High-fidelity Electromagnetic Simulations Research Articles

Fast EM-driven nature-inspired optimization of antenna input characteristics using response features and variable-resolution simulation models

Utilization of optimization technique is a must in the design of contemporary antenna systems. Often, global search methods are necessary, which are associated with high computational costs when conducted at the level of full-wave electromagnetic (EM) models. In this study, we introduce an innovative method for globally optimizing reflection responses of multi-band antennas. Our approach uses surrogates constructed based on response features, smoothing the objective function landscape processed by the algorithm. We begin with initial parameter space screening and surrogate model construction using coarse-discretization EM analysis. Subsequently, the surrogate evolves iteratively into a co-kriging model, refining itself using accumulated high-fidelity EM simulation results, with the infill criterion focusing on minimizing the predicted objective function. Employing a particle swarm optimizer (PSO) as the underlying search routine, extensive verification case studies showcase the efficiency and superiority of our procedure over benchmarks. The average optimization cost translates to just around ninety high-fidelity EM antenna analyses, showcasing excellent solution repeatability. Leveraging variable-resolution simulations achieves up to a seventy percent speedup compared to the single-fidelity algorithm.

Antenna Modeling Based on Two-Stage Gaussian Process Considering Sensitivity Information

For antenna modeling and optimization design, high-fidelity full-wave electromagnetic simulation software can generally be used to obtain training samples. However, this process takes a long time. To solve this problem, a two-stage Gaussian process (GP) considering electromagnetic sensitivity information is proposed. Based on the coarse grid and fine grid of electromagnetic simulation software, the first stage learns the mapping relationships of antenna performance and sensitivity of each parameter, simultaneously. In the second stage, the accurate surrogates are established based on the mapping relationships above obtained. Because the method in this study takes sensitivity information into consideration during the modeling process, it can better reflect the mapping relationship between antenna input and output, which can develop a more accurate surrogate model. The proposed two-stage GP surrogate model considering sensitivity information can significantly reduce the demand of high-fidelity training samples for modeling, and greatly save the simulation time of calling electromagnetic simulation software. Therefore, this method is more suitable for the problems of insufficient electromagnetic simulation samples. The proposed approach is evaluated by modeling and optimization of an inverted F antenna and an ultra-wideband planar monopole antenna. The results show that the method has high modeling accuracy with limited training samples given by high-fidelity electromagnetic simulation, which further verify its effectiveness and efficiency.

Rapid multi-objective optimization of antennas using nested kriging surrogates and single-fidelity EM simulation models

PurposeThis study aims to propose a computationally efficient framework for multi-objective optimization (MO) of antennas involving nested kriging modeling technology. The technique is demonstrated through a two-objective optimization of a planar Yagi antenna and three-objective design of a compact wideband antenna.Design/methodology/approachThe keystone of the proposed approach is the usage of recently introduced nested kriging modeling for identifying the design space region containing the Pareto front and constructing fast surrogate model for the MO algorithm. Surrogate-assisted design refinement is applied to improve the accuracy of Pareto set determination. Consequently, the Pareto set is obtained cost-efficiently, even though the optimization process uses solely high-fidelity electromagnetic (EM) analysis.FindingsThe optimization cost is dramatically reduced for the proposed framework as compared to other state-of-the-art frameworks. The initial Pareto set is identified more precisely (its span is wider and of better quality), which is a result of a considerably smaller domain of the nested kriging model and better predictive power of the surrogate.Research limitations/implicationsThe proposed technique can be generalized to accommodate low- and high-fidelity EM simulations in a straightforward manner. The future work will incorporate variable-fidelity simulations to further reduce the cost of the training data acquisition.Originality/valueThe fast MO optimization procedure with the use of the nested kriging modeling technology for approximation of the Pareto set has been proposed and its superiority over state-of-the-art surrogate-assisted procedures has been proved. To the best of the authors’ knowledge, this approach to multi-objective antenna optimization is novel and enables obtaining optimal designs cost-effectively even in relatively high-dimensional spaces (considering typical antenna design setups) within wide parameter ranges.

