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A mixed domain decomposition method (DDM) is presented in this article. This method falls within the needs of advanced numerical optimization of electrical machines. Thus, we aim to adapt the LATIN method to the magnetostatic equations, as it has been so far mainly used for mechanical studies. The method provides a powerful iterative scheme that relies on mixed writing of the equations on the domain interfaces, including both primal and dual fields. The method is presented in detail in this article, and its performances are evaluated on a first implementation for the simulation of Team Problem No. 24. [ABSTRACT FROM AUTHOR]

This article proposes a computationally efficient thermal study method that substitutes computational fluid dynamics (CFDs) with a finite-element analysis (FEA) involving minimal loss of accuracy and omission of the increased computational load. The suggested technique contributes toward the realization of multiphysics optimization of high-speed electrical machines that effectively includes critical thermal evacuation phenomena. The strategy is regression-based and succeeds in constructing convection coefficient regression models that typically involve many variables and small training datasets. The proposed scheme is verified in high-loss density and high-speed outer rotor surface-mounted permanent magnet (SMPM) motor geometries that an optimization algorithm typically encounters during execution. The reference target specifications correspond to 20 kW output power, 15k r/min rotating speed, and 2 kHz electrical frequency. [ABSTRACT FROM AUTHOR]

Topology optimization (TO) has been formulated as a quadratic unconstrained binary optimization (QUBO) problem that can be solved by Digital Annealer (DA) and quantum computers, which can realize massive parallelization. It is shown that the 3-D TO of a permanent magnet (PM), with 600 unknowns, is successfully performed by the DA with the aid of the finite element method (FEM) for field computations. Moreover, it is shown to perform TO of a magnetic core, with 758 unknowns, in such a way that the QUBO and field problems are iteratively solved to determine the optimal solution in which the magnetic field and optimal core structure are self-consistent. [ABSTRACT FROM AUTHOR]

This article proposes a novel 2.5-D multi-phase topology optimization method using a Gaussian basis function for permanent magnet motors. The design region in the rotor was sliced into cylindrical layers; the 2-D topology optimization was performed for each layer such that the average torque was maximized, while the torque ripple was suppressed to the maximum possible extent. The proposed topology optimization could determine the rotor core and magnet shapes, as well as the magnetization direction. It was shown that the optimized 2.5-D topology optimization led to better torque performance when compared to conventional 2-D optimizations. [ABSTRACT FROM AUTHOR]

In this article, a method for topology optimization (TO) of a synchronous reluctance motor (SynRM) is proposed using deep reinforcement learning (RL). Due to the need for simulating a large number of finite-element models in a traditional TO task, incorporating a study involving a different problem formulation (such as a varying design domain) can be an overwhelming task. A neural network (NN)-based agent trained using an RL formulation is able to extend the knowledge from one TO design problem to other similar TO tasks. The applicability of such learning is performed using a sequence-based TO environment. It is observed that such an approach not only reduces the computation required for TO, but also introduces the capability to generalize RL to unseen TO scenarios. The proposed optimization method reduces computation time by 70%–90% when compared to a genetic algorithm-based implementation. [ABSTRACT FROM AUTHOR]

In topology optimization (TO), the finally manufactured topology will inherently deviate from the optimized one due to both curved lines or smoothed surfaces being used in the real-world topology and the manufacture error (ME). Generally, such deviation or tolerance is called an uncertainty in the topology. However, the deviation of the built topology from the optimized one may result in degradation of the performance of the optimized solution for microscale magnetic devices. Therefore, robust optimization (RO) considering ME and topology deviations (TDs) has become a hot concern in TO problems. Consequently, it is demanding to develop feasible and efficient procedures for TOs considering MEs and TDs. In this respect, a novel methodology based on the min-cut theorem and Bezier curve (MCBC) is first proposed. Based on the min-cut-based methodology for deterministic TOs, the Bezier curve is proposed to generate interface distortion realistically. The numerical result on the TO of a magnetic actuator has demonstrated that the proposed methodology has a good ability to obtain optimized topology insensitive to MEs and TDs. [ABSTRACT FROM AUTHOR]

A transient model of an induction machine (IM) is developed in this work using an artificial neural network (ANN) surrogate model. The model is suitable to be used for direct-on-line IMs. The finite-element (FE)-based model of IM is used to generate the training, validation, and testing datasets. Different inputs and model configurations are investigated to find an optimal solution in developing the transient model. The proposed transient model is suitable to be used in digital twin services since it can estimate the current and torque accurately in real time based on only voltage and measured shaft speed. [ABSTRACT FROM AUTHOR]

This article proposes a new topology optimization (TO) method for a motor that leads to an optimized solution in less time under multiple current conditions while considering the torque ripple-order component. To consider the different characteristics, a multitask CNN is newly employed. The multitask CNN predicts multiple torque performances based on the current conditions and cross-sectional magnetic flux density distribution and applies it to the TO process. As a result, the average torque and harmonic components of the target motor improved by 8.9% and 48.2%, respectively, under multiple current conditions. Furthermore, the computational cost for TO was reduced by 95.1% using the proposed method, compared with that of conventional methods. Therefore, the proposed method enables fast optimization of torque ripple-order components under a wide range of current conditions. [ABSTRACT FROM AUTHOR]

