Search Results (44)

Axial flux motors have gained widespread attention in the field of electric vehicles. The stator may exert a unilateral axial force on the dual rotors under uneven air gaps. The unbalanced magnetic pull can influence the production and processing of the motor, leading to issues such as vibrations, bearing degradation, reduced lifespan, and torque reduction attributed to the bearings. Accurate evaluation of the unilateral magnetic pull can reduce costs associated with bearing protection. For dual-rotor motors, the axial forces of the rotors act in opposite directions with nearly equal magnitudes, resulting in the catastrophic cancellation of unbalanced magnetic pull calculations. A similar phenomenon may occur between coils, introducing computational errors. To avoid these errors, the stator was selected as the computational target for unilateral axial force calculations. The integration domain was defined to encompass the entire air region containing all windings, rather than summing individual force components. This merged integration approach was mathematically validated through the Maxwell stress tensor method. Finally, the obtained stator axial force closely matched the rotor axial force in magnitude, demonstrating the accuracy of the proposed method. Full article

This paper proposes a 1200V 4H-SiC MOSFET incorporating a High-K dielectric-integrated fused source-gate (HKSG) structure, engineered to concurrently enhance the third-quadrant operation and high-frequency figure of merit (HF-FOM). The High-K dielectric enhances the electric field effect, reducing the threshold voltage of the source-gate. As a result, the reverse conduction voltage drops from 2.79 V (body diode) to 1.53 V, and the bipolar degradation is eliminated. Moreover, by incorporating a shielding area within the merged source-gate architecture, the gate-to-drain capacitance C_gd of the HKSG-MOS is reduced. The simulation results show that the HF-FOM C_gd × R_on,sp and Q_gd × R_on,sp of the HKSG-MOS are decreased by 48.1% and 58.9%, respectively, compared with that of conventional SiC MOSFET. The improved performances make the proposed SiC MOSFEET have great potential in high-frequency power applications. Full article

In the past few years, metal chalcogenides have received extensive consideration because of their excellent physicochemical belongings. Particularly, molybdenum selenide (MoSe₂) is a promising metal dichalcogenide which possesses decent optical, electrical, and chemical properties and can be explored for a variety of applications. MoSe₂ has been extensively used for several applications such as energy storage and sensing. Since the energy crisis is one of the major challenges of today’s world, super-capacitors and hydrogen evolution are promising energy technologies that may benefit the global world in the future. Thus, researchers have been motivated towards the strategy and fabrication of electrode materials for super-capacitors and hydrogen evolution applications. MoSe₂ is a multifunctional material, and previous years have witnessed the rapid growth in the publication of MoSe₂-based electrode materials for super-capacitors, hydrogen evolution, and electrochemical sensing applications. Thus, it is of great significance to merge the previous reports into a single review article on MoSe₂-based modified electrode materials for super-capacitors, hydrogen evolution, and electrochemical sensing applications. Therefore, we have compiled the previous reports on the design and fabrication of MoSe₂ and electrodes based on its composites for super-capacitors, hydrogen evolution, and electrochemical sensing applications. It is believed that this article may benefit the researchers working in the research field of super-capacitors, hydrogen evolution, and electrochemical sensing applications. Full article

This study investigated the correlations between field-aligned currents (FACs), polar electrojets (PEJs), and external solar and geomagnetic activity parameters during the intense geomagnetic storm that occurred from 10 to 12 October 2024. Notably, the merging electric field (Em) had a greater impact on FACs and PEJs compared to the May 2024 storm, while the influence of solar wind pressure (Pd) was equally important in both storms. The peak FAC densities in the northern dawn (southern dusk) and nighttime sectors correlate strongly with Em, whereas Pd dominates in the northern dusk (southern dawn) and daytime sectors. For PEJs, Em correlates strongly with current densities in the northern dawn–dusk and southern nighttime sectors, while Pd is the primary correlated parameter on the dayside. FAC (PEJ) latitudes are most strongly influenced by Em (Pd or Dst) on the dawnside–duskside. Additionally, FACs and PEJs are mostly more intense on the dawnside than on the duskside and extend to lower latitudes at dusk than at dawn. Analysis of the May and October 2024 storms reveals that FACs in the summer hemisphere are generally stronger and situated at more poleward latitudes than those in the winter hemisphere. This pattern is largely driven by summer–winter variations in ionospheric conductivity, with some differences also arising from the asymmetric magnetic field geometry between the two hemispheres. Full article

