Search Results (303)

This paper proposes a fault-tolerant optimal design method for quad-winding permanent magnet synchronous motors (PMSMs) considering irreversible demagnetization under fault conditions. In quad-winding motors, when one or more winding sets become unavailable, the remaining windings must carry higher current to maintain the required torque. This increases the external magnetomotive force acting on the permanent magnets and may cause irreversible demagnetization, particularly in spoke-type magnet structures. To address this issue, the demagnetization characteristics of the quad-winding motor were analyzed under healthy and faulty operating conditions. Based on this analysis, an optimization process using a Radial Basis Function–Multi-Layer Perceptron (RBF–MLP) surrogate model and a combination of grid-based search and local optimization was applied to obtain an optimal motor design. The optimization results show that the irreversible demagnetization ratio was reduced from 5.9% to 0.5% while maintaining a similar magnet volume. The proposed design approach effectively suppresses irreversible demagnetization in quad-winding PMSMs. Full article

Objective: To report on a series of three cases in which problems with rotating magnets (blocked rotation, demagnetization) occurred in cochlear implants and to resolve these problems without surgical intervention. Methods: Of the 3635 devices with rotating magnets implanted at this tertiary referral hospital, 2 exhibited rotation blockage (associated with misalignment of the coil or audio processor), and 1 was partially demagnetized in a 1.5 T MRI scanner. Results: One blockage resolved spontaneously without intervention. The second blockage was resolved in the static field of a 3T MRI scanner, where the demagnetized magnet was also re-magnetized to its original strength. Surgical intervention or re-implantation was not necessary in either case. Conclusions: Surgical intervention or re-implantation is not primarily required in the event of problems with the rotating implant magnet. Prior to surgery, technical analysis can lead to a conservative solution. Full article

Transformers serve as crucial hubs for power transmission, but during no-load energization, the nonlinear magnetization of their cores frequently induces extreme magnetizing inrush currents. Current suppression methods encounter challenges regarding transient feature extraction and excessive circuit complexity. To overcome these limitations, this study develops a high-fidelity model of a 100 kVA transformer using MATLAB/Simulink to investigate the interaction between residual flux and the closing angle. Extensive simulations were executed across a closing phase angle range of 0° to 360° and a residual flux domain of −0.8 p.u. to 0.8 p.u. Furthermore, this study utilizes Wavelet and Clarke transforms to extract characteristic parameters and quantitatively analyze the transients within the energy domain, enabling a multi-scale assessment of the mitigation efficacy based on these extracted features. The analytical results demonstrate that an optimal pre-magnetization distribution of −0.8 p.u. for Phase A, 0 p.u. for Phase B, and 0.8 p.u. for Phase C, coupled with a target closing angle of 330°, achieves the best suppression. This strategy strictly clamps the peak inrush current to 1.5 times the rated current, significantly outperforming conventional demagnetization alone. Consequently, this highly pronounced mitigation effect provides robust support for reliable transformer protection and overall power grid security. Full article

To address the difficulty of detecting demagnetization severity in permanent magnet synchronous motors (PMSMs), this paper proposes a demagnetization severity detection method based on temperature signal and Convolutional Neural Network (CNN). First, the differences between local demagnetization and eccentricity fault in stator current harmonics are analyzed from an electromagnetic perspective, and fast Fourier transform (FFT) is used for frequency-domain analysis of the stator current to identify local demagnetization faults. On this basis, an electromagnetic–thermal coupling model is established by considering motor losses and heat dissipation boundary conditions to obtain the winding temperatures under different demagnetization severities and operating conditions. Furthermore, the temperature time series, together with speed and load torque, is constructed into a three-dimensional state space, and the proposed Conditionally Modulated Multi-Scale Convolutional Neural Network (CMSCNN) is introduced for feature learning to achieve demagnetization severity detection. Experimental results show that the proposed method achieves an average detection accuracy of 98.06% on the simulation test set and outperforms the baseline CNN model. On measured data collected from the faulty prototype, the average detection accuracy reaches 93.34%, verifying the effectiveness of the proposed method for demagnetization severity detection. Full article