Although the output power of an induction motor (IM) is generally lower than that of the interior permanent magnet synchronous motor, IMs are widely used in industrial world due to their solidity, low cost, self-starting, etc. For designing high-performance IMs, a computer-aided engineering approach using the finite-element method is essential. However, because a finite-element analysis of IMs takes a long time due to the long transient state in the time domain, it is difficult to perform the optimization using evolutionary algorithms. To overcome this difficulty, a sensitivity-based topology optimization (TO) method for IMs in the frequency domain was proposed. However, this method has a drawback: because the magnetic nonlinearity in the rotor and stator cores cannot be considered, the physical quantities also cannot be accurately estimated. To consider the magnetic nonlinearity in the TO of the IM, the sensitivity analysis with the rotor rotation of the IM using the time-domain adjoint variable method is proposed here. The proposed sensitivity analysis method for the steady state of the IM was evaluated, and it was applied to the TO of a squirrel-cage IM. The effective rotor structure, which is composed of the iron core and secondary conductor, was derived from the multi-material level-set method. [ABSTRACT FROM AUTHOR]

The induction welding of a stringer on an aircraft fuselage skin with a lightning strike protection (LSP) on the underside of the skin is considered. The presence of LSP makes electromagnetic welding very difficult because it concentrates most of the power induced and is therefore more strongly heated than the stringer–skin interface. A new technic coupled with numerical approach that consists in inserting an electrically conductive material into the tooling in order to modify the magnetic field distribution and limit the power induced in the LSP is presented. A strongly coupled electrothermal 2-D finite element analysis (FEA) is developed to optimize the temperature profile. The 3-D FEA is then carried out in order to validate the optimum design. [ABSTRACT FROM AUTHOR]

We propose an innovative technique for dealing with optimal shape design problems that exploits the flexibility of the virtual element method (VEM) in handling meshes with general polygonal and polyhedral elements. The shape synthesis of a magnetic pole is considered as the case study. [ABSTRACT FROM AUTHOR]

This article proposes a novel topology optimization approach for permanent magnet synchronous motors (PMSMs) driven by a pulsewidth modulation (PWM) current control system operating in different modes such as maximum torque per ampere (MTPA) and weakening flux controls. The cost function consits of iron and copper losses, which are computed by considering the spatial and time harmonics in the current generated under these conditions. It is demonstrated that the total loss of the optimized model is reduced compared with that of the conventional model in the operating region. [ABSTRACT FROM AUTHOR]

In the framework of induction hardening, the coil design task is particularly suitable to be formulated as a multi-objective optimization problem. In fact, the Pareto front estimation raises the issue of guaranteeing a satisfactory diversity and number of non-dominated solutions to be provided to the decision maker (DM). In this article, a generative adversarial network (GAN) and a forward neural network (FNN), which is cascade connected to the GAN generator, produce additional Pareto optimal solutions starting from the results of a genetic algorithm [non-dominated sorting genetic algorithm (NSGA II)] used as a training set. The FNN ensures an accurate prediction of the objectives of the added solutions, removing the need for further field analyses. This method is first tested against two analytical problems and subsequently validated on a three-objective coil design task to illustrate its utility for a real-world case. [ABSTRACT FROM AUTHOR]

Since the torque characteristic of a surface permanent magnet synchronous motor (SPMSM) is strongly dependent on its magnetization distribution (MD), the prior estimation of MD is essential for its practical design. An effective method for estimating MD involves minimizing the squared function of the difference between the measured magnetic flux density and the flux density derived from the numerical analysis of the region surrounding the permanent magnet. However, as there are countless MDs for reconstructing the specified magnetic flux density, it is important to estimate the realistic MD. In this article, we proposed an estimation method to derive the realistic MD based on the Biot–Savart law incorporated with mathematical programming (steepest descent method and quasi-Newton method). In this method, MD was formulated in the spherical coordinate system. Since the constraints regarding magnetization intensity and angle were defined by the sigmoid function, both quantities were definitely distributed within the constraints. Consequently, the generation of nonphysical MD was efficiently suppressed. Furthermore, the validity of the proposed method was illustrated in comparison with other estimation methods, such as the least-square method, Tikhonov regularization, and the truncated singular value decomposition (TSVD) method. [ABSTRACT FROM AUTHOR]

A global sensitivity analysis (SA) method enhanced with a kriging surrogate model is considered in this work. To decide on the importance of many model parameters in the case of a functional output, the Sobol’ sensitivity indices—being functions of an independent variable—are to be calculated over a domain of interest. Since for expensive-to-evaluate models obtaining such a sensitivity map is highly computationally demanding, the number of required samples is reduced by a kriging approximation combined with an adaptive sampling strategy that exploits the probability information provided by the kriging model. The advantages of the approach are demonstrated through the example of an electromagnetic interference (EMI) filter. [ABSTRACT FROM AUTHOR]

