Electromagnetic Optimization Research Articles

Surrogate-Assisted Multistate Tuning-Driven EM Optimization for Microwave Tunable Filter

This article proposes a novel surrogate-assisted multistate tuning-driven electromagnetic (EM) optimization technique to address the challenges of microwave tunable filter design with multiple tuning states. The desired multiple tuning states are satisfied simultaneously using the proposed surrogate-assisted technique. The proposed surrogate model is composed of several subsurrogate models. Each subsurrogate model is developed to perform the optimization for each tuning state. The subsurrogate models share the same values of nontunable parameters and possess different values of tunable parameters. The overall surrogate model is developed to find a single set of optimal solutions for nontunable parameters and multiple sets of optimal solutions for tuning parameters simultaneously. Parallel computation scheme is exploited to generate the training samples for establishing the proposed surrogate model. Furthermore, a new trust-region updating formulation specifically for multistate tuning is proposed to improve the convergence of the proposed optimization algorithm. Using the proposed optimization technique, different tuning states are considered together and optimized simultaneously. The values of nontunable design parameters are constrained by all tuning states and consequently there is a higher chance that more suitable solutions can be found to satisfy all the desired tuning states simultaneously. The proposed technique for the tunable filter design with multiple tuning states has a better capability of avoiding local minima and can reach the optimal solution more effectively in comparison with the existing optimization method. Two microwave examples are used to validate the proposed technique.

Design and Optimization of Metal Cavity Bandpass Filter and Fragmented Patch Antenna By Combining Simulation Software and Genetic Algorithm

An electromagnetic (EM) structure optimization method based on Genetic algorithm (GA) and EM simulation software is proposed. The aim is to accelerate optimization speed of electromagnetic structure and improve designed performance. An algorithm considered continuous and discrete problems is adopted to expand design domain of electromagnetic structures. In order to evaluate the performance of proposed method, a fourth-order cavity bandpass filter and a fragmented patch antenna are considered as two examples to verify the analysis.

Design of Silicon Nitride Edge Coupler for Monolithically Integrated Laser on Silicon Photonic Circuits With Relaxed Alignment Tolerance and High Efficiency

We propose a bi-layer 5-tip edge coupler in a multilayer silicon nitride-on-silicon (SiN-on-Si) waveguide platform. The coupler is used for the integration between a monolithic 1550 nm laser and a single-mode SiN waveguide. The simulated coupling efficiency is 92.8%. The vertical 1-dB-loss misalignment tolerance is as large as 0.5 μm. Broad 1-dB-drop bandwidth (1338 nm to 1700 nm) and small footprint (total length: 38.2 μm) are achieved simultaneously. A broadband bi-layer SiN-Si adiabatic transition cascaded to the edge coupler is designed to couple the laser power into a single-mode Si waveguide at an efficiency of 90.6%. Low-computation-cost electromagnetic numerical simulation and optimization strategies are applied to improve the reverse design of the complex couplers.

Metaheuristic-based Near-Optimal Fractional Order PI Controller for On-grid Fuel Cell Dynamic Performance Enhancement

Fractional-order PI (FOPI) is considered a possible efficient alternative controller to classical PI controller for fuel cell (FC) on-grid systems applications. This paper aims to enhance the dynamic performance of grid-connected FC systems while employing different metaheuristic optimization techniques to FOPI controllers and consequently improve the power quality and stability of the system. The proposed metaheuristic techniques are modified flower pollination algorithm (MFPA), harmony search (HS), and electromagnetic field optimization (EFO). In the study, two FOPI-based regulators are suggested for controlling the current and the power flow between the FC and the network. For this purpose, the inverter's pulse width modulation (PWM) regulation is performed while integrating FC systems into the network. The three metaheuristic optimizers are used for the optimal gain scheduling of the FOPI controller parameters while minimizing the absolute value of the error as the cost function. To demonstrate how efficient the FOPI controllers are, fair comparisons for the on-grid FC-based system performance while proposing the different optimized advanced FOPI and conventional PI controllers are analyzed. According to the simulation results, the most efficient metaheuristic technique is identified based on an impartial defined criterion regarding the dynamic behaviors’ steady-state error, overshoot besides both setting and execution time. Through the suggested criterion, the EFO is considered the best optimization approach. Through the numerical analysis, the power quality assessment of the optimally controlled overall FC on-grid systems is illustrated through voltage sag/swell studies.

