction model are introduced in this paper to elucidate the physical mechanism of ENDT, as both models provide a basis for the selection of the constitutive relationship for simulation analysis. Then, a higher precision three-dimensional nonlinear finite element simulation model is established, and the effects of the excitation parameters and detection positions on signal characteristics are investigated. The simulation results indicate that the excitation frequency influences both the detection depth and sensitivity of ENDT, while the voltage amplitude affects the peak strength of the magnetic signal. Consequently, the excitation parameters are determined to be a 10 Hz frequency with a 25 V amplitude. Based on the characterization of signal peaks at positions of 0°, 90°, 180°, and 270°, the characteristic parameter K_A of the peak electrical signal curve is proposed as a quantitative index for evaluating defects. The quantitative experimental results show that K_A is related to the defect dimension. Specifically, the K_A value monotonically decreases from a constant greater than 1 to a constant less than 1 as the defect length increases, K_A is positively correlated with the defect width, and K_A follows a parabolic trend (first increase and then decrease) as the defect depth increases. Notably, K_A values associated with defect width and depth remain below 1. All the above findings provide a basis for evaluating defect dimensions. The results of this paper provide a novel ENDT method for evaluating defects, which is of great significance for improving the accuracy of ENDT and promoting its engineering applications. Full article

In the era of artificial intelligence, the demand for rapid and efficient data processing is growing, and traditional computing architectures are increasingly struggling to meet these needs. Against this backdrop, memristor devices, capable of mimicking the computational functions of brain neural networks, have emerged as key components in neuromorphic systems. Despite this, memristors still face many challenges in biomimetic functionality and circuit integration. In this context, a starch–glycerol-based hydrogel memristor was developed using starch as the dielectric material. The starch–glycerol–water mixture employed in this study has been widely recognized in literature as a physically cross-linked hydrogel system with a three-dimensional network, and both high water content and mechanical flexibility. This memristor demonstrates a high current switching ratio and stable threshold voltage, showing great potential in mimicking the activity of biological neurons. The device possesses the functionality of auditory neurons, not only achieving artificial spiking neuron discharge but also accomplishing the spatiotemporal summation of input information. In addition, we demonstrate the application capabilities of this artificial auditory neuron in gain modulation and in the synchronization detection of sound signals, further highlighting its potential in neuromorphic engineering applications. These results suggest that starch-based hydrogel memristors offer a promising platform for the construction of bio-inspired auditory neuron circuits and flexible neuromorphic systems. Full article

Magnetic pulse welding (MPW) is a promising solid-state joining process that utilizes electromagnetic forces to create high-speed, impact-like collisions between two metal components. This welding technique is widely known for its ability to join dissimilar metals, including aluminum, steel, and copper, without the need for additional filler materials or fluxes. MPW offers several advantages, such as minimal heat input, no distortion or warping, and excellent joint strength and integrity. The process is highly efficient, with welding times typically ranging from microseconds to milliseconds, making it suitable for high-volume production applications in sectors including automotive, aerospace, electronics, and various other industries where strong and reliable joints are required. It provides a cost-effective solution for joining lightweight materials, reducing weight and improving fuel efficiency in transportation systems. This contribution concerns an application for the automotive sector (body-in-white) and specifically examines the welding of AA6082-T6 aluminum alloy with HC420LA cold-rolled micro-alloyed steel. One of the main aspects for MPW optimization is the determination of the process window that does not depend on the equipment used but rather on the parameters associated with the physical mechanisms of the process. It was demonstrated that process windows based on contact angle versus output voltage diagrams can be of interest for production use for a given component (shock absorbers, suspension struts, chassis components, instrument panel beams, next-generation crash boxes, etc.). The process window based on impact pressures versus impact velocity for different impact angles, in addition to not depending on the equipment, allows highlighting other factors such as the pressure welding threshold for different temperatures in the impact zone, critical transition speeds for straight or wavy interface formation, and the jetting/no jetting effect transition. Experimental results demonstrated that optimal welding conditions are achieved with impact velocities between 900 and 1200 m/s, impact pressures of 3000–4000 MPa, and impact angles ranging from 18–35°. These conditions correspond to optimal technological parameters including gaps of 1.5–2 mm and output voltages between 7.5 and 8.5 kV. Successful welds require mean energy values above 20 kJ and weld specific energy values exceeding 150 kJ/m². The study establishes critical failure thresholds: welds consistently failed when gap distances exceeded 3 mm, output voltage dropped below 5.5 kV, or impact pressures fell below 2000 MPa. To determine these impact parameters, relationships based on Buckingham’s π theorem provide a viable solution closely aligned with experimental reality. Additionally, shear tests were conducted to determine weld cohesion, enabling the integration of mechanical resistance isovalues into the process window. The findings reveal an inverse relationship between impact angle and weld specific energy, with higher impact velocities producing thicker intermetallic compounds (IMCs), emphasizing the need for careful parameter optimization to balance weld strength and IMC formation. Full article

