Both laser and write current influences write-ability and areal density capability (ADC) in heat-assisted magnetic recording (HAMR). Sufficiently high laser and write current is required to achieve a high proportion of magnetization switching of media grains, as measured by THMap, and obtain low bit error rate. However, increasing these currents also lead to a wider write width, resulting in a loss of ADC. This study compares these conditions necessary to achieve saturation of magnetization switching and those for maximizing ADC. ADC increases with write current up to the level required for saturation of magnetization switching. In contrast, higher laser current than that necessary for saturation of magnetization switching is required to maximize ADC. This is because increasing the laser current enhances the down-track thermal gradient and reduces bit-to-bit transition jitter. This finding suggests that achieving saturation of magnetization switching is a necessary condition for maximizing ADC. Furthermore, optimizing the laser current to reduce jitter plays a significant role in improving ADC.

Optimizing permanent magnet and slot shapes improves surface-mounted permanent magnet motor thrust performance. However, this optimization complicates air-gap boundary conditions, creating electromagnetic analysis challenges. To address this, this paper proposes a hybrid analytical method integrating equivalent magnetic network and subdomain approaches. Two key innovations enable its application in motors with non-uniform airgap length: First, arc-shaped permanent magnets are converted into rectangular equivalents. This geometric transformation achieves planar interfaces between PMs and air-gap regions, thereby reducing the computational complexity of magnetic vector potential solutions. Secondly, extending the equivalent magnetic network into the air-gap region decouples the core from the boundary. This eliminates repeated EMN discretization during slot shape optimization. The hybrid analytical model is employed to predict back electromotive force, detent force, and load thrust, which are validated by the finite element analysis. During motor optimization, the hybrid analytical model achieves an 89% computation time reduction compared to finite element method. Experimental results align with both the finite element method and hybrid model predictions, confirming the method’s applicability for rapid surface-mounted permanent magnet motor design.

This work presents, for the first time, a Physics Informed Neural Network (PINN) model for magnetic hyperthermia, a promising non-invasive cancer therapy known for its high efficacy and minimal side effects. Effective cancer cell destruction requires heating to 42-45 °C. The heat generated by magnetic nanoparticles (MNP) under an alternating magnetic field depends strongly on their physicochemical properties. Hence, the optimization of MNP for effective heat generation remains a key challenge and constitutes the fundamental motivation of this study. In this work, we study and compare various approaches employing regression models, Artificial Neural Network (ANN), and PINN to address the challenges associated with magnetic fluid hyperthermia (MFH) prediction and analysis. The model incorporates input parameters, including particle size, saturation magnetization, magnetic field intensity, frequency, specific heat of fluid, nanoparticle concentration, and time, to predict temperature evolution as the output. The dataset is compiled from our published research work, comprising 3,690 data points, ensuring sufficient variability and robustness for model training and evaluation. Our PINN model shows an excellent R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value of around 0.98 against the test data.

This work studies a magneto-optical surface plasmon resonance (MOSPR) sensor design that uses the photonic spin Hall effect (PSHE) concept, including cerium-substituted yttrium iron garnet (CeYIG) and graphene to allow for very sensitive detection of magnetic fields at 632.8 nm. When subjected to a magnetic field, the CeYIG layer exhibits anisotropic optical behavior, explained by a permittivity tensor with nonzero off-diagonal components, thereby inducing magneto-optical (MO) activity. The magnetization is oriented along the z-axis, normal to the sensor surface. Notably, the proposed design achieves an extraordinary transverse spin-dependent shift (SDS) of approximately 237.93 μm with the external orbital angular momentum (EOAM) based centroid shift of 0.508 μm at resonance angle. This enhancement has enabled the MOSPR sensor to use as an ultra-sensitive PSHE-based magnetometer, with a maximal sensitivity of 11258.36 μm/RIU at a refractive index (RI) of 1.1 for detecting minute RI variations in the sensing medium. The proposed MOSPR sensor, performance enhanced by the integration of CeYIG and graphene with PSHE, demonstrates the importance of this sensor for next-generation MO sensing devices.

Pulsed eddy current testing (PECT) is an electromagnetic nondestructive inspection technology capable of detecting pipe corrosion in-service without the need to remove the insulation coating. However, current PECT inspection methods based on intact conductor models lack sufficient accuracy in quantitative evaluation of localized corrosion defects, restricting their wider application. Based on the analytical model of the intact metal plate, this paper develops a defect equivalent source model to analytically solve the eddy current field of conductors containing flat-bottomed hole defects, thereby improving computational accuracy and efficiency. First, artificial current excitation sources are introduced in the defect region to establish a defect equivalent source model, and the boundary-value problem for this model is solved. Then an iterative algorithm is employed to determine the defect equivalent source magnitudes based on the condition that the total current density within the defect region is zero. Subsequently, the time-domain analytical solution for the induced voltage in the PECT of the metal plate with defects is derived using the superposition principle. Finally, experiments on an aluminum alloy plate containing cylindrical flat-bottom hole defects validate the accuracy of the proposed defect equivalent source model and demonstrate its feasibility and advantages in analytically solving the eddy current field involving defects. The findings significantly enrich the analytical methods for defect-containing eddy current field and lay a theoretical foundation for innovation in PECT technology for localized corrosion defects.

Hysteresis loss, the main component of core loss, is directly related to the motor core material. A nanocrystalline alloy with low hysteresis loss induced by low coercivity is developed by machine learning (ML). Firstly, the eXtreme Gradient Boosting (XGBoost) decision tree algorithm is employed to design amorphous alloys with good soft magnetic properties. Subsequently, the alloy Fe<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">77.5</sub>Co<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3.8</sub>B<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">15</sub>P<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.7</sub>Si<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub>Nb<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> designed is prepared by melt spinning and annealed at 470 °C for 180 s. The obtained coercivity (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H_c</i>) of the nanocrystalline alloy is 2.84 A/m, and the saturation magnetic flux density (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B_s</i>) is 1.88 T. Finally, hysteresis loss is investigated by the finite-element analysis of the motor with this nanocrystalline alloy stator core and compared with the results of the DW315-50 stator core motor. It is revealed that the hysteresis loss of the motor by the annealed alloy has been reduced by around 88% compared with the silicon steel stator core. It is indicated that the low <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H_c</i> nanocrystalline alloy designed by machine learning can effectively reduce the hysteresis loss of the motor, which is of great significance for industrial energy conservation.

In air-gapped inductors, the magnetic flux bulges outward from the gap - a phenomenon called the fringing effect. When these flux lines cut across the conductor windings, they generate undesired eddy currents that lead to substantial AC losses and reduced efficiency. To mitigate these losses, we develop a constrained shape optimization, aiming at finding an optimal air gap profile that minimizes conductor losses and keeps the inductance at a desired value. Our framework leverages established techniques such as complex permeability homogenization for loss modeling, shape sensitivity analysis coupling the adjoint method with the so-called Hilbertian regularization-extension method. The optimized air gap profile is curved to concentrate the flux lines away from the (fixed) conductors. Our implementation uses the open-source finite-element toolbox NGSolve and the Mmg remeshing library; all the codes are freely available.