This study investigates the responses of the equatorial and low-latitude ionosphere in the American–Atlantic longitude sector during the super geomagnetic storm that occurred on 10–11 May 2024. The investigation utilizes multi-instrument datasets, including ground-based observations (GNSS TEC, ionosonde, and Fabry–Perot interferometer) as well as space-borne satellite measurements (GOLD, Swarm, DMSP, and TIMED). Our findings reveal significant day-to-day variations in the storm-time equatorial ionization anomaly (EIA), summarized as follows: (1) During the main phase of the storm, the low- and mid-latitude ionosphere experienced a positive storm, with TEC drastically enhanced by 50–100% within a few hours. The EIA crests exhibited a substantial poleward expansion, reaching as high as ±35° MLAT. This expansion was caused by the enhanced fountain effect driven by penetration electric fields, along with increased ambipolar diffusion due to transient meridional wind surges. (2) During the recovery phase of the storm, the global ionosphere was characterized by a substantial negative storm with a 50–80% depletion in TEC. The EIA crests were notably suppressed and merged into a single equatorial band, which can be attributed to the composition change effect and the influence of disturbance dynamo electric fields. These results illustrate the complex processes of magnetosphere–ionosphere–thermosphere coupling during a superstorm, highlighting the significant impacts of space weather on the global ionosphere. Full article

This paper presents a new approach in the field of probability-informed machine learning (ML). It implies improving the results of ML algorithms and neural networks (NNs) by using probability models as a source of additional features in situations where it is impossible to increase the training datasets for various reasons. We introduce connected mixture components as a source of additional information that can be extracted from a mathematical model. These components are formed using probability mixture models and a special algorithm for merging parameters in the sliding window mode. This approach has been proven effective when applied to real-world time series data for short- and medium-term forecasting. In all cases, the models informed by the connected mixture components showed better results than those that did not use them, although different informed models may be effective for various datasets. The fundamental novelty of the research lies both in a new mathematical approach to informing ML models and in the demonstrated increase in forecasting accuracy in various applications. For geophysical spatiotemporal data, the decrease in Root Mean Square Error (RMSE) was up to $27.7\%$, and the reduction in Mean Absolute Percentage Error (MAPE) was up to $45.7\%$ compared with ML models without probability informing. The best metrics values were obtained by an informed ensemble architecture that fuses the results of a Long Short-Term Memory (LSTM) network and a transformer. The Mean Squared Error (MSE) for the electricity transformer oil temperature from the ETDataset had improved by up to $10.0\%$ compared with vanilla methods. The best MSE value was obtained by informed random forest. The introduced probability-informed approach allows us to outperform the results of both transformer NN architectures and classical statistical and machine learning methods. Full article

This study investigates field-aligned currents (FACs) during strong magnetic storms in December 2023, analyzing variations in different local times and in the Northern (NH) and Southern Hemispheres (SH). Peak FAC densities were approximately 7.8 times higher than nominal values, with the most equatorward FACs reaching −52° magnetic latitude (MLat). In the summer hemisphere, the daytime FACs were stronger than the nighttime FACs, with the daytime westward Polar Electrojet (PEJ) surpassing nighttime levels. In the winter hemisphere, the nighttime FACs and westward PEJ were stronger than daytime. Generally, the FACs and westward PEJ were stronger in the SH than in the NH across most local time sectors, attributed to greater solar illumination. The NH pre-midnight currents were stronger than for the SH, indicating enhanced substorm currents during winter nights. The nighttime FACs occurred at lower MLat than daytime, with pre-noon FACs at a higher MLat than post-noon. The NH FACs were positioned more equatorward than their SH counterparts. In the NH, the mean FACs correlated most strongly with the merging electric field (Em) at pre-noon, post-noon, and post-midnight and with the SMU (SuperMAG Electrojet Upper Index) at pre-midnight. In the SH, the mean FACs correlated best with the SMU at pre-midnight/pre-noon, with the SML (SuperMAG Electrojet Lower Index) at post-midnight, and Em at post-noon. The mean MLat of the peak FACs show the strongest correlation with Em across most local times and hemispheres. Full article

Integrated photonics is a cutting-edge field that merges optics and electronics on a single microchip, revolutionizing how we manipulate and transmit light. Imagine traditional bulky optical systems condensed onto a chip smaller than a fingernail, enabling faster communication, more efficient sensors, and advanced computing. At its core, integrated photonics relies on guiding light through waveguides etched onto semiconductor substrates, analogous to how wires conduct electricity in traditional electric circuits. These waveguides can route, modulate, and detect light signals with unprecedented precision and speed. This technology holds immense promise across various domains. Despite its immense potential, integrated photonics faces challenges, including manufacturing complexities and integration with existing electronic systems. However, ongoing research and advancements continue to push the boundaries, promising a future where light-based technologies seamlessly integrate into our everyday lives, powering a new era of innovation and connectivity. Full article