Driven by increasingly stringent carbon emission regulations from the International Maritime Organization (IMO), the maritime industry increasingly requires eco-friendly power systems and enhanced energy efficiency. Lithium-ion batteries, a core component of these systems, necessitate precise temperature management to ensure safety, performance, and longevity, especially under high-temperature conditions owing to the inherent risk of thermal runaway. This study proposes a sensorless temperature estimation method using a long short-term memory network. Using key parameters, including state of charge, voltage, current, C-rate, and depth of discharge, a MATLAB-based analysis program was developed to model battery dynamics. The proposed method enables real-time internal temperature estimation without physical sensors, demonstrating improved accuracy via data-driven learning. Operational data from the training vessel Hannara were used to develop an integrated organic Rankine cycle–energy storage system model, analyze factors influencing battery temperature, and inform optimized battery operation strategies. The results highlight the potential of the proposed method to enhance the safety and efficiency of shipboard battery systems, thereby contributing to the achievement of the IMO’s carbon reduction goals. Full article

We present a reduced-order battery management system (BMS) for lithium-ion cells in electric and hybrid vehicles that couples a physics-based single-particle model (SPM) derived from the Cahn–Hilliard phase-field formulation with a lumped heat-transfer model. A three-dimensional COMSOL^® 5.0 simulation of a LiFePO₄ particle produced voltage and temperature data across ambient temperatures (253–298 K) and discharge rates (1 C–20.5 C). Principal component analysis (PCA) reduced this dataset to five latent variables, which we then mapped to experimental voltage–temperature profiles of an A123 Systems 26650 2.3 Ah cell using a self-normalizing neural network (SNN). The resulting ROM achieves real-time prediction accuracy comparable to detailed models while retaining essential electrothermal dynamics. Full article

Harmonic currents can intensify transformer vibrations, seriously threatening their mechanical stability and safe operation. Drawing upon this foundation, the present paper undertakes a thorough simulation and experimental investigation into the vibration characteristics of transformers under diverse harmonic current scenarios. Initially, a multi-field coupling model incorporating both “electromagnetic and structural forces” was developed to simulate and analyze how the vibration acceleration of a transformer is distributed under varying harmonic currents. Subsequently, a specialized transformer harmonic loading and vibration measurement platform was constructed to validate the multi-physical-field vibration simulation. Finally, through a rigorous experimental analysis of transformer vibrations under harmonic currents, this research elucidates the variation patterns of characteristic vibration parameters of transformers under different harmonic currents. The results demonstrate that as the proportion of harmonic current grows, the mean winding vibration acceleration escalates following a power-function law. With increasing harmonic current frequency, the vibration acceleration augmentation in high-voltage (HV) windings exceeds that which is observed in low-voltage (LV) windings. Empirical validation confirms that the discrepancy between the measured and simulated acceleration increases remains within 5%, indicating the effectiveness and reliability of the simulation method. Experimental findings reveal that as the harmonic current content increases, six vibration characteristic parameters—including root mean square value, absolute average value, peak-to-peak value, and mean frequency—exhibit a pronounced upward trend. Furthermore, harmonic currents significantly increase the spectral dispersion and high-frequency components of the vibration signal. These research findings provide valuable references for transformer operation, maintenance, and anti-vibration design strategies. Full article

This study investigates the mechanisms driving current generation, power output, and charge storage in carbon nanotube springs under mechanical strain, addressing the gap between experimental observations and theoretical modeling, particularly in asymmetric electrical responses. Leveraging the Dirac equation in curved spacetime, we analyze how curvature-induced scalar and pseudo-gauge potentials shape two-dimensional electron gases confined to carbon nanotube springs. We incorporate applied mechanical strain by introducing time-dependent variations in the Lamé coefficient and curvature parameters, enabling the analysis of mechanical deformation’s influence on electrical properties. Our model clarifies asymmetric electrical responses during stretching and compression cycles and explains how strain-dependent power outputs arise from the interplay between mechanical deformation and curvature effects. Additionally, we demonstrate mechanisms by which strain influences charge redistribution within the helically coiled structure. We develop a new equivalent circuit model linking mechanical deformation directly to electronic behavior, bridging theoretical physics with practical electromechanical applications. The analysis reveals asymmetric time-dependent currents, enhanced power output during stretching, and strain-dependent charge redistribution. Fourier analysis uncovers dominant frequency components (primary at $\Omega$, harmonic at $2\Omega$) explaining these asymmetries. Theoretical investigations explain the mechanisms behind the curvature-driven time-dependent current source, the frequency-dependent peak power, the characteristics of open-circuit voltage with strain, and the asymmetric electrical property response under applied strain as the generated current and the charge distribution within the carbon nanotube springs. These findings highlight carbon nanotube springs applied to energy harvesting, wearable electronics, and sensing technologies. Full article