This article presents the optimal design of a permanent magnet synchronous generator (PMSG). A finite element (FE) model is used to construct a metamodel, which afterward is utilized to define the objective function that models the PMSG. Kriging modeling is employed along with the design of experiments based on Latin hypercube sampling. The utilization of a surrogate model allows to speed up the optimization process while keeping the accuracy since they are developed from the FE analysis. On the other hand, it has been reported that the non-sorting genetic algorithm (NSGA) III is better than NSGA-II because it can solve multi- and many-objective optimization problems. This article demonstrates by numerical experiments that NSGA-III can be successfully used in the optimal design of PMSG with three objectives. [ABSTRACT FROM AUTHOR]

In this study, we propose a fast topology optimization (TO) method based on a deep neural network (DNN) that predicts the current-dependent motor torque characteristics using its cross-sectional image. The trained DNN is shown to provide the current condition that provides the maximum torque under the assumed motor control method. The proposed method helps perform TO with a reduced number of field computations while maintaining a high search capability. [ABSTRACT FROM AUTHOR]

This article presents an investigation of the optimal positioning of tank-wall magnetic shunts in power transformers. Magnetic shunts are made of grain-oriented magnetic silicon steel laminations and are used to avoid hot spots in metallic regions inside the power transformer. The magnetodynamic equations are solved in terms of the electric vector potential and the magnetic scalar potential. Thermal effects are calculated using heat transfer coefficients. This article proposes and solves an optimization problem where the objective is to position several shunts near the tank wall. Several design variables, temperature, and geometrical constraints are considered. As the finite-element (FE) model to be solved is in 3-D, the computing resources must be efficiently used. A multi-threading method was developed and numerically tested where the 3-D FE model, temperature calculation, and the particle swarm optimizer (PSO) are executed in a multicore computer in a parallel scheme. [ABSTRACT FROM AUTHOR]

In this article, an optimization of an ironless permanent magnet tubular linear synchronous motor (PMTLSM) for applications in human support systems is presented. For this application, a bendable and light-weighted motor design is desired. For the computation of the motor force, an analytical method is established. The method is validated by the finite element (FE) analysis and utilized for the optimization of the motor geometry regarding the force-to-weight ratio. [ABSTRACT FROM AUTHOR]

Soft magnetic composites (SMCs) demonstrate excellent potentials for electromagnetic applications. Isotropy is one critical feature in designing the composite. This work presents a shape optimization algorithm for SMCs subject to isotropy and volume constraints. The maximal isotropic effective permeability is the design target for a fixed volume fraction of the SMCs’ inclusion. Augmented Lagrange is applied to satisfy the volume requirement. In addition, an adaptive weighted sum approach is used to deal with the isotropy constraint. A level-set function is defined on the whole design domain and propagates the shape of the inclusion. Shape derivative is deduced to determine the descent direction. A numerical application is implemented for SMCs in magnetostatics in 2-D for various initial shapes. The local minimum is located at a rapid convergence rate. [ABSTRACT FROM AUTHOR]

A novel automatic design method for permanent magnet (PM) motors using a Monte Carlo tree search is presented. The optimal motor structures are determined through a tree search, in which the motors with different numbers of poles, current phase angles, PM configurations, and numbers of PMs are simultaneously considered. At the leaf nodes, parameter and topology optimizations are performed to obtain the optimal material shape and distribution. The proposed method was applied to the optimization of a 24-slot motor. It was shown to be effective in finding the optimal motor structure and geometry to maximize the average torque while considering iron loss. The proposed method can be applied not only to the design of PM motors but also to many types of electric apparatus and other systems. [ABSTRACT FROM AUTHOR]

In computational electromagnetics, there are manyfold advantages when using machine learning methods because no mathematical formulation is required to solve the direct problem for given input geometry. Moreover, due to the inherent bidirectionality of a convolutional neural network, it can be trained to identify the geometry giving rise to the prescribed output field. All this puts the ground for neural meta-modeling of fields, despite different levels of cost and accuracy. For the sake of an example, a surrogate model of the field in a small device is shown. In particular, a concept of multi-fidelity model makes it possible to control both prediction accuracy and computational cost. Moreover, TEAM Problem 35 is solved and it is shown how a generative adversarial network can help multiobjective optimal design. [ABSTRACT FROM AUTHOR]

Conventional magneto-static finite element (FE) analysis of electrical machine design is time-consuming and computationally expensive. Since each machine topology has a distinct set of parameters, design optimization is commonly performed independently. This article presents a novel method for predicting key performance indicators (KPIs) of differently parameterized electrical machine topologies at the same time by mapping a high-dimensional integrated design parameters in a lower-dimensional latent space using a variational autoencoder (VAE). After training, via a latent space, the decoder and multi-layer neural network will function as meta-models for sampling new designs and predicting associated KPIs, respectively. This enables parameter-based concurrent multi-topology optimization. [ABSTRACT FROM AUTHOR]