Permanent magnet synchronous motors (PMSMs) have been widely used in new-energy vehicles and industrial servo systems. However, demagnetization faults (DMFs) can lead to severe issues, including torque ripple and magnetic field distortion. This paper proposes an intelligent diagnostic approach for DMFs based on stator tooth flux (STF). A mathematical model of STF is formulated, and the magnetic flux change is measured using multiple sets of anti-series-connected detection coils (DCs). By combining finite element simulation with signal processing technology, we establish a comprehensive diagnostic system covering fault feature extraction, fault location identification, and severity assessment is established. The proposed method employs wavelet transform (WT) to extract time-frequency features of voltage signals and combines it with a convolutional neural network (CNN) to form the WT-CNN intelligent diagnosis model. Based on the extracted voltage signal features, the method achieves intelligent identification and visual localization of DMFs. Simulation results show that the proposed method achieves an accuracy above 80% for fault location identification (defined as sample-level multi-label classification accuracy across 12 PMs) and above 85% for demagnetization severity estimation (defined as classification accuracy across 9 severity degrees from 10% to 90%). These results provide an effective technical foundation for motor condition monitoring and fault early warning in simulation environments. Full article

In this study, we propose a data-driven diagnostic system that uses Extreme Gradient Boosting (XGBoost) to detect, classify, and assess the severity of multiple faults in permanent magnet synchronous motors (PMSMs). The three main fault categories that are the focus of the suggested method are inter-turn short-circuit (ITSC) faults, stator open-circuit faults, and permanent magnet demagnetization. To capture fault-specific characteristics and their development with severity, discriminative electrical features are retrieved from stator currents, flux linkage, and dq-axis values. Next, using the chosen electrical indications, an aggregated diagnostic index is created to facilitate defect diagnosis and severity quantification in a single learning process. The XGBoost-based model has been shown to produce excellent diagnostic accuracy and robust separation between various fault causes via extensive assessment. It also maintains dependable performance under previously unknown operating or fault situations. These findings show that an XGBoost-only approach offers a scalable and efficient way to monitor advanced PMSM conditions in industrial and safety-critical applications. Full article

Dielectric and magnetic spherical hollow shells are employed in many applications as standard building units. These structures are commonly subjected to size reduction to obtain a high surface area/volume ratio, a property that is in favor of specific applications. However, the size reduction enhances the importance of physical mechanisms that originate from surfaces, such as the depolarization effect. Here we tackle the problem of dielectric and magnetic spherical hollow shells, consisting of a linear, homogeneous and isotropic parent material, subjected to an external potential, $U_{ext}(\mathit{r})$, of any spatial form (either dc (static) or ac of low-frequency (quasistatic limit)). By applying the method-of-linear-recursive-solution (MLRS) to the Laplace equation, we calculate analytically the internal, $U_{int}(\mathit{r})$, and total, $U_{tot}(\mathit{r})$, potentials in respect to the external one, $U_{ext}(\mathit{r})$. From $U_{int}(\mathit{r})$ and $U_{tot}(\mathit{r})$ we calculate all relevant scalar and vector physical entities of interest. The MLRS unveils straightforwardly the existence of two distinct depolarization factors, $N_l=l/(2l+1)$ and $N_{l+1}=(l+1)/(2l+1)$, both depending on the degree, $l$, however not on the order, $\mathrm{m}$, of the mode of the external potential, $U_{ext}^{(l,m)}(\mathit{r})$. These depolarization factors, $N_l$ and $N_{l+1}$, originate from the outer, $r=\mathrm{b}$, and inner, $r=\mathrm{a}$, surfaces and are accompanied by two extrinsic susceptibilities, ${\chi }_{e,l}^{ext}={\chi }_e/(1+N_l{\chi }_e)$ and ${\chi }_{e,l+1}^{ext}={\chi }_e/(1+N_{l+1}{\chi }_e)$, respectively. Importantly, $N_l+N_{l+1}=1$, irrespective of the degree, $l$, as it should. The properties of spherical hollow shells are investigated through analytical modeling and detailed simulations, with emphasis on application-relevant scenarios including resonance phenomena in scattering, quantitative materials characterization, and shielding/distortion. The generic MLRS strategy provides a flexible and reliable route for analyzing depolarization processes in other dielectric and magnetic building-unit geometries encountered in practice. Full article

In the aviation sector, there is a growing demand for high-specific-power electrical machines to realize More Electric Aircraft (MEA). The goals for these machines were set by the National Aeronautics and Space Administration (NASA) as 1 $\mathrm{M}$$\mathrm{W}$ power, >13 kW kg⁻¹ of power density, and efficiency >96%. To address these requirements, this paper proposes an electromagnetic design of a high-speed, power-dense, 1 $\mathrm{M}$$\mathrm{W}$ radial-flux Permanent Magnet Synchronous Machine (PMSM) for aerospace propulsion applications that achieves NASA targets. Achieving high-specific-power objectives necessitates geometry optimization that simultaneously minimizes motor mass while maximizing output power. This paper presents a faster optimization algorithm that hybridizes Genetic Algorithm and Artificial Neural Network (ANN)-based surrogate modeling to optimize the motor for multi-objective goals. The proposed framework employs a multi-objective approach targeting maximum torque output and efficiency within a minimum motor mass. This approach, using an ANN-based surrogate, significantly reduces optimization time by saving 95% of the time compared to FEM simulations. The optimized 1 $\mathrm{M}$$\mathrm{W}$ motor attains 98% efficiency and an active power density of 24.87 kW kg⁻¹. The various stages of the optimization are presented in detail and a comparison of the time saving using the proposed algorithm is outlined. To demonstrate the feasibility of design, a detailed electromagnetic analysis, stator thermal analysis with a jet impingement design, and magnet demagnetization risk analysis were also presented. Full article