Accelerated multiobjective design of miniaturized microwave components by means of nested kriging surrogates

Design of microwave components is an inherently multiobjective task. Often, the objectives are at least partially conflicting and the designer has to work out a suitable compromise. In practice, generating the best possible trade-off designs requires multiobjective optimization, which is a computationally demanding task. If the structure of interest is evaluated through full-wave electromagnetic (EM) analysis, the employment of widely used population-based metaheuristics algorithms may become prohibitive in computational terms. This is a common situation for miniaturized components, where considerable cross-coupling effects make traditional representations (eg, network equivalents) grossly inaccurate. This article presents a framework for accelerated EM-driven multiobjective design of compact microwave devices. It adopts a recently reported nested kriging methodology to identify the parameter space region containing the Pareto front and to render a fast surrogate, subsequently used to find the first approximation of the Pareto set. The final trade-off designs are produced in a separate, surrogate-assisted refinement process. Our approach is demonstrated using a three-section impedance matching transformer designed for the best matching and the minimum footprint area. The Pareto set is generated at the cost of only a few hundred of high-fidelity EM simulations of the transformer circuit despite a large number of geometry parameters involved.

On deterministic procedures for low-cost multi-objective design optimization of miniaturized impedance matching transformers

PurposeThis paper aims to investigate deterministic strategies for low-cost multi-objective design optimization of compact microwave structures, specifically, impedance matching transformers. The considered methods involve surrogate modeling techniques and variable-fidelity electromagnetic (EM) simulations. In contrary to majority of conventional approaches, they do not rely on population-based metaheuristics, which permit lowering the design cost and improve reliability.Design/methodology/approachThere are two algorithmic frameworks presented, both fully deterministic. The first algorithm involves creating a path covering the Pareto front and arranged as a sequence of patches relocated in the course of optimization. Response correction techniques are used to find the Pareto front representation at the high-fidelity EM simulation level. The second algorithm exploits Pareto front exploration where subsequent Pareto-optimal designs are obtained by moving along the front by means of solving appropriately defined local constrained optimization problems. Numerical case studies are provided demonstrating feasibility of solving real-world problems involving expensive EM-simulation models of impedance transformer structures.FindingsIt is possible, by means of combining surrogate modeling techniques and constrained local optimization, to identify the set of alternative designs representing Pareto-optimal solutions, in a realistic time frame corresponding to a few dozen of high-fidelity EM simulations of the respective structures. Multi-objective optimization for the considered class of structures can be realized using deterministic approaches without defaulting to evolutionary methods.Research limitations/implicationsThe present study can be considered a step toward further studies on expedited optimization of computationally expensive simulation models for miniaturized microwave components.Originality/valueThe proposed algorithmic solutions proved useful for expedited multi-objective design optimization of miniaturized microwave structures. The problem is extremely challenging when using conventional methods, in particular evolutionary algorithms. To the authors’ knowledge, this is one of the first attempts to investigate deterministic surrogate-assisted multi-objective optimization of compact components at the EM-simulation level.

Rapid dimension scaling for notch frequency redesign of UWB band-notch antennas

One of important issues in the design of ultra-wideband (UWB) antennas is implementation of band-notch capability, critical to avoid interference with other standards such as WLAN. At the same time, appropriate dimension adjustment of antenna geometry parameters for precise frequency allocation of the notch and to ensure sufficient reflection level in the UWB band is quite challenging. For the sake of reliability it has to be based on full-wave electromagnetic (EM) simulations of the structure at hand. In this letter a simple technique for dimension scaling of UWB antennas is proposed that allows for rapid reallocation of the band-notch to a required frequency. Our methodology exploits a fast inverse model established using several reference designs obtained for coarse-discretization EM model of the antenna of interest as well as appropriate correction techniques that allows us to apply the model for antenna scaling at high-fidelity EM simulation level. The proposed procedure is demonstrated using a compact UWB monopole with the band-notch scaling range from 5 to 8 GHz. Simulation results are validated using physical measurements of the fabricated antenna prototypes.

Surrogate-assisted multi-objective optimization of compact microwave couplers

This work presents a rigorous methodology for expedited simulation-driven multi-objective design of microwave couplers with compact footprints. The proposed approach is a viable alternative for computationally expensive population-based metaheuristics and exploits a surrogate-assisted point-by-point Pareto set determination scheme that utilizes – for the sake of computational efficiency – space-mapping-corrected equivalent circuit models. The technique is showcased using a complex design example of a compact rat-race coupler, for which a set of nine alternative design solutions is efficiently identified. The latter design solutions illustrate the best possible trade-offs between conflicting design objectives for the structure at hand, that is, its operational bandwidth and the layout area. The overall design cost corresponds to approximately 20 high-fidelity electromagnetic simulations of the miniaturized coupler. Several selected trade-off designs have been manufactured and measured for the purpose of method validation.