In this work, a novel general pattern of assisted flux barriers in an asymmetric V-shape interior permanent magnet (AVIPM) machine is presented. The AVIPM machine has a symmetric permanent magnet (PM) structure and an asymmetric rotor core structure to realize the magnetic-field-shifting (MFS) effect. The general pattern can represent four possible types of assisted flux barriers at different positions on the rotor core and the final structure can be automatically determined by using optimization method. The advantage of the proposed optimization pattern is that the optimal design of assisted flux barriers in V-shape interior permanent magnet (VIPM) machines with high torque and low torque ripple can be generated within a short computing time. The proposed optimization method is applied to improve the structure of a conventional 8-pole 48-slot VIPM machine, which is commonly used for driving electric vehicles (EVs). A non-dominated sorting genetic algorithm II (NSGA-II) is used for the global optimization of both a VIPM machine and an AVIPM machine. The electromagnetic performance is computed using a finite element analysis (FEA). Moreover, a frozen permeability (FP) method is applied to perform an accurate separation of PM torque and reluctance torque. The results exhibit that the peak output torque of the AVIPM machine is increased by 8.7% compared to a conventional VIPM machine with the same PM volume due to the notable MFS effect. In the meantime, the AVIPM machine has a better overload capability due to the larger contribution of reluctance torque. Moreover, the proposed machine has high efficiency of over 97%. [ABSTRACT FROM AUTHOR]

Zeroth-mode vibration is the dominant noise source in permanent magnet synchronous machines (PMSMs). In conventional approaches, the zeroth-mode vibration is reduced by lowering the amplitudes of concentrated forces. Different from conventional approaches, this article proposes a zeroth-mode vibration suppression method through adjustment on phase difference of concentrated forces. First, the characteristics of concentrated forces are analyzed considering the effect of slotted structure on air-gap electromagnetic force wave (EMFW), and the relationship between phase difference of concentrated force harmonics (CFHs) and the mode of vibration is obtained. Subsequently, the effects of both permanent magnet (PM) and harmonic currents on air-gap EMFW are analyzed. It is found that the phase differences of CFHs can be adjusted by combining PM shape optimization and harmonic current injection. Finally, a 12-pole 36-slot PMSM and a 16-pole 48-slot PMSM are carried out to calculate the concentrated forces and vibrations by finite element method (FEM). It is proven that the proposed method can adjust the phase difference from zero to two tooth pitches, and the zeroth-mode vibration is significantly suppressed. [ABSTRACT FROM AUTHOR]

In this article, a dual-mechanical-port flux-switching permanent magnet (DMP-FSPM) motor is studied for achieving high-efficiency design and optimization. The key of this study is to propose an optimization design approach for broadening high-efficiency region, where the motor efficiency fitting curve and the speed–torque envelope curve are combined. First, the efficiency function is established according to the motor power and loss equation. Then, high-efficiency region boundary points are obtained by combination of the fitted efficiency curve and speed–torque envelope curve. Next, the efficiency of representative operating points is determined and optimized to realize the broadening of motor high-efficiency region. Finally, the effectiveness of the proposed method and the studied DMP-FSPM motor is verified by the study results and experiment test. [ABSTRACT FROM AUTHOR]

The circumferential and axial widths of permanent magnet (PM) pole and E-shape stator pole of linear rotary motor with interlaced PM poles are very influential to the back electromotive force (EMF) and the flux leakage analysis is complex, which causes difficulty in optimization of the motor structure. One of the key issues in optimization design is to calculate the flux leakage accurately. In this article, the main flux and side flux variation laws of stator pole are derived by analytical method. The flux leakage can be divided into the flux leakage between the PM and the mover, flux leakage on the stator tooth tip, and the flux leakage between the adjacent PM poles and stator pole. The 3-D flux leakage calculation model is built, which is suitable for the calculation of magnetic flux based on different motor operating principles. Then the rotational and rectilinear back EMFs are calculated by analytical method, which are consistent with the results analyzed by 3-D finite element method. [ABSTRACT FROM AUTHOR]

The reluctance coaxial magnetic gear (RMG) has been recently considered for high-speed applications. Here, topology optimization (TO) is applied to the variable reluctance component of the RMG, with the objective of maximizing the RMG’s volumetric torque density (VTD) and gravimetric torque density (GTD). The optimization algorithm achieves a novel RMG topology with non-ferromagnetic material for the variable reluctance rotor core, outperforming the conventional topology. The novel RMG topology is optimized using parametric 2-D and 3-D finite-element analysis (FEA). The results reveal that VTD, GTD, and permanent magnet (PM)-GTD of the novel RMG are, respectively, ~5%, ~40%, and ~5% higher than those of the conventional RMG. Moreover, the novel topology exhibits ~20% less iron and PM losses. In addition, compared to the conventional RMG, the torque ripples of the high- and low-speed rotors of the novel topology, respectively, decrease from 10.3% to 7.6% and increase from 3.5% to 6.3%. [ABSTRACT FROM AUTHOR]

