Design Of Electromagnetic Devices Research Articles

Dynamic characteristics testing and modeling in electromagnetic-thermal-force coupling of electromagnetic devices using parallel finite element simulation

Electromagnetic systems of Electromagnetic devices involve multiphysics coupling of electromagnetic fields, electrical circuits, mechanical motion, and temperature fields. When the digital prototype model has a large number of meshes, the commercial finite element software also has the disadvantage of long computation time in the simulation of dynamic characteristics of electrical apparatus. This paper proposes a transmission line method (TLM) based digital modeling method for the electromagnetic-thermal-force coupling to test and evaluate the static and dynamic characteristics of electromagnetic devices more accurately and efficiently. The case study is developed on a direct current (DC) contactor, and the dynamic characteristics testing and modeling in the electromagnetic-thermal-force coupling of the contactor are investigated. The numerical results of the finite element method (FEM) model for the electromagnetic system of the contactor are compared with the commercial FEM software Altair Flux and the experimental results, which demonstrate the exceptional accuracy of the proposed digital modeling method for the multiphysics coupling of the DC contactor. Meanwhile, the dynamic characteristics of the DC contactor at different ambient temperatures are measured and evaluated, and the comparison with the experimental measurements shows the applicability of the proposed digital prototype model at different ambient temperatures. Compared with the conventional Newton-Raphson (NR) iteration, when dividing fine mesh and extremely fine mesh, the TLM parallel computing significantly improves the efficiency of solving the dynamic characteristics of multiphysics coupling by about 35 % and 44 % with the same computational accuracy, respectively. The dynamic characteristics evaluation method combined with digital modeling technology and experimental measurements proposed in this paper demonstrates high accuracy and solving efficiency, making a notable contribution to the field of design and optimization of electromagnetic devices.

Ultraweakly low-dispersion epsilon-negative response of GR-CNT/PVDF ternary metacomposites

The epsilon-negative (ε’ < 0) response displayed by metacomposites offers notable potential for diverse applications in the design of dielectric and electromagnetic (EM) devices, underpinned by novel principles. However, achieving a weakly low-dispersion ε′-negative performance within the radio frequency (RF) band remains challenging. In this study, we bypassed this issue by constructing three-dimensional (3D) conductive networks through the integration of graphene (GR) and carbon nanotubes (CNT) with polyvinylidene fluoride (PVDF) serving as the matrix material. Leveraging the modifiable GR-CNT network, a ε′-negative response at approximately −20 was attained spanning the 100 MHz-1 GHz range. This ultraweak ε′-negative response was attributed to the formation of a moderate plasmonic state of free carriers within the metacomposites. Further, simulations were conducted, assessing the electric field vector distribution of the ε′-negative materials, revealing promising EM shielding performance. Concurrently, this investigation elucidates the RF ε′-negative response mechanism, paving the way for the practical application of metacomposites.

Design and testing of a hybrid electromagnetic damping device for automotive applications

This study proposes a new design concept for combining a viscous fluid damper (VFD) with an eddy current damper (ECD). Three main concepts were thoroughly discussed: the magnetic system, the conductive material, and the housing. In particular, the excitation circuit and magnetic field were applied from the exterior of the main chamber of the VFD to minimize the intrusive risks introduced into the damper when trying to connect an internal magnetic system to an external power source.Experimental investigations on a test rig developed for this purpose were carried out in this work. The design aimed to generate eddy currents in an oscillating cylinder made of copper placed around an existing real-world car damper and secured to its moving rod. When exposed to an electromagnetic field, the newly designed system rapidly generated an extra level of damping in addition to the initial damping effect provided by the viscous mechanism. The obtained results showed that applying an electric current of only 1A increased the amplitude of the drag force by 12 % for an electrode with a thickness of 1.5 mm. A magnetic saturation phenomenon resisted any extra increase in the current. Making the metal conductor thicker is expected to increase the volume of the conductor in which the currents are flowing and, consequently, increase the damping effect.

