Electromagnetic Design Optimization Research Articles

Electromagnetic Design Optimization Integrated with Mechanical Stress Analysis of PM-Assisted Synchronous Reluctance Machine Topologies Enabled with a Blend of Magnets

Permanent Magnet-Assisted Synchronous Reluctance Machines (PMASynRM) provide a low-cost alternative to Surface PM Machines due to the use of relatively lower grades of rare-earth (RE) or RE-free magnets, as the performance degradation due to weaker magnets is compensated by the presence of reluctance torque. However, the weaker magnets suffer from a high risk of demagnetization, leading to unreliable motor operation. Using a blend of RE and RE-free magnets has the potential to overcome this issue. This paper proposes to blend different grades of various rare-earth (RE) and rare-earth-free (RE-free) magnets in six different combinations and utilizes them in two-layer and three-layer U-shaped PMASynRM topologies with both eight-pole and six-pole variations. The rotor of the various designs is then optimized using a differential evolution (DE) based optimization algorithm to obtain low-cost designs with reduced RE magnet volume and minimum demagnetization risk. The optimization of each design is also integrated with the evaluation of mechanical stresses in the rotor laminations so as to maintain the stresses below the material yield strength. Furthermore, the various performance metrics, such as toque–speed/power–speed characteristics, demagnetization, and efficiency maps, are evaluated for each of the optimized and mechanically feasible designs. A quantitative comparison of the various optimized designs is also obtained to highlight the various trade-offs. The results indicate the feasibility of meeting the baseline torque requirement across the entire speed range, even with a 100% reduction in RE magnet volume and less than 5% demagnetization risk, while achieving a cost reduction exceeding 50%. Moreover, the two-layer, eight-pole designs exhibit relatively higher performance, whereas the three-layer, eight-pole designs are found to be the most economical option.

Combined Electromagnetic and Mechanical Design Optimization of Interior Permanent Magnet Rotors for Electric Vehicle Drivetrains

In many high-speed electrical machines, centrifugal forces within the rotor can be first-order constraints on electromagnetic optimization. This can be particularly acute in interior permanent magnet (IPM) machines in which magnets are usually retained entirely by the rotor core with no additional mechanical containment. This study investigates the nature of the trade-off between mechanical and electromagnetic requirements within the context of an eight-pole, 100 kW IPM machine with a base speed of 4000 rpm and an extended speed range up to 12,000 rpm. A series of mechanical and electromagnetic models are used to estimate the level of shaft interference, mechanical stress in critical regions of the rotor and the impact of various features and dimensions within the machine on electromagnetic torque. A systematic exploration of the design space is undertaken for rotor diameters from 120 mm to 180 mm, with optimal designs in terms of torque per unit length established at each diameter while meeting the constraints imposed on mechanical stress. The final preferred design has a rotor of 165 mm and an axial length of 103 mm long with a fractional slot winding in a 30-slot stator. The overall machine has an active mass of 42.3 kg, which corresponds to ~2.4 kW/kg. This paper describes the optimization study in detail and draws on the results to explore the nature of the design trade-offs in such rotors and the impact of core properties.

1-D multi-channel CNN with transfer functions for inverse electromagnetic behaviors modeling and design optimization of high-dimensional filters

1-D multi-channel CNN with transfer functions for inverse electromagnetic behaviors modeling and design optimization of high-dimensional filters

Electromagnetic-thermal co-optimization to minimize case-to-winding temperature rise in permanent magnet machines

Electrification of aerospace platforms requires efficient heat dissipation from electric machines to meet their temperature requirements while minimizing the weight and size of the onboard thermal management system. It is critical to couple available electromagnetic design optimization approaches with complementary thermal models, so that these thermal management system objectives can be considered, namely to reduce the temperature rise between the sources of heat generation and coolant. In this work, a thermal equivalent circuit model for the stator of a permanent magnet synchronous machine is developed and validated. The thermal network includes a discretization of individual conductors and considers their placement within a slot, compared to previous approaches that assumed a lumped/homogenized representation that is not versatile for robust optimization. The temperature predictions reveal that the choice of conductor sizing and packing within the stator slot significantly affects their peak temperatures. After validation, the thermal model is coupled to an electromagnetic model to perform multi-physics multi-objective design that minimizes machine mass and the temperature rise between the conductors and coolant. This approach contrasts a traditional electromagnetic design trade off between loss and mass. Design studies were performed to generate Pareto-optimal designs for comparison between these objectives. By using the co-optimization design framework with an integrated thermal objective function, the machine operating temperature can be significantly reduced while delivering the same power density at the cost of machine efficiency. The detailed machine designs are analyzed to explain the thermal advantages of the co-optimization design.