Digital image thresholding by using a lateral inhibition 2D histogram and a Mutated Electromagnetic Field Optimization

Digital image thresholding by using a lateral inhibition 2D histogram and a Mutated Electromagnetic Field Optimization

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.

Lightweight TiO2@C/Carbon Fiber Aerogels Prepared from Ti3C2Tx/Cotton for High-Efficiency Microwave Absorption.

Carbon fiber aerogel (CFA) derived from cotton wool as a potential microwave absorbing material has received intensive attention owing to the low density, high conductivity, large surface area, and low cost, but its applications are limited by the relatively high complex permittivity. To solve this problem, TiO2@C (derived from Ti3C2Tx) is introduced into CFA to prepare lightweight TiO2@C/CFA composites based on electromagnetic (EM) parameter optimization and enhanced EM wave attenuation performance. The microwave absorption capacity of TiO2@C/CFA-2 composite is obviously better than that of CFA. It is confirmed that good impedance matching derived from the combination of TiO2@C and CFA is the main factor to achieve excellent microwave absorption. Moreover, the improved microwave absorption capabilities are closely related to multiple EM wave absorbing mechanisms including multiple reflections and scattering, dipolar and interfacial polarization, and conductivity loss. TiO2@C/CFA-2 possesses a maximum reflection loss (RL) of -43.18 dB at a low response frequency of 6.0 GHz. As the matching thickness is less than 2.0 mm, the maximum RL values can still exceed -20 dB, and at the same time, the wide effective absorption bandwidth (EAB) below -10 dB achieves 4.36 GHz at only 1.9 mm thickness. Our work confirms that the lightweight and high-performance TiO2@C/CFA composites are promising choices and offer a new approach to design and construct carbon-based microwave absorbents derived from biomass.

Multi-Objective Grey Wolf Optimization Algorithm for Solving Real-World BLDC Motor Design Problem

The first step in the design phase of the Brushless Direct Current (BLDC) motor is the formulation of the mathematical framework and is often used due to its analytical structure. Therefore, the BLDC motor design problem is considered to be an optimization problem. In this paper, the analytical model of the BLDC motor is presented, and it is considered to be a basis for emphasizing the optimization methods. The analytical model used for the experimentation has 78 non-linear equations, two objective functions, five design variables, and six non-linear constraints, so the BLDC motor design problem is considered as highly non-linear in electromagnetic optimization. Multi-objective optimization becomes the forefront of the current research to obtain the global best solution using metaheuristic techniques. The bio-inspired multi-objective grey wolf optimizer (MOGWO) is presented in this paper, and it is formulated based on Pareto optimality, dominance, and archiving external. The performance of the MOGWO is verified on standard multi-objective unconstraint benchmark functions and applied to the BLDC motor design problem. The results proved that the proposed MOGWO algorithm could handle nonlinear constraints in electromagnetic optimization problems. The performance comparison in terms of Generational Distance, inversion GD, Hypervolume-matrix, scattered-matrix, and coverage metrics proves that the MOGWO algorithm can provide the best solution compared to other selected algorithms. The source code of this paper is backed up with extra online support at and .

Systematic Design of Multimode Antennas for MIMO Applications by Leveraging Symmetry

A systematic procedure for constructing orthogonal antenna ports on arbitrary symmetric multimode antennas is proposed. The procedure is based on the projection operator method from group theory. With this method, optimal orthogonal port configurations are generated analytically. Electromagnetic simulations, modal analyses, or optimization techniques are not required. The optimal port configurations are solely governed by the symmetry of an antenna and can thus be considered as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge. Accordingly, this knowledge is available right from the beginning of the design process and can be purposefully used to compare different suitable antenna geometries with respect to the practical implementation effort. The procedure is illustrated by means of an application example resulting in a complete prototype of a six-port multimode antenna.