This paper investigates the control of magnetic energy confinement in one- and three-dimensional magnetic systems by systematically accounting for magnetic interactions. The analysis provides new insight into magnetic behavior at the nanoscale and introduces a simulation-based framework that clearly distinguishes between magnetizing and demagnetizing interaction regimes. Within this framework, magnetic energy confinement is rigorously defined and can be quantitatively controlled through the underlying interaction landscape. To validate this approach, extensive numerical simulations were performed on representative one- and three-dimensional nanostructures, including individual nanowires and hexagonal arrays of nanowires. Each nanowire was modeled as a chain of interacting ellipsoidal grains, enabling an accurate description of the complex magnetic interactions governing energy confinement in these systems. Full article

Ultrafast spin dynamics is a core research focus for advancing ultrafast spintronic devices, yet its accurate quantitative probing remains a challenge with conventional time-resolved techniques. Herein, we employ double-pump optical pump–terahertz emission spectroscopy (OPTE) to investigate the ultrafast spin dynamics of a Pt/Gd₁₉(Co_0.8Fe_0.2)₈₁/Ta ferrimagnetic rare-earth–transition-metal heterostructure. Experimental measurements resolve a single-step ultrafast demagnetization process with a characteristic time of ~0.42 ± 0.02 ps, followed by two-stage magnetic recovery involving a fast relaxation and a slow relaxation process. The fast and slow recovery time constants show a distinct positive dependence on the control pump fluence, increasing from 2.49 ± 0.11 ps to 3.28 ± 0.03 ps and 57.36 ± 11.28 ps to 164.96 ± 1.61 ps, respectively, as the pump fluence rises from 0.80 to 1.19 mJ/cm². The ~0.42 ps demagnetization timescale is consistent with that of 3d transition metals, indicating the transient magnetic response of the low-Gd-concentration heterostructure is dominated by the CoFe sublattice. Our findings validate that OPTE is an effective approach for the quantitative characterization of electron–lattice–spin coupling processes in spin-based heterostructures and provide critical experimental insights for controllable manipulation of ultrafast spin dynamics, laying a foundation for the design of ultrafast terahertz spintronic devices. Full article

Online temperature estimation of key components (windings and magnets) in permanent magnet synchronous motors (PMSMs) has emerged as a critical technology for ensuring the safe operation of PMSMs, preventing insulation degradation, and avoiding the demagnetization of magnets. Because of such advantages, online temperature estimation is attracting growing attention from fields with stringent reliability requirements, such as electric vehicles, as well as electrified railway transportation and more/all-electric aircraft, where similar high-reliability demands exist. This paper gives a comprehensive review of the latest and most effective solutions in the online temperature estimation methods for PMSMs. It analyzes the principles, application progress, and limitations of existing methods, including electrical model-based approaches, thermal model-based approaches, and data-driven approaches, in which process the advantages and challenges of different methods are compared. And an outlook on the future application of this technology are summarized. Full article

Adiabatic demagnetization refrigeration (ADR) has attracted considerable attention as an effective approach to reach ultra-low temperatures required for fundamental physics and quantum technologies. Here we directly characterize the cryogenic magnetocaloric performance of the rare-earth-based double-perovskite oxide Gd₂CuTiO₆ (GCTO) through quasi-adiabatic demagnetization measurements. Magnetization measurements show no long-range magnetic transition above 1.8 K and indicate dominant antiferromagnetic (AFM) interactions, consistent with an AFM ordering temperature of $T_{\mathrm{N}}\approx 1.15$ K reported previously. Notably, the isothermal magnetization $M\left(H\right)$ at 1.8 K deviates from an ideal single-ion Brillouin response and is better described by a molecular-field correction for the Gd sublattice, suggesting correlated paramagnetism persisting above $T_{\mathrm{N}}$. In contrast to previous studies that inferred cooling performance from thermodynamic estimates, we directly validate the achievable sub-Kelvin cooling in GCTO through quasi-adiabatic measurements. In the quasi-ADR process starting from $T_0$∼2 K, demagnetization fields of 4, 6, and 9 T yield minimum temperatures of $T_{\min }=761.5$, 452.4, and 289.2 mK, respectively, well below $T_{\mathrm{N}}$. After complete removal of the magnetic field, the sample temperature remains highly stable for at least several tens of minutes, demonstrating a long hold time under quasi-adiabatic conditions. Moreover, the $T\left(H\right)$ curves reveal a characteristic field scale around $H_{\mathrm{c}}$∼1 T, implying a field-induced modification of the low-temperature magnetic-entropy landscape that is relevant to the cooling behavior during demagnetization. These results highlight GCTO as a promising magnetic refrigerant for sub-Kelvin ADR applications and underscore the role of correlated magnetism in optimizing cryogenic magnetocaloric performance. Full article