Here we present the cascade converter (CC), which provides real-time imaging of ionizing radiation (IoR) distribution. It was designed and manufactured with the simplest architecture, utilizing liquid crystal display (LCD) technology. Based on two merged substrates with transparent electrodes, armed with functional layers, with the cell filled with nematic liquid crystal, a display-like, IoR-stimulated CC was achieved. The CC comprises low-absorbing polymer substrates (made of polyethylene terephthalate—PET) armed with a transparent ITO electrode covered with a thin semipermeable membrane of polymer (biphenylperfluorocyclobutyl: BP-PFCB) doped with functional nanoparticles (NPs) of Lu₂O₃:Eu. This stack was covered with a photoconductive layer of α-Se and finally with a thin polyimide (PI) layer for liquid crystal alignment. The opposite substrate was made of LCD-type glass with ITO and polyimide aligning layers. Both substrates form a cell with a twisted structure of nematic liquid crystal (TN) driven with an effective electric field $E_{eff}$. An effective electric field driving TN structure is generated with a sum of (1) a bias voltage $V_{BIAS}$ applied to ITO transparent electrodes and (2) the photogenerated additional voltage $V_{Xray}$ induced between ITO and α-Se layers with a NPs-doped BP-PFCB polymer layer in-between. The IoR (here, X-ray) conversion into real imaging of the IoR distribution was achieved in the following stages: (1) conversion of IoR distribution into non-ionizing red light emitted with functional NPs, (2) transformation of red light into an electric charge distributed in a layer of the photoconductive α-Se, which is what results in the generation of distributed voltage $V_{Xray}$, and (3) a voltage-mediated, distributed switching of the TN structure observed with the naked eye. The presented imaging device is characterized by a simple structure and a simple manufacturing process, with the potential for use as a portable element of IoR detection and as a dosimeter. Full article

Due to the unpredictable nature of the wind, uncertainty in the characteristics of wind electrical conversion systems (WECSs), and inefficient management tactics, wind turbines have historically had operational inefficiencies. In order to overcome these drawbacks, the hybrid excitation synchronous generator (HESG), an alternative to traditional generators, is presented in this study along with the suggestion to use robust regulators to regulate HESGs. This research begins with a thorough review of the literature on generators often seen in modern wind systems. Next, a simulation platform that merges a WECS with a HESG tied to an isolated load is built using the MATLAB Simulink environment. Pitch angle control investigation shows a new experimental approach to determine the link between turbine output and the reference pitch angle. Furthermore, an evaluation of the mechanical stability of the WECS is conducted by a comparison of the performance of a H∞ controller and a CRONE controller. The simulation results demonstrate the efficiency of the CRONE controller in reducing mechanical vibrations in the WECS. By reducing vibrations, the proposed control technique enhances the overall performance and efficiency of the wind turbine system. The field is extended by the demonstration of how HESGs and reliable control systems can enhance wind turbine performance while eliminating inherent limitations. Full article

Ultrafast laser processing possesses unique outlooks for the synthesis of novel nanoarchitectures and their further applications in the field of life science. It allows not only the formation of multi-element nanostructures with tuneable performance but also provides various non-invasive laser-stimulated modalities. In this work, we employed ultrafast laser processing for the manufacturing of silicon–gold nanocomposites (Si/Au NCs) with the Au mass fraction variable from 15% (0.5 min ablation time) to 79% (10 min) which increased their plasmonic efficiency by six times and narrowed the bandgap from 1.55 eV to 1.23 eV. These nanostructures demonstrated a considerable fs laser-stimulated hyperthermia with a Au-dependent heating efficiency (~10–20 °C). The prepared surfactant-free colloidal solutions showed good chemical stability with a decrease (i) of zeta (ξ) potential (from −46 mV to −30 mV) and (ii) of the hydrodynamic size of the nanoparticles (from 104 nm to 52 nm) due to the increase in the laser ablation time from 0.5 min to 10 min. The electrical conductivity of NCs revealed a minimum value (~1.53 µS/cm) at 2 min ablation time while their increasing concentration was saturated (~10¹² NPs/mL) at 7 min ablation duration. The formed NCs demonstrated a polycrystalline Au nature regardless of the laser ablation time accompanied with the coexistence of oxidized Au and oxidized Si as well as gold silicide phases at a shorter laser ablation time (<1 min) and the formation of a pristine Au at a longer irradiation. Our findings demonstrate the merged employment of ultrafast laser processing for the design of multi-element NCs with tuneable properties reveal efficient composition-sensitive photo-thermal therapy modality. Full article

