Search Results (3,133)

Electromagnetic and magnetic devices are increasingly prevalent in sectors such as transportation, industry, and renewable energy due to the ongoing electrification trend. These devices exhibit nonlinear behavior, particularly under signals rich in harmonics. They require precise and appropriate modeling for accurate sizing. Identifying model-specific parameters, which depend on frequency, is crucial. This article focuses on a specific frequency range where a circuit model with series resistance and inductance, along with a parallel resistance to account for iron losses (${\mathrm{R}}_{\mathrm{iron}}$), is applicable. While the determination of series elements is well documented, the determination of ${\mathrm{R}}_{\mathrm{iron}}$ remains complex and debated, with traditional methods neglecting operating conditions such as magnetic saturation. To address these limitations, an innovative experimental method is proposed, comprising two main steps: determining the complex impedance of the magnetic device and extracting ${\mathrm{R}}_{\mathrm{iron}}$ from the model. This method aims to provide a more precise and representative estimation of ${\mathrm{R}}_{\mathrm{iron}}$, improving the reliability and accuracy of electromagnetic and magnetic device simulations and designs. The obtained values of the iron loss equivalent resistance are different by at least 300% than those obtained by an impedance analyzer. The proposed method is expected to advance the understanding and modeling of losses in electromagnetic and magnetic devices, offering more robust tools for engineers and researchers in optimizing device performance and efficiency. Full article

The present paper proposes a three-dimensional (3D) spherical shell model for the magneto-electro-elastic (MEE) free vibration analysis of simply supported multilayered smart shells. A mixed curvilinear orthogonal reference system is used to write the unified 3D governing equations for cylinders, cylindrical panels and spherical shells. The closed-form solution of the problem is performed considering Navier harmonic forms in the in-plane directions and the exponential matrix method in the thickness direction. A layerwise approach is possible, considering the interlaminar continuity conditions for displacements, electric and magnetic potentials, transverse shear/normal stresses, transverse normal magnetic induction and transverse normal electric displacement. Some preliminary cases are proposed to validate the present 3D MEE free vibration model for several curvatures, materials, thickness values and vibration modes. Then, new benchmarks are proposed in order to discuss possible effects in multilayered MEE curved smart structures. In the new benchmarks, first, three circular frequencies for several half-wave number couples and for different thickness ratios are proposed. Thickness vibration modes are shown in terms of displacements, stresses, electric displacement and magnetic induction along the thickness direction. These new benchmarks are useful to understand the free vibration behavior of MEE curved smart structures, and they can be used as reference for researchers interested in the development of of 2D/3D MEE models. Full article

The alloy Pr${}_2$Fe${}_{16.75}$Ni${}_{0.25}$ has been examined to investigate its structural properties, critical behavior, and magnetocaloric effects. Rietveld’s refinement of X-ray diffraction patterns has revealed a rhombohedral structure with an $R\overline{3}m$ space group. Pr${}_2$Fe${}_{16.9}$Ni${}_{0.25}$ also demonstrates a direct magnetocaloric effect near room temperature, accompanied by a moderate magnetic entropy change ($-\Delta S_M^{\max }$ = 5.5 J kg${}^{-1}$ K${}^{-1}$ at ${\mu }_0\Delta H=5$ T) and a broad working temperature range. Furthermore, the Relative Cooling Power (RCP) is approximately 89% of the widely recognized gadolinium (Gd) for ${\mu }_0\Delta H=2$ T. This compound exhibits a commendable magnetocaloric response, on par with or even surpassing that of numerous other intermetallic alloys. Critical behavior was analyzed using thermo-magnetic measurements, employing methods such as the modified Arrott plot, critical isotherm analysis, and Kouvel-Fisher techniques. The obtained critical exponents ($\beta$, $\gamma$, and $\delta$) exhibit similarities to those of the 3D-Ising model, characterized explicitly by intermediate range interactions. Full article