The large-scale high-intensity homogeneous magnetic field is an important resource to perform magnetic immunity tests for large scientific facilities. Generally, a multi-coil system with large cross-sectional area is used to generate the required test field, which has high power loss and conductor mass. In this article, we propose a novel method that introduces magnetically shielded room (MSR) to enhance the coil magnetic field and reduce power loss. The analytical model of the coil system based on MSR is established through the multi-reflection image method. In addition, the multi-objective optimization model is brought forward based on the non-dominated sorting genetic algorithm (NSGA-II). Theoretical results indicate that the magnetic field can be significantly enhanced under the presence of MSR. Consequently, prominent reductions in total current by about 61.4% and in total power loss by 75.5%–81.7% are achieved. Finally, the effectiveness of the theoretical result is numerically verified by the finite-element analysis (FEA). The proposed method provides a novel and promising way to reduce the power loss and conductor mass of such high-intensity magnet systems and other similar applications. [ABSTRACT FROM AUTHOR]

At high gear ratios, surface permanent magnet (SPM) cycloidal magnetic gears (CyMGs) can achieve higher torque densities than SPM coaxial magnetic gears (CoMGs). This article introduces the radial flux reluctance CyMG (Rel CyMG) topology and its operating principles. The Rel CyMG replaces the permanent magnets (PMs) on an SPM CyMG’s inner rotor with salient teeth. Additionally, the Rel CyMG only requires half of the SPM CyMG’s outer rotor PM count to achieve the same gear ratio, which simplifies assembly and allows the use of wider magnets. This may improve high gear ratio designs’ manufacturability. A genetic algorithm (GA) was used with 2-D finite element analysis (FEA) to optimize Rel CyMGs, SPM CyMGs, and SPM CoMGs and demonstrate that SPM CyMGs significantly outperform the other topologies at higher gear ratios in terms of specific torque (ST) and PM ST. However, Rel CyMGs outperform SPM CoMGs at higher gear ratios, with respect to ST and PM ST. Rel CyMGs also eliminate the need for a PM retention sleeve and potentially enable the use of smaller air gaps, which can allow them to achieve slightly higher PM STs than SPM CyMGs at ultrahigh gear ratios. Employing Halbach arrays significantly improves both CyMG topologies’ ST. [ABSTRACT FROM AUTHOR]

Fractional-slot concentrated-windings (FSCWs) interior permanent magnet (IPM) synchronous machines (IPMSMs) provide several advantages compared with other types of electric machines and have promising applications in electric vehicles (EVs). Minimizing the cogging torque and torque ripple of this type of motors is essential for improving output torque and driving stability. This article proposes a method for optimizing the torque performance of IPMSM with single-layer FSCW. At first, the theoretical analysis of cogging torque and torque ripple is conducted as the research basis for the optimization. The key point of this method is to describe the structure of the rotor notch as determined by seven design parameters. Then, the response surface methodology (RSM) is adopted to analyze the relationships between torque performance and design parameters. For the multiobjective optimization process, the seagull optimization algorithm (SOA) is introduced to obtain the optimal solution. After the optimization, the torque ripple of the motor achieves a significant reduction and the output torque is also improved. The advantages and reliability are compared and analyzed. Finally, a prototype is manufactured to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article presents a fast population-based multi-objective optimization of electromagnetic devices using an adaptive neural network (NN) surrogate model. The proposed method does not require any training data or construction of a surrogate model before the optimization phase. Instead, the NN surrogate model is built from the initial population in the optimization process, and then it is sequentially updated with high-ranking individuals. All individuals were evaluated using the surrogate model. Based on this evaluation, high-ranking individuals are reevaluated using high-fidelity electromagnetic field computation. The suppression of the execution of expensive field computations effectively reduces the computing costs. It is shown that the proposed method works two to four times faster, maintaining optimization performance than the original method that does not use surrogate models. [ABSTRACT FROM AUTHOR]

This article proposes a novel direct inverse modeling (DIM) of electromagnetic (EM) devices using Gaussian kernel regression. In this method, the nonlinear multivariate relationship between design and electrical properties is represented by the Gaussian kernels. Once the regression function is built, the device parameters that lead to the required electrical properties can be directly computed using the Newton method. It is shown that the proposed DIM can find the multiple solutions in case of both 2-D and 3-D inductor design with less computing cost compared with the conventional modeling method. [ABSTRACT FROM AUTHOR]