The application of artificial intelligence and machine learning in the design process for electromagnetic devices

Designing an electromagnetic device, as with many other devices, is an inverse problem. The issue is that the performance and some constraints on the inputs are provided but the solution to the design problem is non-unique. Additionally, conventionally, at the start of the design process, the information on potential solutions needs to be generated quickly so that a designer can make effective decisions before moving on to detailed performance analysis, but the amount of information that can be obtained from simple analysis tools is limited. Machine learning may be able to assist by increasing the amount of information available at the early stages of the design process. This is not a new concept, in fact it has been considered for several decades but has always been limited by the computational power available. Recent advances in machine learning might allow for the creation of a more effective “sizing” stage of the design process, thus reducing the cost of generating a final design. The goal of this paper is to review some of the work in applying artificial intelligence to the design and analysis of electromagnetic devices and to discuss what might be possible by considering some examples of the use of machine learning in several tools used in conventional design, which have been considered over the past three decades.

An electroplastic constitutive model of fcc metals and their alloys under high current density

The mechanical behavior of fcc (face center cubic) metals and their alloys such as copper-based and aluminum-based materials changes obviously under high current density, but there is a lack of suitable constitutive model to describe the electroplastic behavior of these materials, which limits the accuracy of the mechanical response prediction in applications, such as electromagnetic launch. In this paper, a novel nonlinear constitutive model for electroplastic behavior under the high current density of fcc metals and their alloys is proposed. The model proposed is based on the theory of dislocation evolution, energy transfer theory and electron transport theory and takes into account the effects of high current density on dislocation density, strain rate and resistivity. Considering the evolution of forward and reverse dislocations caused by pulsed current, the relationship between the evolution rate of reverse dislocation and strain is established. Due to the sensitivity of the electroplastic effect to strain rate, the strain rate under electric current is obtained by combining the theory of dislocation thermal activation and energy transfer. According to the concept of thermal resistance and dislocation resistance, the relationship between resistance and current density is derived. The quantitative results demonstrate that this model can well capture the flow stress softening under high current density (>3000 A/mm2) and the transient hardening after current removal because of the consideration of a more suitable strain rate and dislocation evolution. The critical current density that has a significant effect on the strain rate and the current density corresponding to the subsection point of the resistivity are quantitatively obtained. Especially, it is shown that the evolution rate of the reverse dislocation decreases nonlinearly with the increase of strain after the current is removed. These results will be helpful to study the mechanical behavior of the rail during electromagnetic launch and further optimize the design of electromagnetic launch device.

Genetic sampling for surrogate-assisted topology optimization in electromagnetic riveting device design

Electromagnetic riveting (EMR) has been widely used in aerospace field, but the riveting gun is heavy and its cost is high. In this paper, the reduction of cost and weight for rivet gun were investigated from the optimization of concentric-ring slave coil. For the lack of accuracy of the traditional genetic algorithm coupled with artificial neuron network (GA-ANN), an improved computational framework was proposed. In the framework, a genetic-drop sampling strategy enhanced the accuracy of GA-ANN and helped ANN to generalize to the optimal. The prototype was manufactured in a case study to verify the riveting quality of the optimized EMR gun. Besides, coils of different sizes proved the generalization ability. The proposed method has the potential to significantly reduce the prediction error by 62% and effectively decrease the number of numerical model calculations by 73%. Optimization for EMR gun led to a 51% reduction in weight, a 73% increase in material utilization and little effect on riveting quality. Meanwhile, the algorithm was still accurate on coils of different sizes. The proposed computational framework could improve efficiency in the design of EMR gun in industry.

Physics-Informed Neural Networks for Solving 2-D Magnetostatic Fields

Physics-informed neural network (PINN) has shown great potential in the inverse and parametric designing problems in electrical engineering. Moreover, most existing works on PINN are dedicated to computational fluids, and very few attentions have been paid to static and low-frequency electromagnetic near fields with multiple media in electrical engineering applications. In this work, a PINN for solving two-dimensional (2D) magnetostatic fields in electromagnetic devices and systems is proposed. The magnetic field intensity and the magnetic vector potential are solved by training a neural network which encodes partial differential equations and boundary conditions as residuals. The computation of the spatial derivatives of media constitutive parameters, which negatively impacts the training of PINN, is eliminated. A mesh-assisted non-uniform sampling method for the selection of collocation points is proposed to further improve the performance of PINN. The proposed PINN is verified by comparing its results with those of the finite element method in two 2D magnetostatic case studies. It is expected that this work will promote further applications of PINN in the modelling, numerical analysis, and parametric design of electromagnetic devices and systems.