The current paper presents a comprehensive intelligent system designed to optimize the performance of a barrier eddy current separator (BECS), comprising a conveyor belt, a vibration feeder, and a magnetic drum. This system was trained and validated on real-world industrial data gathered directly from the working separator under 81 different operational scenarios. The intelligent models were used to recommend optimal settings for drum speed, belt speed, vibration intensity, and drum angle, thereby maximizing separation quality and minimizing energy consumption. the smart separation module utilizes YOLOv11n-seg and achieves a mean average precision (mAP) of 0.838 across 7163 industrial instances from aluminum, copper, and plastic materials. For shape classification (sharp vs. smooth), the model reached 91.8% accuracy across 1105 annotated samples. Furthermore, the thermal monitoring unit can detect iron contamination by analyzing temperature anomalies. Scenarios with iron showed a maximum temperature increase of over 20 °C compared to clean materials, with a detection response time of under 2.5 s. The architecture integrates a Digital Twin using Azure Digital Twins to virtually mirror the system, enabling real-time tracking, behavior simulation, and remote updates. A full connection with the PLC has been implemented, allowing the AI-driven system to adjust physical parameters autonomously. This combination of AI, IoT, and digital twin technologies delivers a reliable and scalable solution for enhanced separation quality, improved operational safety, and predictive maintenance in industrial recycling environments. Full article

Background and Objective: Temperature management is key for reliable operation of permanent magnet synchronous motors (PMSMs). The lumped-parameter thermal network (LPTN) is fast and interpretable but struggles with nonlinear behavior under high power density. We propose OLTEM, a physics-informed deep model that combines LPTN with a thermal neural network (TNN) to improve prediction accuracy while keeping physical meaning. Methods: OLTEM embeds LPTN into a recurrent state-space formulation and learns three parameter sets: thermal conductance, inverse thermal capacitance, and power loss. Two additions are introduced: (i) a state-conditioned squeeze-and-excitation (SC-SE) attention that adapts feature weights using the current temperature state, and (ii) an enhanced power-loss sub-network that uses a deep MLP with SC-SE and non-negativity constraints. The model is trained and evaluated on the public Electric Motor Temperature dataset (Paderborn University/Kaggle). Performance is measured by mean squared error (MSE) and maximum absolute error across permanent-magnet, stator-yoke, stator-tooth, and stator-winding temperatures. Results: OLTEM tracks fast thermal transients and yields lower MSE than both the baseline TNN and a CNN–RNN model for all four components. On a held-out generalization set, MSE remains below 4.0 °C² and the maximum absolute error is about 4.3–8.2 °C. Ablation shows that removing either SC-SE or the enhanced power-loss module degrades accuracy, confirming their complementary roles. Conclusions: By combining physics with learned attention and loss modeling, OLTEM improves PMSM temperature prediction while preserving interpretability. This approach can support motor thermal design and control; future work will study transfer to other machines and further reduce short-term errors during abrupt operating changes. Full article

Spin transport behaviors in heavy metal/ferromagnetic insulator (HM/FI) bilayers have attracted considerable attention due to various novel phenomena and applications in spintronic devices. Herein, we investigate the planar Hall effect (PHE) in Pt/Tm₃Fe₅O₁₂ (Pt/TmIG) heterostructures at low temperatures; moment switching in the ferrimagnetic insulator TmIG is detected by using electrical measurements. Double switching hysteresis PHE curves are found in Pt/TmIG bilayers, closely related to the magnetic moment of Tm³⁺ ions, which makes a key contribution to the total magnetic moment of TmIG film at low temperature. More importantly, a magnetoresistance (MR) curve with double switching is found, which has not been reported in this simple HM/FI bilayer, and the sign of this MR effect is sensitive to the angle between the magnetic field and current directions. Our findings of these effects in this HM/rare earth iron garnet (HM/REIG) bilayer provide insights into tuning the spin transport properties of HM/REIG by changing the rare earth. Full article

The transition to sustainable energy systems demands efficient recycling methods for critical raw materials like lithium. In this study, we present a new class of pH- and light-switchable flotation collectors based on isomeric derivatives of the natural product Punicine, termed inverse Punicines. These amphoteric molecules were synthesized via a straightforward four-step route and structurally tuned for hydrophobization by alkylation. Their performance as collectors was evaluated in microflotation experiments of lithium aluminate (LiAlO₂) and silicate matrix minerals such as melilite and calcium silicate. Characterization techniques including ultraviolet-visible (UV-Vis), nuclear magnetic resonance (NMR) and electron spin resonance (ESR) spectroscopy as well as contact angle, zeta potential (ζ potential) and microflotation experiments revealed strong pH- and structure-dependent interactions with mineral surfaces. Notably, N-alkylated inverse Punicine derivatives showed high flotation yields for LiAlO₂ at pH of 11, with a derivative possessing a dodecyl group attached to the nitrogen as collector achieving up to 86% recovery (collector conc. 0.06 mmol/L). Preliminary separation tests showed Li upgrading from 5.27% to 6.95%. Radical formation and light-response behavior were confirmed by ESR and flotation tests under different illumination conditions. These results demonstrate the potential of inverse Punicines as tunable, sustainable flotation reagents for advanced lithium recycling from complex slag systems. Full article

Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) and wavelet-based methods, are primarily designed for steady-state conditions and rely on manual feature selection, limiting their applicability in real-time embedded systems. Furthermore, the lack of publicly available, high-fidelity datasets capturing the transient dynamics and nonlinear flux-linkage behaviors of PMSMs under fault conditions poses an additional barrier to developing data-driven diagnostic solutions. To address these challenges, this study introduces a simulation framework that generates a comprehensive dataset using finite element method (FEM) models, incorporating magnetic saturation effects and inverter-driven transients across diverse EV operating scenarios. Time-frequency features extracted via Discrete Wavelet Transform (DWT) from stator current signals are used to train a Transformer model for automated ITSC fault detection. The Transformer model, leveraging self-attention mechanisms, captures both local transient patterns and long-range dependencies within the time-frequency feature space. This architecture operates without sequential processing, in contrast to recurrent models such as LSTM or RNN models, enabling efficient inference with a relatively low parameter count, which is advantageous for embedded applications. The proposed model achieves 97% validation accuracy on simulated data, demonstrating its potential for real-time PMSM fault detection. Additionally, the provided dataset and methodology contribute to the facilitation of reproducible research in ITSC diagnostics under realistic EV operating conditions. Full article

Transition-metal and rare-earth element co-doped ZnO nanoparticles have attracted significant attention due to their potential applications in spintronics and optoelectronics. In this study, Zn_0.95Co_0.01EuxO (x = 0.01–0.05) nanoparticles were synthesized using the sol–gel technique. The estimated stress, strain, and crystallite sizes of the synthesized Co/Eu co-doped ZnO nanoparticles were calculated using the Williamson–Hall method, and their electron spin resonance (ESR) properties were investigated to examine the effect on their magnetic and structural properties. X-ray diffraction (XRD) analysis confirmed the presence of a single-phase structure. Surface morphology, elemental composition, crystal quality, defect types, density, and magnetic behavior were characterized using scanning electron microscope (SEM), electron-dispersive spectroscopy (EDS), and ESR techniques, respectively. The effect of Eu concentration on the linewidth (ΔBpp) and g-factor in the ESR spectra was studied. By correlating ESR results with the obtained structural properties, room-temperature ferromagnetic behavior was identified. Full article

High-gradient magnetic separation (HGMS) is a conventional and effective method for processing weak magnetic materials. A multi-field dynamic coupling simulation method integrating the Finite Element Method (FEM), Computational Fluid Dynamics (CFD), and the Discrete Element Method (DEM) was employed to investigate the separation behavior in HGMS. The dynamic deposition process of magnetic particles under the interactions of magnetic fields, fluid flow fields, and particle–particle forces was simulated using a two-way fluid–solid coupling algorithm based on the FEM-CFD-DEM coupling approach. Experimental results demonstrated that the particle deposition profiles predicted by the double-wire medium model were in good agreement with the measured data. The research findings indicated that the separation process could be divided into three distinct stages—the adsorption stage, the closure stage, and the clogging stage—each characterized by unique dynamic behaviors and pressure-drop evolution patterns. Additionally, the effects of key parameters such as the feeding velocity and medium filling ratio on the separation process were analyzed, providing theoretical foundations and technical support for the optimization of HGMS processes and the enhancement of separation efficiency. Full article

Acid mine drainage, a consequence of exposure of sulfide mining waste to weathering processes, results in significant water, sediment, and soil contamination. This contamination results in acidophilic ecosystems, with low pH values and elevated concentrations of sulfate and potentially toxic elements. The São Domingos mine, an abandoned site in the Iberian Pyrite Belt, lacks remediation measures and has numerous waste dumps, which are a major source of contamination to local water systems. Therefore, this study examines sediment accumulation in five mine dams along the São Domingos stream that traverses the entire mine complex. Decades of sediment and waste transport since mine closure have resulted in dam-clogging processes. The geochemical, mineralogical, and magnetic properties of the sediments were analyzed to evaluate the mineralogical controls on the mobilization of potentially toxic elements. The sediments are dominated by iron oxides, oxyhydroxides, and hydroxysulfates, with jarosite playing a key role in binding high concentrations of iron and toxic elements. However, no considerable correlation was found between potentially toxic elements and magnetic parameters, highlighting the complex behavior of these contaminants in acid mine drainage-affected systems. Full article