The development of an efficient microwave absorbing structure (MAS) for the concealment of electromagnetic (EM) waves is a consistent puzzling errand. In this regard, a new kind of lightweight broadband multilayer honeycomb (HC)-MAS is developed, by coating aramid HC with Fe/SiC polymeric paint optimized using effective medium approximations. An environment-friendly, cost-effective, and easily accessible procedure is embraced for the preparation of microwave absorbing paint and diverse coatings at X-band. The multi-objective Jaya’s algorithm is envisioned for the optimum design of multilayered and hybrid HC-MASs. A multi-objective fitness function is integrated into the optimization algorithm to resolve the issue of the thickness-bandwidth (BW) tradeoff. As a result, optimized single layer HC-MAS with thickness 2.1 mm attains a reflection coefficient (RC) of −12.0 dB at 10.3 GHz with a BW of 3.2 GHz below the −10 dB threshold. The intriguing results are obtained by doing a dual-mode analysis, i.e., multilayer HC-MAS and multilayer hybrid HC-MAS. Hybrid HC-MAS achieves an RC of −31 dB at 9.8 GHz with a thickness of 2.8 mm exhibiting 100% BW. Besides, the response of the proposed structure is evaluated for distinct oblique angles of incidence. The findings offer a significant opportunity for the improvement of lightweight and broadband HC-MAS for stealth applications. [ABSTRACT FROM AUTHOR]

The demand for low-speed (LS) and high-torque electrical machines is increasing in different applications. Previous research showed that dual-stator magnetically geared machines, such as Vernier pseudo-direct-drive machines (VPDDs), can have a high torque density and a high power factor. However, the size of their end-winding can affect heavily their torque density. Based on a distributed winding VPDD, we propose a concentrated-winding split-teeth VPDD (ST-VPDD), which can reduce the size of end-winding and output higher torque density. In this research, a model of the end-winding volume is given. Through preliminary design and finite element model (FEM) analysis, the torque density optimization has been performed to maximize the torque density. Taking the end-winding into account, there is a 93.8% improvement for the torque density, compared to the VPDD, to reach 78.1 Nm/L. After adding a sleeve for mechanical stability, the ST-VPDD still gives a 68.7% improvement compared to the VPDD. [ABSTRACT FROM AUTHOR]

In the exploration of broadband radar absorbing structure (RAS), a new kind of multilayer gradient perforated RAS constituted of magnetic iron metal (Fe) and silicon carbide (SiC) is examined utilizing a top-down fabrication technique and metaheuristic grey wolf optimization (GWO) algorithm. The investigation of proposed structures and magneto-dielectric study of samples is carried out in 8.2–12.4 GHz (i.e., ${X}$ -band). Distinct single and multilayered RASs are optimized using the GWO algorithm under certain restrictive conditions. In addition, the GWO algorithm is reinforced with a fitness function (FF) to acquire maximum bandwidth below −10 dB threshold (RC $\le -10$ dB) in various oblique angles of incidence with a minimum coating thickness (${t} \le2.0$ mm). Among all investigated RASs, triple-layer gradient perforated structure (TLGPS) is found to be effective which fulfills the lightweight, broadband, and angular stable behavior of an efficient absorber. TLGPS has considerably exhibited the concept of impedance gradient which assists in triumph broadband radar absorbing materials (RAMs) for low observable applications. [ABSTRACT FROM AUTHOR]

In this article, a dual-loop active shielding coil directly connected in series with the primary coil is proposed to reduce the leakage magnetic field while eliminating the additional coupling between the shielding coil and the receiving coil of a wireless electric vehicle (EV) charging system. Parameter design strategy featuring system efficiency improvement and shielding coil cost reduction using multi-objective optimization algorithm is proposed. Finally, a 1.5 kW with 19 cm air gap prototype is fabricated to verify the analytical results and investigate the influence of the shielding coil over the system parameters. The results prove that the proposed active shielding coil shows its advantage on the system transmission performance and the shielding effectiveness on the target surface comparing to conventional aluminum shielding. [ABSTRACT FROM AUTHOR]

In this article, a novel spoke-type permanent magnet (PM) machine with two types of PM materials is proposed to reduce the cost. In order to improve torque density and reduce torque ripple, the multi-objective optimization combined finite element method (FEM) and genetic algorithm (GA) are used. In addition, the electromagnetic performances of the novel spoke-type PM machine, including no-load air-gap flux density, electromagnetic torque, torque ripple, efficiency, and cost are compared with the conventional spoke-type PM machine. It is demonstrated that the novel spoke-type PM machine obtains a 1.5% lower torque ripple and 36% lower cost, and the torque density is 4.3% lower than the conventional spoke-type PM machine. Meanwhile, the novel spoke-type PM machine has lower core loss and higher efficiency. Finally, a 40 kW spoke-type PM machine is manufactured to verify the analyzing method. Experimental results demonstrate that the proposed machine exhibits high torque density and low cost in the application of electric vehicles (EVs). [ABSTRACT FROM AUTHOR]

In this article, a novel global search algorithm tornado optimization (TO) and its hybridization with pattern search method (PSM) are proposed to solve the multimodal optimization problem. The TO enhances the capability of global searching with two strategies: intensification strategy and diversification strategy. The hybridization with PSM supports the exact and fast convergence on adjacent optima from the points that are discovered in global search using TO. The outstanding performance of the proposed algorithm is verified by comparison with niching genetic algorithm at the two test functions, and the algorithm is applied to the optimal design of the interior permanent magnet synchronous motor for electric vehicles. [ABSTRACT FROM AUTHOR]