Multi-Objective Electromagnetic Design Optimization of a Power Transformer Using 3D Finite Element Analysis, Response Surface Methodology, and the Third Generation Non-Sorting Genetic Algorithm

This paper presents a multi-objective design optimization of a power transformer to find the optimal geometry of its core and the low- and high-voltage windings, representing the minimum power losses and the minimum core and copper weights. The optimal design is important because it allows manufacturers to build more efficient and economical transformers. The approach employs a manufacturer’s design methodology, which is based on the usage of the laws of physics and leads to an analytical transformer model with the advantage of requiring a low amount of computing time. Afterward, the multi-objective design optimization is defined along with its constraints, and they are solved using the Non-Sorting Genetic Algorithm III (NSGA-III), which finds a set of optimal solutions. Once an optimal solution is selected from the Pareto front, it is necessary to fine-tune it with the 3D Finite Element Analysis (FEA). To avoid the large computing times needed to carry out the 3D Finite Element (FE) model simulations used in multi-objective design optimization, Response Surface Methodology (RSM) polynomial models are developed using 3D FE model transformer simulations. Finally, a second multi-objective design optimization is carried out using the developed RSM empirical models that represent the cost functions and is solved using the NSGA-III. The numerical results of the optimal core and windings geometries demonstrate the validity of the proposed design methodology based on the NSGA-III. The used global optimizer has the feature of solving optimization problems with many cost functions, but it has not been applied to the design of transformers. The results obtained in this paper demonstrate better performance and accuracy with respect to the commonly used NSGA-II.

Fully Automated Design Method Based on Reinforcement Learning and Surrogate Modeling for Antenna Array Decoupling

Modern electromagnetic (EM) device design generally relies on extensive iterative optimizations by designers using simulation software (e.g., CST), which is a very time-consuming and tedious process. To relieve human engineers and boost productivity, we proposed a machine learning (ML) framework to solve the problem of automated design for EM tasks. The proposed approach combines advanced reinforcement learning (RL) algorithms and deep neural networks (DNNs) in an attempt to simulate the decision-making process of human designers to realize automation learning. Specifically, the RL-based agent can interact with the EM design software without engaging human designers, allowing for automated design. Besides, the data accumulated during EM software simulation in the early design stage are reused as training data to build a DNN surrogate model to replace the time-consuming EM simulation and further accelerate the training of RL to achieve better optimization of EM design. Two types of antenna array decoupling including <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times $ </tex-math></inline-formula> 2 and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times $ </tex-math></inline-formula> 4 arrays working at 3.5 GHz are used as test vehicles to validate the proposed method. The decoupling metasurfaces designed by the proposed fully automated method based on RL showed satisfactory results comparable to the results achievable by human designers. This indicates that the proposed method can be used to build powerful tools to boost the design efficiency of EM devices.

Optimization Design of High-speed Interior Permanent Magnet Motor with High Torque Performance Based on Multiple Surrogate Models

In order to obtain better torque performance of high-speed interior permanent magnet motor(HSIPMM) and solve the problem that electromagnetic optimization design is seriously limited by its mechanical strength, a complete optimization design method is proposed in this paper. The object of optimization design is a 15kW, 20000r/min HSIPMM whose permanent magnets in rotor is segmented. Eight structural dimensions are selected as its optimization variables. After design of experiment(DOE), multiple surrogate models are fitted, a set of surrogate models with minimum error is selected by using error evaluation indexes to optimize, the NSGA-II algorithm is used to get the optimal solution. The optimal solution is verified by load test on a 15kW, 20000 r/min HSIPMM prototype. This paper can be used as a reference for the optimization design of HSIPMM.

Electromagnetic Optimal Design of a PMSG Considering Three Objectives and Using NSGA-III

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.

An Analytical Method for Calculating the Cogging Torque of a Consequent Pole Hybrid Excitation Synchronous Machine Based on Spatial 3D Field Simplification

Consequent pole hybrid excitation synchronous (CPHES) machines have the advantage of symmetrical bidirectional magnetomotive force increments. Compared with a traditional hybrid excitation motor (HEM), a CPHES machine improves the disadvantage of asymmetry in the adjustment range when magnetization and demagnetization occur. The calculation and analysis of the cogging torque of the CPHES machine are complex due to the complicated structure. This paper proposes an analytical method for calculating the cogging torque of a CPHES machine. This analytical method converts the complex three-dimensional magnetic field problem into a two-dimensional magnetic circuit problem and, through the accumulation method, can quickly and accurately calculate the cogging torque of the CPHES machine. In contrast with the finite element method, the calculation results basically follow each other, but the analytical method is more efficient and omits complicated meshing. This is of great significance to the preliminary electromagnetic design and performance optimization of a CPHES machine.