Optimization of Inside-Out Nuclear Magnetic Resonance Sensor With Logging-While-Drilling Tool Specification and Experimental Validation

The sensor determines the basic performance of the nuclear magnetic resonance (NMR) logging-while-drilling (LWD) system. However, the electromagnetic calculation and structure optimization of the sensor are complicated and difficult. In this study, we proposed an effective design method for inside-out NMR LWD sensor from the perspective of electromagnetic filed. First, a region of interest (ROI) was set first, the static magnetic field strength range in ROI corresponding to the ideal operating frequency bandwidth was defined as sensitive region, and the magnet system was optimized efficiently by introducing uniform design method to maximize the sensitive region. Different simplified finite element models were established to calculate different electromagnetic parameters of the RF coil system. The inside-out NMR LWD sensor was processed. Static magnetic field measurement result show that the proposed static magnetic field optimization method can effectively obtain the sensitive region with ideal radial position and strength, and is superior to the traditional optimization method. The NMR signal detection ability was tested by importing the Carr–Purcell–Meiboom–Gill (CPMG) sequence and phase-alternate pulse sequence technology. In particular, a porous and radially layered measurement device was designed, the signal sensitive region was quantitatively measured based on changes in sample porosity, and the width and height of signal sensitive region are 50 mm and 90 mm, respectively. The differences between static magnetic field sensitive region and signal sensitive region were analyzed, and suggestions for improving the sensor optimization method were provided at last.

Novel Topology Optimization Method for Weight Reduction in Electrical Machines

This paper proposes a combined electromagnetic and mechanical topology optimization for weight reduction in electrical machines based on Finite Element Analysis (FEA) and Evolutionary Structural Optimization (ESO). The devised method is an on-off-type algorithm with adaptative mesh in which low flux density and low Von Mises stress cells are removed successively from a first machine design. With this approach, the weight of the machine can be considerably reduced without compromising the electromagnetic performance of the machine, with a reduced computation time compared to other topology optimization methods. A case study involving a 1.2 MW low-speed, permanent magnet motor is analyzed under different conditions (algorithm parameters, initial mesh, rotational speed) and used to compare the proposed method with two other topology optimization approaches.

Optimization of Electromagnetic Devices using a RSM Enhanced ACOR Algorithm

The present paper proposes and studies the efficiency of using a RSM enhanced ACOR algorithm for the optimization of electromagnetic devices. Different RSM methods, such as Box-Behnken, CCD and Doelhert, are applied to find most suitable parameters (optimal set) for the ACOR in order to solve the corresponding electromagnetic optimization problems. The parameters optimal set is found by building a metaheuristic function. In the same time, the optimal parameter set is searched and determined for each electromagnetic problems for different objective functions, the best and the average global best solution for a tests set. The electromagnetic devices to be optimized are the Loney’s solenoid and an energy storage device, as defined by the TEAM22 problem. Both electromagnetic problems are proposed benchmarks from electromagnetic community

Accurate microwave filter design based on particle swarm optimization and one‐dimensional convolution autoencoders

This paper proposes a one-dimensional convolutional autoencoders (1D-CAE) surrogate-based electromagnetic (EM) optimization technique exploiting particle swarm optimization (PSO) algorithm for microwave filter design. The 1D-CAE is a flexible neural network structure that can be used as surrogate model to handle forward modeling problems as well as solving inverse problems, such as extracting the coupling matrix from S-parameters. In this paper, the 1D-CAE is proposed to handle forward modeling problem. On the one hand, the 1D-CAE model is adopted to as the surrogate model to the EM simulation solver to speed up the PSO algorithm optimization process. On the other hand, the 1D-CAE model can update the data set and network parameters online to continuously improve the prediction accuracy of the surrogate model during the PSO algorithm iterations. Compared with other neural network structures, the 1D-CAE model proposed in this paper requires less data and converges faster in the same test environment. To demonstrate the validity of the proposed method, it was used to design a sixth-order and an eighth-order cross-coupled filter. The design results shown that the proposed method is valid.