In this article, a new asymmetric PM-placed rotor topology is applied to the conventional less-rare-Earth hybrid magnet machine, where such new placement of hybrid magnets can be designed to improve the torque performance effectively. Considering the difference between the two magnetic materials, the optimal placement scheme of NdFeB-PMs and ferrite-PMs in the rotor are determined by theoretical derivation of equivalent magnetic circuit and finite element analysis. Based on the torque component separation analysis using frozen permeability method, the sizes of the two magnets are preliminarily designed to improve the utilization of reluctance torque and to reduce the superposition coupling loss of PM torque. Then, based on the parameter sensitivity analysis, the proposed asymmetric-hybrid-PM machine (AHPM) is further optimized aiming at the multi-objective of torque performance and anti-demagnetization performance. Finally, a prototype motor is fabricated and tested. Both the theoretical analysis and experimental results verify the effectiveness and reasonability of the AHPM and the proposed design method. [ABSTRACT FROM AUTHOR]

Advances in additive manufacturing have been paving the way for unconventional topologies in electromechanical systems. As such, topology optimization can be employed to uncover topologies that were once considered impractical. This article investigates enhancing the shear stress of the reluctance trans-rotary magnetic gear (TROMAG) by optimizing the topology of the gear’s variable reluctance component. Reluctance TROMAG is a strong contender for force-intensive linear motion applications where a long stroke is required. Topology optimization is performed on the reluctance TROMAG with radially magnetized permanent magnet (PM) configuration as well as with quasi-Halbach magnetized configuration. Results show that the use of elliptical iron teeth backed by non-ferromagnetic core can make a 40% increase compared to the shear stress of the conventional (rectangular teeth with iron core) reluctance TROMAG with radial magnetization. The Halbach-magnetized TROMAG with elliptical iron teeth backed by non-ferromagnetic core achieves the same shear stress as the conventional reluctance TROMAG with Halbach magnetization, and the use of lightweight non-ferromagnetic material for the variable reluctance component can make significant reductions in the total gear weight. [ABSTRACT FROM AUTHOR]

In this article, the split-tooth stator-slot permanent-magnet (ST-SSPM) machines with different permanent magnet (PM) arrangements are comprehensively analyzed. Four types of ST-SSPM machines are first introduced and classified by the positions and polarities of PMs. Then, the operation principles are elaborated and the slot–pole combinations are derived from the view of the field modulation theory. Besides, four types of ST-SSPM machines with different slots and rotor poles are globally optimized to achieve maximum torque and minimum torque ripple. Furthermore, the electromagnetic performances of the optimal designs are thoroughly analyzed by using the finite-element analysis (FEA), including the cogging torque, back electromotive force (EMF), steady torque, PM utilization ratio, loss, efficiency, and PM demagnetization. Finally, some conclusions are drawn based on the optimal designs and analyzed results. [ABSTRACT FROM AUTHOR]

In this article, a novel metamaterial absorber is designed within the visible light regime. The structure comprises nickel (Ni) resonator disks placed over a silicon dioxide substrate in a uniform pattern, with the operating frequency between 350 and 800 THz. Additionally, the infrared (IR) and ultra-violet regions are also investigated. Numerical studies are carried out using a computational electromagnetic solver. The obtained results show nearly perfect absorption, with an average absorption of 99% between 100 and 1000 THz. The physical dimensions are optimized allowing for maximum absorption results. Due to the ultra-wideband characteristic in the visible regime, the proposed design can be used as a highly efficient absorber in several applications, including solar energy harvesting, IR detector, cloaking, sensing, and EMC projects. [ABSTRACT FROM AUTHOR]

Flux switching motors (FSMs) with conventional stator slots have a relatively low copper slot filling factor. In this article, a new octane modular stator with pentagon-shaped slot electrically excited FSM (EEFSM) that has a comparatively higher copper slot filling factor is analyzed analytically to reduce computational complexity, high computational time, and high drive storage. The analytical results of the proposed EEFSM are validated via the finite element method. Furthermore, the initial design is optimized to improve the electromagnetic performance, and finally the prototype is fabricated and tested experimentally. There is good agreement among the analytical, finite element analysis (FEA), and experimental results with variations of 3%–5%. The average torque of the optimized design has improved by 30%, cogging torque is suppressed by 36%, and torque ripples have reduced by 38%, which have improved the performance of EEFSM in terms of low acoustic noise, low vibration, and longer motor lifetime. The proposed octane modular stator can be easily unfolded for windings maintenance, coils placement, and motor transportation in case of large-size applications. [ABSTRACT FROM AUTHOR]