Applications of Machine Learning in Antenna Array Processing [Book/Software Reviews

This book is intended to be a practical guide for students, engineers, and researchers in electromagnetics with minimal background in ML. It can also serve as a basic reference book for ML algorithms, advanced topics of electromagnetics, and applications of ML in antenna array processing. The book is divided into two parts. The first part contains five chapters, which cover the theoretical principles of the most common ML architectures and algorithms used today in machines, kernel methods for antenna array, neural networks, and deep learning for computational electromagnetics. The second part consists of four chapters that provide detailed applications of the algorithms of electromagnetic problems, such as in antenna array beamforming, angle of arrival detection, computational electromagnetics, antenna optimization, reconfigurable antennas, cognitive radio, and other aspects of electromagnetic design. This book is the front runner of ML as it applies to electromagnetics and antenna array processing. It requires extensive mathematics and statistics skills and knowledge by the reader to read, understand, and use this book. The original graphics and charts were in color, with explanations of the charts based on the color in the charts, but the color has been removed in the published copy, which may cause the reader some confusion.

Fully-staggered-array bulk Re-Ba-Cu-O short-period undulator: large-scale 3D electromagnetic modelling and design optimization using A-V and H-formulation methods

The development of a new hard x-ray beamline I-TOMCAT equipped with a 1 m long short-period bulk high-temperature superconductor undulator (BHTSU) has been scheduled for the upgrade of the Swiss Light Source at the Paul Scherrer Institute. The very hard x-ray source generated by the BHTSU will increase the brilliance at the beamline by over one order of magnitude in comparison to other state-of-the-art undulator technologies and allow experiments to be carried out with photon energies in excess of 60 keV. One of the key challenges for designing a 1 m long (100 periods) BHTSU is the large-scale simulation of the magnetization currents inside 200 staggered-array bulk superconductors. A feasible approach to simplify the electromagnetic model is to retain five periods from both ends of the 1 m long BHTSU, reducing the number of degrees of freedom to the scale of millions. In this paper, the theory of the recently-proposed 2D A -V formulation-based backward computation method is extended to calculate the critical state magnetization currents in the ten-period staggered-array BHTSU in 3D. The simulation results of the magnetization currents and the associated undulator field along the electron beam axis are compared with the well-known 3D H -formulation and the highly efficient 3D H -ϕ formulation method, all methods showing excellent agreement with each other as well as with experimental results. The mixed H -ϕ formulation avoids computing the eddy currents in the air subdomain and is significantly faster than the full H -formulation method, but is slower in comparison to the A -V formulation-based backward computation. Finally, the fastest and the most efficient A -V formulation, implemented in ANSYS 2020R1 Academic, is adopted to optimize the integrals of the undulator field along the electron beam axis by optimizing the sizes of the end bulks.

Modeling of antenna resonant frequency based on co‐training of semi‐supervised Gaussian process with different kernel functions

Usually, traditional machine learning (ML) methods use only labeled samples for learning. However, in practical problems including electromagnetic optimization design, the acquisition cost of labeled samples is relatively high. Obtaining label training samples is the most time-consuming part, so how to use relatively few label samples for training to obtain a high-precision surrogate model is a hot topic. This study proposes a co-training algorithm of semi-supervised Gaussian Process (GP) with different kernel functions, based on the differences between these two different GP models. The algorithm is conducted by a small number of labeled samples in combination with unlabeled samples, so as to continuously improve the accuracy of the models. Stop criteria is set in advance to control the number of unlabeled samples introduced, preventing the accuracy of the model reduced by introducing too much unlabeled samples. Furthermore, the proposed algorithm is evaluated by benchmark functions and resonant frequency modeling problems of two different antennas. Results show that the proposed GP model has good fitting effects on the benchmark functions. For the problems of resonant frequency modeling, in the case of the same labeled samples, its predictive ability is better than that of the traditional supervised learning (SL) method.

Selective Protection Miniature Circuit Breaker Electromagnetic Actuator Design and Dynamic Optimization

This paper first proposes an electromagnetic actuator design and dynamic optimization method for selective miniature circuit breaker (SMCB), and realizes good coordination of electromagnetic actuator force and energy characteristics. Based on short delay tripping mechanism force and magnetic field model, it builds Matlab and Adams co-simulation platform, simulates and analyzes the magnetic field distribution and force energy property under different electromagnetic topological structure, and establishes the permanent magnet electromagnetic structure model. Then, it builds multi-field coupled mathematical model, and studies multi-objective dynamic optimization design for electromagnetic actuator based on quantum particle swarm algorithm. Finally, it develops the 100A miniature circuit breaker. The experimental results show that the optimized electromagnetic actuator design can effectively control the miniature circuit breaker tripping action threshold and mechanical characteristics.