To develop diagnostic imaging approaches, this paper emphasizes the transformational potential of merging geophysics with health sciences. Diagnostic imaging technology improvements have transformed the health sciences by enabling earlier and more precise disease identification, individualized therapy, and improved patient care. This review article examines the connection between geophysics and diagnostic imaging in the field of health sciences. Geophysics, which is typically used to explore Earth’s subsurface, has provided new uses of its methodology in the medical field, providing innovative solutions to pressing medical problems. The article examines the different geophysical techniques like electrical imaging, seismic imaging, and geophysics and their corresponding imaging techniques used in health sciences like tomography, magnetic resonance imaging, ultrasound imaging, etc. The examination includes the description, similarities, differences, and challenges associated with these techniques and how modified geophysical techniques can be used in imaging methods in health sciences. Examining the progression of each method from geophysics to medical imaging and its contributions to illness diagnosis, treatment planning, and monitoring are highlighted. Also, the utilization of geophysical data analysis techniques like signal processing and inversion techniques in image processing in health sciences has been briefly explained, along with different mathematical and computational tools in geophysics and how they can be implemented for image processing in health sciences. The key findings include the development of machine learning and artificial intelligence in geophysics-driven medical imaging, demonstrating the revolutionary effects of data-driven methods on precision, speed, and predictive modeling. Full article

Lithium-ion batteries, crucial in powering Battery Electric Vehicles (BEVs), face critical challenges in maintaining safety and efficiency. The quest for an effective Battery Thermal Management System (BTMS) arises from critical concerns over the safety and efficiency of lithium-ion batteries, particularly in Battery Electric Vehicles (BEVs). This study introduces a pioneering BTMS solution merging a two-phase immersion cooling system with heat pipes. Notably, the integration of NovecTM 649 as the dielectric fluid substantially mitigates thermal runaway-induced fire risks without requiring an additional power source. Comprehensive 1-D modeling, validated against AMESim (Advanced Modeling Environment for Simulation of Engineering Systems) simulations and experiments, investigates diverse design variable impacts on thermal resistance and evaporator temperature. At 10 W, 15 W, and 20 W heat inputs, the BTMS consistently maintained lithium-ion battery temperatures within the optimal range (approximately 27–34 °C). Optimized porosity (60%) and filling ratios (30–40%) minimized thermal resistance to 0.3848–0.4549 °C/W. This innovative system not only enhances safety but also improves energy efficiency by reducing weight, affirming its potential to revolutionize lithium-ion battery performance and address critical challenges in the field. Full article

This study investigates the propagation dynamics of plasma streamers in a packed-bed dielectric barrier discharge using a 2D particle-in-cell/Monte Carlo collision model. To accurately simulate the high-intensity discharge and streamer propagation mechanism at atmospheric pressure, additional algorithms for particle merging and a new electron mechanism are incorporated into the traditional particle-in-cell/Monte Carlo collision model. To validate the accuracy of this improved model, qualitative comparisons are made with experimental measurements from the existing literature. The results show that the speed of streamer propagation and the distribution of plasma are strongly influenced by the dielectric constant of the packed pellet, which is commonly used as a catalyst. In cases with a moderate dielectric constant, the presence of a strong electric field between the pellet and dielectric layer on the electrode significantly enhances the discharge. This enables the streamer to propagate swiftly along the pellet surface and results in a wider spread of plasma. Conversely, a very high dielectric constant impedes streamer propagation and leads to localized discharge with high intensity. The improved model algorithms derived from this research offer valuable insights for simulating high-density plasma discharge and optimizing plasma processing applications. Full article

Electric motors play a pivotal role in the functioning of autonomous vehicles, necessitating accurate fault diagnosis to ensure vehicle safety and reliability. In this paper, a novel motor fault diagnosis approach grounded in vibration signals to enhance fault detection performance is presented. The method involves capturing vibration signals from the motor across various operational states and frequencies using vibration sensors. Subsequently, the signals undergo transformation into frequency domain representations through fast Fourier transform. This includes normalizing and concatenating the amplitude frequency and phase frequency signals into comprehensive frequency domain information. Leveraging Gramian image-encoding attributes, cross-domain fusion of time-domain and frequency-domain data is achieved. Finally, the fused Gram angle field map is fed into the ConvMixer deep learning model, augmented by the ECA mechanism to facilitate precise motor fault identification. Experimental outcomes underscore the efficacy of cross-domain data fusion, showcasing improved pattern recognition and recognition rates for the models compared to traditional time-domain methods. Additionally, a comparative analysis of various deep learning models highlights the superior performance of the ECA-ConvMixer model. This study makes significant contributions by introducing a cross-domain data fusion method, merging time-domain and frequency-domain information to enhance motor vibration signal analysis. Additionally, the incorporation of the ECA-ConvMixer deep learning model, equipped with attention mechanisms, effectively captures critical features, thus serving as a robust tool for motor fault diagnosis. These innovations not only enhance diagnostic accuracy but also have broad applications in areas like autonomous vehicles and industry, leading to reduced maintenance expenses and enhanced equipment reliability. Full article