Data-Driven Methods for Design and Analysis of Electromagnetic Devices

Electromagnetic devices, such as electrical drive systems for electric vehicles, have been widely employed in domestic and industrial equipment [...]

Research on a demand design method of a cross polarization converter metasurface based on a depth generation model

As a two-dimensional electromagnetic metamaterial, the cross-polarization conversion (CPC) metasurface is thin, easy to develop, and has attracted wide attention. However, existing CPC cell surface designs still rely on inefficient full-wave numerical simulation. Although some researchers have explored deep learning CPC metasurface structure design methods, the generated metasurface patterns are of poor quality. In this paper, an on-demand design method for cross-polarization conversion metasurface based on depth-generation model is proposed. Firstly, Wasserstein generative adversarial network (WGAN) is used to reverse design CPC metasurface, and Wasserstein distance is introduced to replace JS divergence and KL divergence to optimize the target. The problem of training difficulty caused by gradient elimination of original generative adversarial network (GAN) is fundamentally solved. Secondly, in the WGAN model, U-Net architecture generator is used to generate images, which greatly improves the surface image quality of CPC. In addition, a simulator composed of convolutional neural network (CNN) is also added in this paper to carry out forward prediction of S-parameter spectrum diagram. By inputting the patterns generated by WGAN into the simulator, the corresponding S-parameter spectrum diagram is generated and compared with the real S-parameter spectrum diagram, so as to verify whether the surface patterns of generated elements meet the requirements. The depth generation model proposed in this paper organically combines the forward spectrum prediction model and the reverse CPC metasurface structure design model, so that the CPC metasurface structure satisfying the expected electromagnetic response can be designed quickly on demand. This on-demand design method is expected to promote the rapid design, fabrication and application of electromagnetic devices.

Using Time-Varying Systems to Challenge Fundamental Limitations in Electromagnetics: Overview and summary of applications

Time-varying systems open intriguing opportunities to explore novel approaches in the design of efficient electromagnetic devices. While such explorations date back to more than half a century ago, recent years have experienced a renewed and increased interest into the design of dynamic electromagnetic systems. This resurgence has been partly fueled by the desire to surpass the performance of conventional devices, and to enable systems that can challenge various well-established performance bounds, such as the Bode–Fano limit, the Chu limit, and others. Here, we give an overview of this emerging research area and provide a concise and systematic summary of the most relevant applications for which the relevant performance bounds can be overcome through time-varying elements. In addition to enabling devices with superior performance metrics, such research endeavors may open entirely new opportunities and offer insight towards future electromagnetic and optical technologies.

Analytical Approach for Sharp Corner Reconstruction in the Kernel Free Boundary Integral Method during Magnetostatic Analysis for Inductor Design

It is very important to perform magnetostatic analysis accurately and efficiently when it comes to multi-objective optimization of designs of electromagnetic devices, particularly for inductors, transformers, and electric motors. A kernel free boundary integral method (KFBIM) was studied for analyzing 2D magnetostatic problems. Although KFBIM is accurate and computationally efficient, sharp corners can be a major problem for KFBIM. In this paper, an inverse discrete Fourier transform (DFT) based geometry reconstruction is explored to overcome this challenge for smoothening sharp corners. A toroidal inductor core with an airgap (C-core) is used to show the effectiveness of the proposed approach for addressing the sharp corner problem. A numerical example demonstrates that the method works for the variable coefficient PDE. In addition, magnetostatic analysis for homogeneous and nonhomogeneous material is presented for the reconstructed geometry, and results carried out using KFBIM are compared with the results of FEM analysis for the original geometry to show the differences and the potential of the proposed method.