Electromagnetic and bandwidth performance optimization of new waveguide polarizers with septum of a stepped-thickness type for satellite systems

Development and optimization of new waveguide polarizers with conducting stepped-thickness septum were performed in the article. Matching and polarization performance of novel microwave devices were accurately modelled using numerical finite elements method. An experimental prototype of the developed waveguide polarization converter with the suggested configuration of a septum was manufactured and measured. It was demonstrated that the application of a stepped-thickness conducting septum with 3 sections instead of a constant-thickness conducting septum improves return losses and cross-polarization isolation of the square waveguide polarization converter by 5 and 7 dB, respectively. In addition, measured cross-polarization isolation of the developed septum waveguide polarizer in an assembled antenna feed network is > 30 dB. Therefore, designed compact stepped-thickness septum polarization converter can be effectively applied in modern dual-polarization antenna systems for satellite communications and radar appliances.

A twofold infill criterion-driven heterogeneous ensemble surrogate-assisted evolutionary algorithm for computationally expensive problems

Over the past decade, surrogate-assisted evolutionary algorithms (SAEAs) have received considerable attention in solving expensive optimization problems from both academia and industry. In SAEAs, model management plays a key role in the all-encompassing use of surrogates and data. Constructing sophisticated management strategies and high-fidelity surrogates, nevertheless, remains an urgent and foundational task. In this context, we propose a twofold infill criterion-driven heterogeneous ensemble surrogate-assisted neighborhood field optimization algorithm (HESNFO). The proposed algorithm takes into account both the diversity and accuracy of surrogates to speed up the optimization process. A twofold infill criterion is presented to strike a balance between exploration and exploitation on the basis of updating the surrogates online. Meanwhile, to enhance the diversity of surrogates, a heterogeneous ensemble surrogate consisting of multiple radial basis function models with different architectures and different inputs has been built. Finally, our experimental results on several benchmark problems as well as an electromagnetic acoustic transducers optimization instance demonstrate that the proposed algorithm is superior.

Optimal Values of Unknown Parameters of Polymer Electrolyte Membrane Fuel Cells Using Improved Chaotic Electromagnetic Field Optimization

In this article, the improved chaotic electromagnetic field optimization (ICEFO) algorithm is adopted to generate the optimal values of unknown parameters of fuel cells (FCs). The mathematical model of polymer electrolyte membrane FC (PEMFC) considers nonlinear and complex optimization problems with different control variables. The sum of squared error between the measured and computed stack voltages is considered as the main objective function. The performance of the ICEFO algorithm is tested on three PEMFC stacks. Moreover, sensitivity and statistical measures are presented to confirm the reliability and accuracy of ICEFO. In addition, the effect of changing the cell temperature and reactants pressures is studied for more validation of ICEFO. Furthermore, the results obtained by ICEFO are competitive compared with other optimization methods. These results confirm the effectiveness of ICEFO in solving the optimization problem of PEMFC parameter estimation. Finally, based on the optimization results a Simulink model for PEMFC is developed to examine the dynamic performance of the PEMFCs.

Study on Electromagnetic Stress Optimization and its Resistant Structure in Toroidal HTS Magnet Applied in High-Energy Storage Density SMES

In this paper, an approach for minimizing stress concentration by optimizing the cross section of a toroidal magnet to D-type is presented. To attempt, the magnetic field distributions of both toroidal and D-shaped toroidal magnets were evaluated and compared using the finite element method (FEM), and a convergence curve of maximal magnetic field effect on the winding part was established. We also reproduced an optimal scheme of the `D-type' toroidal HTS magnet for a 400 MJ grade SMES based on this convergence curve, and evaluated the distribution of electromagnetic stress in the winding of the magnet, including von-mise stress and hoop stress. We next constructed a reinforcement structure for the `D-type' toroidal HTS magnet and investigated the effect of the reinforcement structure material on the suppression of the stress inside the magnet. According these results, an industrialized scheme on the stress inhibition for the D-type HTS magnet for 400 MJ grade SMES was summarized.