Multi-objective optimization of expensive electromagnetic simulation models

Vast majority of practical engineering design problems require simultaneous handling of several criteria. For the sake of simplicity and through a priori preference articulation one can turn many design tasks into single-objective problems that can be handled using conventional numerical optimization routines. However, in some situations, acquiring comprehensive knowledge about the system at hand, in particular, about possible trade-offs between conflicting objectives may be necessary. This calls for multi-objective optimization that aims at identifying a set of alternative, Pareto-optimal designs. The most popular solution approaches include population-based metaheuristics. Unfortunately, such methods are not practical for problems involving expensive computational models. This is particularly the case for microwave and antenna engineering where design reliability requires utilization of CPU-intensive electromagnetic (EM) analysis. In this work, we discuss methodologies for expedited multi-objective design optimization of expensive EM simulation models. The solution approaches that we present here rely on surrogate-based optimization (SBO) paradigm, where the design speedup is obtained by shifting the optimization burden into a cheap replacement model (the surrogate). The latter is utilized for generating the initial approximation of the Pareto front representation as well as further front refinement (to elevate it to the high-fidelity EM simulation model level). We demonstrate several application case studies, including a wideband matching transformer, a dielectric resonator antenna and an ultra-wideband monopole antenna. Dimensionality of the design spaces in the considered examples vary from six to fifteen, and the design optimization cost is about one hundred of high-fidelity EM simulations of the respective structure, which is extremely low given the problem complexity.

Low-cost multi-objective optimization and experimental validation of UWB MIMO antenna

Purpose – The purpose of this paper is to validate methodologies for expedited multi-objective design optimization of complex antenna structures both numerically and experimentally. Design/methodology/approach – The task of identifying the best possible trade-offs between the antenna size and its electrical performance is formulated as multi-objective optimization problem. Algorithmic frameworks are described for finding Pareto-optimal designs using auxiliary surrogate models and two alternative approaches to design refinement: response correction techniques and co-kriging. Numerical and experimental case studies are provided to demonstrate feasibility of solving real-world and complex antenna design tasks. Findings – It is possible, through appropriate combination of the surrogate modeling techniques (both data driven and physics based) and response correction methods, to find the set of alternative designs representing the best possible trade-offs between conflicting design objectives, here, electrical performance and size. Design optimization can be performed at practically feasible computational costs. Research limitations/implications – The study demonstrates feasibility of automated multi-objective design optimization of antennas at low computational cost. The presented techniques reach beyond the commonly used design approaches based on parameter sweeps and similar hands-on methods, particularly in terms of automation, reliability and reduction of the computational costs of the design processes. Originality/value – Multi-objective design of antenna structures is very challenging when high-fidelity electromagnetic simulations are utilized for performance evaluation of the structure at hand. The proposed design framework permits rapid optimization of complex structures (here, MIMO antenna), which is hardly possible using conventional methods.

Strategies for computationally feasible multi-objective simulation-driven design of compact RF/microwave components

Purpose – Strategies for accelerated multi-objective optimization of compact microwave and RF structures are investigated, including the possibility of exploiting surrogate modeling techniques for electromagnetic (EM)-driven design speedup for such components. The paper aims to discuss these issues. Design/methodology/approach – Two algorithmic frameworks are described that are based on fast response surface approximation models, structure decomposition, and Pareto front refinement. Numerical case studies are provided demonstrating feasibility of solving real-world problems involving multi-objective optimization of miniaturized microwave passives and expensive EM-simulation models of such structures. Findings – It is possible, through appropriate combination of the surrogate modeling techniques and response correction methods, to identify the set of alternative designs representing the best possible trade-offs between conflicting design objectives in a realistic time frame corresponding to a few dozen of high-fidelity EM simulations of the respective structures. Research limitations/implications – The present study sets a direction for further studied on expedited optimization of computationally expensive simulation models for miniaturized microwave components. Originality/value – The proposed algorithmic framework proved useful for fast design of microwave structures, which is extremely challenging when using conventional methods. To the authors’ knowledge, this is one of the first attempts to surrogate-assisted multi-objective optimization of compact components at the EM-simulation level.