Design and Optimization of Electromagnetic Induction Device based on Finite Element Analysis

Electromagnetic induction is the basis of most electrical and power equipment. In order to improve the overall performance of electromagnetic induction devices, it is necessary to analyze the distribution of electric and magnetic fields around and inside the devices, and guide the structural adjustment and material selection of devices. However, due to the integral and differential operations involved, complex electrical and magnetic field problems are often unable to be analyzed by analytical methods. With the improvement of numerical analysis theory and computer performance, the use of finite element analysis is expected to solve the complex electromagnetic field problems in practical applications. Using finite element analysis, the electromagnetic field distribution characteristics and influencing factors of electromagnetic induction devices of household induction cooker are studied, the corresponding mathematical model is established, the influence of electromagnetic parameters of each part of the induction heating system on heating energy efficiency is studied, and the optimization design is carried out according to different application requirements, so as to provide a basis for further improving the performance of devices.

A Hybrid Algorithm Based on NSGA-II and MOPSO for Multi-Objective Designs of Electromagnetic Devices

In this paper, a hybrid algorithm is proposed by combining the NSGA-II with MOPSO algorithm. The original NSGA-II is improved by using Logistic mapping initialization and a dynamic selection mechanism of crossover and mutation operators is proposed. The performance of the proposed hybrid algorithm is verified using standard test functions and it is applied to the multi-objective optimization benchmark problem TEAM 22. Numerical results demonstrate the effectiveness and superiority of the proposed hybrid algorithm.

Research on load circuit of medium frequency electromagnetic heat storage device

In order to improve the working efficiency of the electromagnetic heat storage device under high current and high frequency, the electromagnetic field finite element method is used to analyze and calculate the load circuit to make it in a suitable working state. Firstly, the circuit model of the energy storage device is built by using the field-circuit coupling method and the coil inductance is calculated. Secondly, the harmonic capacitor is analyzed and calculated, so that the load circuit works in a weak inductive state. On this basis, the variation law of loss on the inductor core and the distribution of water temperature rise flowing through the core at a certain water velocity are analyzed. A 50 kW prototype was built using the method proposed in this paper, and the experimental results show that the electro-thermal conversion efficiency of the electromagnetic thermal energy storage device reaches 93.39 %. The feasibility of the design method is proved by experiment and simulation, which has important guiding significance for the research and design of electromagnetic thermal energy storage device.

Incorporating aggregation trees in two-archive algorithm 2 for solving high dimensional multi-objective electromagnetic optimization

The optimization designs of modern electromagnetic devices are often modeled as high dimensional multi-objective problems due to their high-quality demands in multiple functions and systemization. To identify and remove the redundant objectives, an interactive aggregation trees method embedded into the two-archive algorithm 2 is proposed to solve the high dimensional multi-objective optimizations. To balance the speed and the accuracy in finding the redundant objectives, conflict degree, conflict list and conflict flag are developed in an interactive aggregation trees frame. The two-archive algorithm 2 is modified to adjust the updated model obtained from the objective-reduction frame. Mathematical test functions and TEAM Workshop Problem 22 are used to test the performance of the proposed method. Numerical results demonstrate that the proposed method can remove the redundant objectives effectively and provide qualified solutions for the electromagnetic optimization designs.

Multiobjective System-by-Design for mm-Wave Automotive Radar Antennas

The computationally-efficient solution of multi-objective optimization problems (MOPs) arising in the design of modern electromagnetic (EM) microwave devices is addressed. Towards this end, a novel System-by-Design (SbD) method is developed to effectively explore the solution space and to provide the decision maker with a set of optimal trade-off solutions minimizing multiple and (generally) contrasting objectives. The proposed MO-SbD method proves a high computational efficiency, with a remarkable time saving with respect to a competitive state-of-the-art MOP solution strategy, thanks to the "smart" integration of evolutionary-inspired concepts and operators with artificial intelligence (AI) and machine learning (ML) techniques. Representative numerical results are reported to provide the interested users with useful insights and guidelines on the proposed optimization method as well as to assess its effectiveness in designing mm-wave automotive radar antennas.