Ultra-high-speed (UHS) surface-mounted permanent-magnet synchronous motors (SPMSMs) are widely used for driving air compressors. UHS SPMSMs can suffer from high stator iron loss and rotor eddy current loss due to their high rotational speed and changes in the magnetic flux density when loading. Since these losses do not have a linear trend but change according to the motor design parameters, mathematical models cannot always predict them. This study aims to design UHS SPMSMs based on a kriging surrogate model that takes into account the stator iron loss and rotor eddy current loss. Since the kriging surrogate model is highly predictive for nonlinear inputs, it is the perfect candidate to take into account the stator iron loss and rotor eddy current loss. The design proposed in this study allowed to minimize the size and losses of a motor that satisfied the power specification. [ABSTRACT FROM AUTHOR]

This article proposes an efficient surrogate-assisted design optimization method based on a multicriteria optimal Latin hypercube design (LHD) for multi-objective optimization of a surface-mounted permanent-magnet vernier machine (SPMVM). An improved iterated local search (ILS) method is proposed to optimize the spatial distribution uniformity and orthogonality of LHDs so that the data feature over the wide ranges of optimization variables can be captured more efficiently. Using the optimal LHD, a highly generalizable surrogate model can be trained with fewer samples, thus greatly reducing the required number of finite element analysis (FEA) cases and improving the optimization efficiency. A prototype corresponding to the optimal design is built and measured to validate the proposed method. [ABSTRACT FROM AUTHOR]

In this article, a high-speed permanent magnet synchronous machine (PMSM), which is rated at 15 kW and 120 krpm for a gas compressor, is designed and optimized. According to the design specifications, two design schemes of different rotor topologies are presented and compared by the finite element method (FEM) to determine the preliminary scheme of the original machine. The performance of the original machine is obtained by FEM. Meanwhile, the losses of the original machine are analyzed and calculated for optimization. Especially, the conductor diameter of the winding is selected to reduce the copper loss of the machine. Finally, in order to reduce wind friction loss and increase power density, the dimensions of the stator core and the thickness of the permanent magnet (PM) are optimized. Compared with the original model, the wind friction loss is reduced by 17.2%, and the power density is increased by 6.64% in the optimized machine. [ABSTRACT FROM AUTHOR]

This article presents an efficient multi-objective optimization model for linear induction motors (LIMs) with a novel approximation and prediction strategy. The proposed method can realize the optimization design with speediness and accuracy, while reducing the calculation complexity of eddy current analysis. The sample data in this model are obtained by 3-D finite-element method (FEM) considering the skin effect and edge effect. Then, the Latin hypercube sampling (LHS) method is adopted to extract dimensional data including every structural parameter of the secondary. Based on these samples, a multiple analysis model is established using eXtreme Gradient Boosting (XGBoost) method. By employing this analysis model, several optimization structures are provided satisfying the limitation and rationality of designing structure and performance requirements. The precisions of the thrust force, vertical force, eddy current, and copper loss of optimal structures are validated by FEM. [ABSTRACT FROM AUTHOR]

Bayesian optimization (BO) is a well-developed machine learning (ML) field for black-box function optimization. In BO, a surrogate predictive model, here a Gaussian process, is used to approximate the black-box function. The estimated mean and uncertainty of the surrogate model are paired with an acquisition function to decide where to sample next. In this study, we applied this technique to known ferromagnetic thin-film materials such as ferromagnetic (Fe100−yGay)1−xBx (x = 0−21 and y = 9−17) to demonstrate optimization of structure–property relationships, specifically the dopant concentration or stoichiometry effect on magnetostriction and ferromagnetic resonance linewidth. Our results demonstrated that BO can be deployed to optimize structure–property relationships in FeGaB and FeGaC thin films. We have shown through simulation that using BO methods to guide experiments reduced the number of samples required to statistically determine the maximum or minimum by 50% compared to traditional methods. Our results suggest that BO can be used to save time and resources to optimize ferromagnetic films. This method is transferrable to other ferromagnetic material structure–property relationships, providing an accessible implementation of ML to magnetic materials development. [ABSTRACT FROM AUTHOR]

An equivalence-principle-based method (EPM) for calculating the magnetic field around the coil assemblies applied in electric vehicle inductive chargers is proposed. The ferrite core and the aluminum shielding plate are substituted with surface equivalent sources (ESs), the distribution of which is solved from surface boundary conditions. The magnetic field is the sum of the incident field (induced by the coil current) and the scattered field (induced by ES). The impacts of ferrite core and aluminum plate are quantitatively characterized using their respective source–field relations. The optimization of coil assemblies to improve the efficiency is selected as an application of EPM. The key factors that affect efficiency, e.g., mutual inductance and stray losses, are approximated using simple functions of ES. An exemplary circular coil assembly pair is manually optimized via EPM. Comparisons with finite element analysis (FEA) prove the accuracy and speediness of EPM. The other benefits of EPM are also discussed. Due to the immeasurability of ES, experimental test results of two coils are compared with FEA results to indirectly validate the accuracy of EPM. [ABSTRACT FROM AUTHOR]