Fast surrogate-assisted simulation-driven optimization of compact microwave hybrid couplers

This work presents a robust methodology for expedited simulation-driven design optimization of compact microwave hybrid couplers. The technique relies on problem decomposition, and a bottom–up design strategy, starting from the level of basic building blocks of the coupler, and finishing with a tuning procedure that exploits a fast surrogate model of the entire structure. The latter is constructed by cascading local response surface approximations of coupler elementary elements. The cross-coupling effects within the structure are neglected in the first stage of the design process; however, they are accounted for in the tuning phase by means of space-mapping correction of the surrogate. The proposed approach is demonstrated through the design of a compact rat-race and two branch-line couplers. In all cases, the computational cost of the optimization process is very low and corresponds to just a few high-fidelity electromagnetic simulations of respective structures. Experimental validation is also provided.

Transient Electromagnetic Analysis of Blanket Modules 14 and 15 in Different Sectors of the ITER Blanket System due to Plasma Disruption

The ITER blanket system provides shielding of the plasma controlling field coils and vacuum vessel from the plasma heat flux as well as nuclear heating from the plasma. In addition to the thermal requirements the blanket module attachment scheme must withstand the electromagnetic forces that occur during possible plasma disruption events. During a plasma disruption event eddy currents are induced in the blanket module (first wall and shield block) and interact with the large magnetic fields to produce forces which could potentially cause mechanical failure. For this reason the design and qualification of the ITER blanket system requires appropriate high-fidelity electromagnetic simulations that capture the physics of these disruption scenarios.The key features of the analysis procedure will be described including the modeling of the geometry of the blanket modules and the plasma current during disruption.The electromagnetic calculations are performed using the Opera-3d software. This software solves the transient 3D finite element problem from which the eddy currents are calculated. The electromagnetic loads due to these eddy currents are then calculated and translated to the local coordinate system of the blanket module of interest.

Recent developments in simulation-driven multi-objective design of antennas

Abstract This paper addresses computationally feasible multi-objective optimization of antenna structures. We review two recent techniques that utilize the multi-objective evolutionary algorithm (MOEA) working with fast antenna replacement models (surrogates) constructed as Kriging interpolation of coarse-discretization electromagnetic (EM) simulation data. The initial set of Pareto-optimal designs is subsequently refined to elevate it to the high-fidelity EM simulation accuracy. In the first method, this is realized point-by-point through appropriate response correction techniques. In the second method, sparsely sampled high-fidelity simulation data is blended into the surrogate model using Co-kriging. Both methods are illustrated using two design examples: an ultra-wideband (UWB) monocone antenna and a planar Yagi-Uda antenna. Advantages and disadvantages of the methods are also discussed.

Fast simulation-driven feature-based design optimization of compact dual-band microstrip branch-line coupler

Design of miniaturized microwave components is a challenging task. On one hand, due to considerable electromagnetic (EM) cross-couplings in highly compressed layouts full-wave EM analysis is necessary for accurate evaluation of the structure performance. Conversely, high-fidelity EM simulation is computationally expensive so that automated determination of the structure dimensions may be prohibitive when using conventional numerical optimization routines. In this article, computationally efficient simulation-driven design of a miniaturized dual-band microstrip branch-line coupler is presented. The optimization methodology relies on suitably extracted features of a highly nonlinear response of the coupler structure under design. The design objectives are formulated in terms of the feature point locations, and the optimization is carried out iteratively with the linear model of the features utilized as a fast predictor. The entire process is embedded in the trust-region framework as convergence safeguard. Owing to only slightly nonlinear dependence of the features on the geometry parameters of the circuit at hand, the optimized design satisfying prescribed performance requirements is obtained at the low computational cost of only 24 high-fidelity EM simulations of the structure. Experimental validation of the fabricated coupler prototype is also provided. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:13–20, 2016.

Fast Optimization of Integrated Photonic Components Using Response Correction and Local Approximation Surrogates

Abstract A methodology for a rapid design optimization of integrated photonic couplers is presented. The proposed technique exploits variable-fidelity electromagnetic (EM) simulation models, additive response correction for accommodating the discrepancies between the EM models of various fidelities, and local response surface approximations for a fine tuning of the final design. A specific example of a 1,555 nm coupler is considered with an optimum design obtained at a computational cost corresponding to about 24 high-fidelity EM simulations of the structure.