Modeling of a Rotary Actuator Using Elliptical Coordinates and Differential Flux Tubes

Analytical models are useful in the design of electromagnetic devices. In this article, a model is developed for a rotary actuator whose stator curvature is elliptically shaped to have a reluctance torque that restores the rotor to the maximum torque per ampere position. The total torque is decomposed into the coil torque and a reluctance torque. The rotor is a permanent magnet represented by equivalent Amperian currents. The stator geometry is simplified to an ellipse having surface current densities at the interpolar regions that are equivalent to the stator currents. Then, the field solution within the ellipse is obtained using Laplace’s equation in elliptical coordinates so that the coil torque can be obtained by the Lorentz force. The reluctance torque is derived by the energy method and an approach referred to here as differential flux tubes, which is similar to the use of conventional flux tubes in magnetic equivalent circuits. A rotating reference frame on the rotor is also adopted to simplify the mathematics. The finite element method (FEM) is also used in the field analysis and development of the proposed model. Finally, the actuator is prototyped, and experimental results are employed to evaluate the results obtained from the analytical model and FEM.

Inverse design of microwave waveguide devices based on deep physics-informed neural networks

Using physics-informed neural networks to solve physical inverse problems is becoming a trend. However, it is difficult to solve the scheme that only introduces physical knowledge through the loss function. Constructing a reasonable loss function to make the results converge becomes a challenge. To address the challenge of physics-informed neural network models for inverse design of electromagnetic devices, a deep physics-informed neural network is introduced by using the mode matching method. The physical equations have been integrated into the network structure when the network is constructed. This feature makes the deep physics-informed neural network have a more concise loss function and higher computational efficiency when solving physical inverse problems. In addition, the training parameters of deep physics-informed neural networks are physically meaningful compared with those of traditional physics-informed neural networks. Users can control the network by parameters more easily. Taking the scattering parameter design of a two-port waveguide for example, we present a new metal topology inverse design scheme and give a detailed explanation. In numerical experiments, we target a set of physically realizable scattering parameters and inversely design the metallic septum by using a deep physics-informed neural network. The results show that the method can not only achieve the design target but also obtain solutions with different topologies. The establishment of multiple solutions is extremely valuable in implementing the inverse design. It can allow the designer to determine the size and location of the design area more freely while achieving the performance requirements. This scheme is expected to promote the application and development of the inverse design of electromagnetic devices.

Directionality between driven-dissipative resonators

The notion of nonreciprocity, in essence when going forwards is different from going backwards, emerges in all branches of physics from cosmology to electromagnetism. Intriguingly, the breakdown of reciprocity is typically associated with extraordinary phenomena, which may be readily capitalized on in the design of (for example) nontrivial electromagnetic devices when Lorentz reciprocity is broken. However, in order to enable the exploitation of nonreciprocal-like effects in the next generation of quantum technologies, basic quantum optical theories are required. Here we present a versatile model describing a pair of driven-dissipative quantum resonators, where the relative phase difference between the coherent and incoherent couplings induces an asymmetry. The interplay between the diverse dissipative landscape —which encompasses both intrinsic losses and dissipative couplings— and the coherent interactions leads to some remarkable consequences including highly directional (or even one-way) energy transport. Our work proffers the tantalizing prospect of observing dissipation-induced quantum directionality in areas like photonics or cavity magnonics (spin waves), which may aid the design of unconventional nanoscopic devices.

Physics-Informed Generative Adversarial Network-Based Modeling and Simulation of Linear Electric Machines

The demand for fast magnetic field approximation for the optimal design of electromagnetic devices is urgent nowadays. However, due to the lack of a publicly available dataset and the unclear definition of each parameter in the magnetic field dataset, the expansion of data-driven magnetic field approximation is severely limited. This study presents a physics-informed generative adversarial network (PIGAN), as well as a permanent magnet linear synchronous motor (PMLSM)-based magnetic field dataset, for fast magnetic field approximation. It includes the current density, material distribution, electromagnetic material properties, and other parameters of the electric machine. Physics-informed loss functions are utilized in the training process, making the output governed by Maxwell’s equation. Different slot-pole combinations of the PMLSM are involved in the dataset to extend the generalization of PIGAN. Some indicators for the further evaluation of magnetic approximation performance, including image-based metrics and calculation methods for the performance of electric motors, are presented in this study. Some challenges of magnetic field approximation using PIGAN are also discussed. The effectiveness of the physics-informed method is verified by comparing the magnetic field approximation results and the performance analysis results of the PMLSM with FEM, and the speed of PIGAN is approximately 40 times faster than that of FEM, while the accuracy is similar.