Using neural networks has become the standard technique for medical diagnostics, especially in cancer detection and classification. This work evaluates the performance of Vision Transformer architectures, including Swin Transformer and MaxViT, for several datasets of magnetic resonance imaging (MRI) and computed tomography (CT) scans. We used three training sets of images with brain, lung, and kidney tumors. Each dataset included different classification labels, from brain gliomas and meningiomas to benign and malignant lung conditions and kidney anomalies such as cysts and cancers. This work aims to analyze the behavior of the neural networks in each dataset and the benefits of combining different image modalities and tumor classes. We designed several experiments by fine-tuning the models on combined and individual datasets. The results revealed that the Swin Transformer achieved the highest accuracy, with an average of 99.0% on single datasets and reaching 99.43% on the combined dataset. This research highlights the adaptability of Transformer-based models to various human organs and image modalities. The main contribution lies in evaluating multiple ViT architectures across multi-organ tumor datasets, demonstrating their generalization to multi-organ classification. Integrating these models across diverse datasets could mark a significant advance in precision medicine, paving the way for more efficient healthcare solutions. Full article

Magnetorheological fluids (MRFs) and elastomers (MREs) are two types of smart materials that exhibit modifiable rheological properties in response to an applied magnetic field. Although they share a similarity in their magnetorheological response, these two materials differ in their nature, structure, and mechanical behavior when exposed to a magnetic field. They also have distinct application differences due to their specific rheological properties. These fundamental differences therefore influence their properties and applications in various industrial fields. This review provides a synthesis of the distinct characteristics of MRFs and MREs. The differences in their composition, rheological behavior, mechanical properties, and respective applications are summarized and highlighted. This analysis will enable a comprehensive understanding of these differences, thereby allowing for the appropriate selection of the material based on the specific requirements of a given application and fostering the development of new applications utilizing these MR materials. Full article

Magnetic nanowires with domain walls (DWs) play a crucial role in the advancement of next-generation memory and spintronic devices. Understanding the thermal effects on domain wall behavior is essential for optimizing performance and stability. This study investigates the thermal chirality-dependent dynamics and pinning of transverse domain walls (TDWs) in Z-junction nanowires using micromagnetic simulations. The analysis focuses on head-to-head (HHW) and tail-to-tail (TTW) domain walls with up and down chirality under varying thermal conditions. The results indicate that higher temperatures reduce the pinning strength and depinning current density, leading to enhanced domain wall velocity. At 200 K, the HHW_down domain wall depins at a critical current density of 1.2 × 10¹¹ A/m², while HHW_up requires a higher depinning temperature, indicating stronger pinning effects. Similarly, the depinning temperature (T_d) increases with Z-junction depth (d), reaching 300 K at d = 50 nm, while increasing Z-junction (λ) weakens pinning, reducing T_d to 150 K at λ = 50 nm. Additionally, the influence of Z-junction geometry and magnetic properties, such as saturation magnetization (M_s) and anisotropy constant (K_u), is examined to determine their effects on thermal pinning and depinning. These findings highlight the critical role of chirality and thermal activation in domain wall motion, offering insights into the design of energy-efficient, high-speed nanowire-based memory devices. Full article

This article presents a non-linear MEC for a linear PM motor, and its experimental validation. In the MEC model, winding flux leakage and iron saturation are considered. In addition, two different linear PM motor models (bread-loaf and surface-type) are examined for linear PM motors. An iterative method is used to predict the magnetic behavior of saturated magnetic steel. The proposed MEC for linear PM motors is compared with finite element analysis (FEA) to determine its accuracy and suitability. FEA is widely regarded as a highly accurate and reliable tool for analyzing linear PM motors. However, its primary limitation lies in its considerable computational time requirement. This disadvantage becomes particularly problematic during the early stages of the design process. Therefore, the proposed model addresses this limitation. Also, experimental results validate the practicality of the MEC. Finally, the proposed model can be a tool for different slot/pole combinations. Thus, the model can be considered suitable for both bread-loaf and surface-type PM motors. Full article