Real power loss reduction by Enhanced Electromagnetic Field and Lightning Process Optimization algorithms

In this research paper Enhanced Electromagnetic Field Optimization (EEFO) algorithm and Lightning Process Optimization (LPO) algorithm are projected to solve the problem. Real power loss reduction is the main objective of the work. In the algorithm population is alienated into three fields: positive, negative, and neutral. Based on the attraction–repulsion forces among these electromagnetic positive, negative, and neutral fields, a new-fangled electromagnetic particle will be engendered in iterations. In Electromagnetic field optimization algorithm, solution symbolized by Electromagnetic Particle (EMPE) and is made through electromagnets which equal to the total number of variables. Among the neighbor’s every electromagnet will imply the attraction and repulsion which possess’​ positive, negative polarity. Then in this paper Lightning Process Optimization (LPO) algorithm is used to solve optimal reactive power problem. Naturally happening lightning process has been imitated to formulate the algorithm. Test points situated on the cloud surface and the ground area are considered as solutions. Objective function is computed with all test points and taken as electric field. With reference to the function of head tip elevation, cloud altitude, channel charge subsequent bound of upward leader is interrelated to charge of the channel. With considering voltage stability index EEFO and LPO algorithms are verified in IEEE 30 bus system. Then EEFO and LPO algorithms evaluated in standard IEEE 14, 30, 57, 118 and 300 bus test systems without considering the voltage stability index. Minimization of real power loss with voltage stability index enhancement has been attained and voltage deviation also minimized.

Efficient EM Optimization Exploiting Parallel Local Sampling Strategy and Bayesian Optimization for Microwave Applications

In electromagnetic (EM) optimization of microwave design, a computationally bad starting point usually leads the local optimization process to be stuck into local optimum, which does not satisfy the design specifications. In this situation, global optimization methods can be an alternative to achieve the final optimal solution. However, global optimization methods always suffer from a relatively low convergence rate. To address this problem, we propose an efficient EM optimization technique with a novel parallel local sampling strategy and Bayesian optimization (BO) for microwave applications in this article. We develop a new parallel local sampling strategy to increase the exploitation ability near the potential optimal solution in each optimization iteration and improve the convergence of the entire optimization process. The local sampling range in each iteration is determined based on the derivative information of the current potential optimal solution. While conventional BO only uses the information of potential optimal solutions in each iteration during optimization, we propose to exploit both the generated local samples and the potential optimal solutions together to build a surrogate model and guide the optimization. Therefore, the exploration and exploitation during the proposed EM optimization are well balanced, and the entire EM optimization process is effectively accelerated in comparison to other existing global methods. Examples of EM optimizations of microwave components are used to demonstrate the proposed technique.

Prediction of the effects of climate change on hydroelectric generation, electricity demand, and emissions of greenhouse gases under climatic scenarios and optimized ANN model

In this study, an attempt is made to manage the gap between energy demand and energy supply by predicting hydropower production, energy demand, and greenhouse gas emissions. The interaction between climatic, hydrological, and socio-economic parameters creates a nonlinear and uncertain relationship. The complexity of this nonlinear relationship necessitate the use of ANN to estimate energy demand. To predict energy demand, ANN model is used along with improved Electromagnetic Field Optimization (IEFO) algorithms. The results show, hydroelectric generation in the near future under RCP2.6, RCP4.5, and RCP8.5 is decreased 10.981 MW, 12.933MW, and 14.765MW and in the far future decreased 21.922 MW, 23.649 MW, and 26.742 MW. The energy demand increases in the near future 513 MW and far future 1168 MW. According to forecasting hydropower generation and energy demand, the gap between the demand-supply will increase. Also, the greenhouse gases emissions is increase due to the increase in fossil fuel consumption.