Nd-based ThMn₁₂ alloys exhibit significant potential as rare-earth (RE)-lean permanent magnets; however, their reliance on a nitriding process imposes limitations on densification due to the thermal instability of nitrides. Herein, we investigate the substitution of Nd with Sm in nanocrystalline melt-spun (Nd_1−xSm_x)_1.2Fe_10.5Mo_1.5 alloys to enhance magnetic performance without nitrogenation. The results confirm that Sm substitution preserves the tetragonal ThMn₁₂-type phase as the dominant matrix across all alloys, ensuring structural stability. Magnetic measurements demonstrate a significant enhancement in both coercivity µ₀H_c and remanence µ₀M_r, attributed to the strengthened magnetocrystalline anisotropy and improved squareness of the demagnetization curves induced by Sm substitution. Furthermore, microstructural characterization indicates that Sm facilitates the preferential formation of the REFe₇ phase under identical rapid solidification conditions. This work provides a strategic pathway to tailoring the magnetic properties of Nd-based ThMn₁₂ alloys, rendering them capable of exhibiting permanent magnet behavior without nitrogenation. Full article

This work presents a detailed micromagnetic analysis of the magnetization reversal process in hollow iron nanospheres with a shell thickness of 16 nm. Using the Ubermag computational framework coupled to the OOMMF, we demonstrate that these nanospheres exhibit high coercivity and remanence, producing elongated hysteresis loops, consistently with previous experimental findings. The reversal process is governed by the nucleation and evolution of non-collinear magnetic domains and domain walls, as revealed by magnetization mapping. A comprehensive energetic evaluation indicates a dynamic competition among anisotropy, exchange, Zeeman, and demagnetizing energies, with the latter exerting a dominant influence on the final magnetic configuration. These results enhance our understanding of the magnetic behavior in hollow nanostructures and provide a theoretical foundation for their application in spintronic and biomedical systems. Full article

With the core advantages of high energy efficiency, high power density, and reliable operation, high-voltage permanent magnet motors have become the mainstream development direction of modern motor technology. However, the risk of demagnetization caused by excessive temperature increases in permanent magnets has become a key bottleneck restricting motor performance and operational reliability, which makes research on the flow and heat transfer characteristics of motor cooling systems of great engineering value. Taking the 710 kW high-voltage permanent magnet motors as the research object, this study established a global flow field mathematical model covering the internal and external air duct cooling systems of the motor based on the theories of computational fluid dynamics and numerical heat transfer, and systematically analyzed the flow characteristics and distribution laws of cooling air. The thermal–fluid coupling numerical method was employed to simulate the temperature field of the motor, and the overall temperature distribution of the motor, temperature gradient of key components, and maximum temperature value were accurately obtained. To verify the validity of the established model, a test platform for the cooling system performance was designed and built. Measuring points for wind speed, air temperature, and component temperature were arranged at key positions, such as the stator radial ventilation ducts, and experimental tests were conducted under the rated operating conditions. The results show that the flow field distribution of the internal and external air ducts of the motor is reasonable and that the cooling air flows uniformly, with the external and internal circulating air volumes reaching 1.2 m³/s and 0.6 m³/s, respectively, which meets the heat dissipation requirements. The maximum temperature of 95 °C occurs in the stator winding area, and the maximum temperature of the permanent magnets is controlled within the safe range of 65 °C. The simulation results were in good agreement with the experimental data, with an average relative error of only 4%, which fell within the engineering allowable range, thus verifying the accuracy and reliability of the established global model and thermal–fluid coupling calculation method. This study reveals the thermal–fluid coupling transfer mechanism of high-voltage permanent magnet motors and provides a theoretical basis and engineering reference for the optimal design, precise temperature rise control, and reliability improvement of motor cooling systems. Full article