Electromagnetic Design and Characterization of an S-band 3-Cell RF Accelerating Cavity

An S-Band (2998 MHz) Radio Frequency (RF) cavity to accelerate electrons was developed taking into account the beam space charge, the relativistic change in velocity of the low energy beam particle distribution through the cavity, and the emittance growth. The electromagnetic design and geometry optimization were done using the codes Poisson Superfish (PSF) and CST Studio (CST). In addition, beam dynamics simulations were done using the program Travel to optimize the emittance and take into account the space charge effect.

Electromagnetic design and characterization of an S-band 3-cell rf acceleration cavity

An S-Band (2998 MHz) Radio Frequency (RF) cavity to accelerate electrons was developed taking into account the beam space charge, the relativistic change in velocity of the low energy beam particle distribution through the cavity, and the emittance growth. The electromagnetic design and geometry optimization were done using the codes Poisson Superfish (PSF) and CST Studio (CST). In addition, beam dynamics simulations were done using the program Travel to optimize the emittance and take into account the space charge effect.

Proposal of a Bi-Objective Kriging Adapted Output Space Mapping Technique for Electromagnetic Design Optimization

Multi-objective optimization with finite-element model (FEM) for the optimal design of electromagnetic devices is a complex task and also time costly. Output space mapping (OSM) techniques allow having an affordable computation cost with a minimum number of computationally expensive FEM evaluations. In this paper, a bi-objective Kriging adapted OSM, an original space-mapping technique based on adaptive nonlinear corrective projection, is proposed. First, the proposed technique is validated using a mathematical test function; Second, it is applied on a three-phase brushless dc motor, which is represented through two modeling levels: 1) coarse (analytical model) and 2) fine (2-D FEM). An advantage of this new technique is its ability to make the coarse model converge effectively toward the fine model.

Research on Extraction and Analysis of Characteristic Conditions of Hub Motor for Electric Vehicles

The traditional electric machine optimization design technology is generally based on continuous power or overload conditions. It is difficult to fully consider the entire operating domain of the machine, and it is difficult to effectively improve the overall efficiency of the motor. In order to achieve an effective improvement of the overall efficiency of the electric motor under the operating conditions of the vehicle, the motor drive system is well matched with the vehicle design. This paper proposes an electromagnetic optimization design technology for multiple working conditions, uses NEDC working conditions to simulate the actual running conditions of the vehicle to the motor, and optimizes the design of the motor with the highest comprehensive efficiency (lowest comprehensive energy consumption) under the working conditions. By using statistical method, more than 1000 working points of the motor in the whole operating range are simplified into 11 characteristic working conditions. By calculating and comparing the overall energy consumption of the motor under NEDC working condition and 11 characteristic points, the effectiveness of the extraction method of characteristic working conditions is verified.

A Parallel Surrogate Model Assisted Evolutionary Algorithm for Electromagnetic Design Optimization

Optimization efficiency is a major challenge for electromagnetic (EM) device, circuit, and machine design. Although both surrogate model-assisted evolutionary algorithms (SAEAs) and parallel computing are playing important roles in addressing this challenge, there is little research that investigates their integration to benefit from both techniques. In this paper, a new method, called parallel SAEA for electromagnetic design (PSAED), is proposed. A state-of-the-art SAEA framework, surrogate model-aware evolutionary search, is used as the foundation of PSAED. Considering the landscape characteristics of EM design problems, three differential evolution mutation operators are selected and organized in a particular way. A new SAEA framework is then proposed to make use of the selected mutation operators in a parallel computing environment. PSAED is tested by a micromirror and a dielectric resonator antenna as well as four mathematical benchmark problems of various complexity. Comparisons with state-of-the-art methods verify the advantages of PSAED in terms of efficiency and optimization capacity.

Optimal Design of a Superconducting Motor for Electric-drive Aeropropulsion Based on Finite-Element Analysis and Genetic Algorithm

Electric-drive Aeropropulsion is one of the key technologies in design of future aircrafts because the electric motor drive has high efficiency and the this would reduce operational cost and pollutant emission. This paper discusses the optimization of electromagnetic design of superconducting motors. Finite element analysis, multi-objective genetic algorithm and analytical equations are applied in the optimization. Power density, power loss including AC loss of superconducting materials, and torque ripple are considered in the objective function. The results of optimization suggest that the application of superconducting motor is feasible for electric-drive aeropropulsion with power density higher than 16.4 kW/kg.

Design of a Nb3Sn 400 T/m Quadrupole for the Future Circular Collider

For the Future Circular Collider (FCC), a 100-TeV post Large Hadron Collider machine, 750 main quadrupoles with a gradient of around 400 T/m are required. This paper presents an electromagnetic design optimization of a double aperture Nb <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Sn quadrupole, fulfilling the specifications and a structural design of a single aperture configuration towards a prototype.