Fast EM Modeling Exploiting Shape-Preserving Response Prediction and Space Mapping

We present a low-cost modeling methodology for microwave structures exploiting low/high-fidelity electromagnetic (EM) simulations, shape-preserving response prediction (SPRP)/generalized shape-preserving response prediction (GSPRP), and space mapping (SM). Our approach is a two-step process. In step 1, a coarse model is constructed from low-fidelity (coarse discretization) EM simulation data by means of a recently developed SPRP/GSPRP technique. A comparison between the SPRP and GSPRP methods is described. In step 2, the coarse model is enhanced by SM using a fine model (high-fidelity EM simulations). We summarize the substeps in each step of the proposed algorithm and illustrate them graphically. We compare our proposed method with relevant benchmark techniques. Our approach is demonstrated by using two microstrip filter examples. The results and efficiency of our approach are compared with the benchmark techniques, indicating substantial accuracy improvement for comparable training set size. Applications for design optimization are also discussed.

Cost-efficient electromagnetic-simulation-driven antenna design using co-Kriging

A technique for design optimisation of antennas is presented. The approach exploits coarse-discretisation electromagnetic (EM) simulations of the antenna of interest that are used to create its fast initial model (a surrogate) through Kriging. During the design process, predictions obtained by optimising the surrogate are verified using high-fidelity EM simulations, and these high-fidelity data are used to enhance the surrogate using co-Kriging interpolation that accommodates all EM simulation data into one surrogate model. The co-Kriging-based optimisation algorithm is simple and it is capable of yielding a satisfactory design at a low-cost equivalent to a few high-fidelity EM simulations of the antenna under design. To the best of author's knowledge, this is a first application of co-Kriging to antenna design. The proposed approach is demonstrated using several antenna design cases and compared with other techniques including pattern search and space mapping.

Reduced-cost microwave component modeling using space mapping-enhanced electromagnetic-based kriging surrogates

We present a versatile technique for constructing fast microwave component models at a low computational cost. Our modeling procedure consists of two stages: (i) kriging interpolation of coarsely discretized electromagnetic simulation data of the device under consideration and (ii) enhancing the kriging model by using space mapping and a limited amount of high-fidelity electromagnetic simulation data. The use of coarse discretization data allows for low-cost model generation, whereas space mapping is a convenient way of ensuring its accuracy. Our approach can be applied to almost any type of microwave device; it can also work with other types of nonsmooth or costly low-fidelity models. The operation and performance of our methodology is demonstrated using a variety of microwave components, including a microstrip filter, a planar ultrawideband antenna, and a microstrip-to-coplanar waveguide transition. Copyright © 2012 John Wiley & Sons, Ltd.

Derivative-free microwave design optimisation using shape-preserving response prediction and space mapping

A two-level derivative-free algorithm for electromagnetic-simulation-driven design optimisation is introduced. The technique exploits a fast surrogate model that is created using a modified version of a shape-preserving response prediction (SPRP) methodology and a limited amount of coarse-discretisation electromagnetic (EM) simulation data. The surrogate is subsequently utilised by the space mapping algorithm to find a design satisfying given performance specifications. As demonstrated using two examples of microstrip filters and a planar antenna, the design cost is low and corresponds to a few high-fidelity EM simulations of the optimised structure and it is 30–50% lower than that for the original SPRP technique exploiting coarse-discretisation EM models.

Electromagnetic Analysis of Forces and Torques on Selected Components of the ITER Blanket System due to Plasma Disruption

The ITER device is based on the tokamak concept of magnetic confinement in which the plasma is contained by the use of strong magnetic fields. The nearest structure to the plasma is the blanket system which provides shielding to the vacuum vessel and the superconducting magnets. There are potential abnormal operating environments where the plasma currents inside the tokamak are disrupted and induce eddy currents in the blanket (first wall and shield module). These currents interact with the large magnetic fields to produce forces in the blanket which could potentially cause mechanical failure in the first wall, shield module, or vacuum vessel. For this reason the design and qualification of the ITER blanket system requires appropriate high-fidelity electromagnetic simulations that capture the physics of these disruption scenarios.A number of different geometries will be discussed revealing the effect of different first wall designs and shield modules on the forces and torques experienced by these assemblies during plasma disruption.The key features of the modeling procedure will be presented including the plasma current modeling and geometric modeling of the first wall, shield modules, and vacuum vessel. The eddy current calculation is performed using the Opera-3d software.