Multiobjective Antenna Design By Means of Sequential Domain Patching

Purpose Development of techniques for expedited design optimization of complex and numerically expensive electromagnetic (EM) simulation models of antenna structures validated both numerically and experimentally. The paper aims to discuss these issues. Design/methodology/approach The optimization task is performed using a technique that combines gradient search with adjoint sensitivities, trust region framework, as well as EM simulation models with various levels of fidelity (coarse, medium and fine). Adaptive procedure for switching between the models of increasing accuracy in the course of the optimization process is implemented. Numerical and experimental case studies are provided to validate correctness of the design approach. Findings Appropriate combination of suitable design optimization algorithm embedded in a trust region framework, as well as model selection techniques, allows for considerable reduction of the antenna optimization cost compared to conventional methods. Research limitations/implications The study demonstrates feasibility of EM-simulation-driven design optimization of antennas at low computational cost. The presented techniques reach beyond the common design approaches based on direct optimization of EM models using conventional gradient-based or derivative-free methods, particularly in terms of reliability and reduction of the computational costs of the design processes. Originality/value Simulation-driven design optimization of contemporary antenna structures is very challenging when high-fidelity EM simulations are utilized for performance utilization of structure at hand. The proposed variable-fidelity optimization technique with adjoint sensitivity and trust regions permits rapid optimization of numerically demanding antenna designs (here, dielectric resonator antenna and compact monopole), which cannot be achieved when conventional methods are of use. The design cost of proposed strategy is up to 60 percent lower than direct optimization exploiting adjoint sensitivities. Experimental validation of the results is also provided.

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

Numerical optimization has been ubiquitous in antenna design for over a decade or so. It is indispensable in handling of multiple geometry/material parameters, performance goals, and constraints. It is also challenging as it incurs significant CPU expenses, especially when the underlying computational model involves full-wave electromagnetic (EM) analysis. In most practical cases, the latter is imperative to ensure evaluation reliability. The numerical challenges are even more pronounced when global search is required, which is most often carried out using nature-inspired algorithms. Population-based procedures are known for their ability to escape from local optima, yet their computational efficiency is poor, which makes them impractical when applied directly to EM models. A common workaround is the utilization of surrogate modeling techniques, typically in the form of iterative prediction-correction schemes, where the accumulated EM simulation data is used to identify the promising regions of the parameter space and to refine the surrogate model predictive power at the same time. Notwithstanding, implementation of surrogate-assisted procedures is often intricate, whereas their efficacy may be hampered by the dimensionality issues and considerable nonlinearity of antenna characteristics. This work investigates the benefits of incorporating variable-resolution EM simulation models into nature-inspired algorithms for optimization of antenna structures, where the model resolution pertains to the level of discretization density of an antenna structure in the full-wave simulation model. The considered framework utilizes EM simulation models which share the same physical background and are selected from a continuous spectrum of allowable resolutions. The early stages of the search process are carried out with the use of the lowest fidelity model, which is subsequently automatically increased to finally reach the high-fidelity antenna representation (i.e., considered as sufficiently accurate for design purposes). Numerical validation is executed using several antenna structures of distinct types of characteristics, and a particle swarm optimizer as the optimization engine. The results demonstrate that appropriate resolution adjustment profiles permit considerable computational savings (reaching up to eighty percent in comparison to high-fidelity-based optimization) without noticeable degradation of the search process reliability. The most appealing features of the presented approach—apart from its computational efficiency—are straightforward implementation and versatility.

We inferred the effective sheath width using the cutoff probe and incorporating a full-wave three-dimensional electromagnetic (EM) simulation. The EM simulation reproduced the experimentally obtained plasma-sheath resonance (PSR) on the microwave transmission (S21) spectrum well. The PSR frequency has a one-to-one correspondence with the width of the vacuum layer assumed to be the effective sheath in the EM simulation model. The sheath width was estimated by matching the S21 spectra of the experiment and the EM simulation for different widths of the sheath. We found that the inferred sheath widths quantitatively and qualitatively agree with the sheath width measured by incorporating an equivalent circuit model. These results demonstrate the excellent potential of the cutoff probe for inferring the effective sheath width from its experimental spectrum data.

A simple yet robust and computationally efficient methodology for design optimization of microwave filters is presented. The proposed algorithm exploits variable-fidelity electromagnetic (EM) simulation models and adjoint sensitivities for fast evaluation of EM model response derivatives. Most of the operations are performed on the low-fidelity EM model, whose correction is realized by means of space mapping (SM). In the optimization process, the SM parameters are optimized together with the design itself, which allows us to keep the number of both low- and high-fidelity simulations at low levels. The operation and performance of the method is demonstrated using two examples of waveguide filters.

Rapid antenna design optimization using shape-preserving response prediction An approach to rapid optimization of antennas using the shape-preserving response-prediction (SPRP) technique and coarse-discretization electromagnetic (EM) simulations (as a low-fidelity model) is presented. SPRP allows us to estimate the response of the high-fidelity EM antenna model, e.g., its reflection coefficient versus frequency, using the properly selected set of so-called characteristic points of the low-fidelity model response. The low-fidelity model, corrected by means of SPRP, is subsequently used to predict the optimal design. The design process is cost efficient because most operations are performed on the low-fidelity model. Performance of our technique is demonstrated using a dielectric resonator antenna and two planar wideband antenna examples. In all cases, the optimal design is obtained at a cost corresponding to a few high-fidelity simulations of the antenna under design.

A holistic approach to risk-based decision on inspection and design of fatigue-sensitive structures

We present an efficient technique for design optimization of antenna structures. Our approach exploits coarse-discretization 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, the predictions obtained by optimizing the surrogate are verified using high-fidelity EM simulations, and this high-fidelity data is used to enhance the surrogate through co-kriging technique that accommodates all EM simulation data into one surrogate model. The co-kriging-based optimization algorithm is simple, elegant and is capable of yielding a satisfactory design at a low cost equivalent to a few high-fidelity EM simulations of the antenna structure. To our knowledge, this is a first application of co-kriging to antenna design. An application example is provided.

In this study, a technique for low‐cost multi‐objective design optimisation of antenna structures has been proposed. The proposed approach is an enhancement of a recently reported surrogate‐assisted technique exploiting variable‐fidelity electromagnetic (EM) simulations and auxiliary kriging interpolation surrogate, the latter utilised to produce the initial approximation of the Pareto set. A bottleneck of the procedure for higher‐dimensional design spaces is a large number of training data samples necessary to construct the surrogate. Here, the authors propose a procedure that allows us to confine the model domain to the subset spanned by the reference points, including the extreme Pareto‐optimal designs obtained by optimising the individual objectives as well as an additional design that determines the Pareto front curvature. Setting up the surrogate in the constrained domain leads to a dramatic reduction of the required number of data samples, which results in lowering the overall cost of the optimisation process. Furthermore, the model domain confinement is generic, i.e. applicable for any number of design goals considered. The proposed technique is demonstrated using an ultra‐wideband monopole antenna optimised with respect to three objectives. Significant reduction of the design cost is obtained as compared to the reference surrogate‐assisted algorithm.

PurposeThis paper aims to summarize design rules based on the process characteristics of selective laser melting (SLM) and structural optimization and apply the design rules in the lightweight design of an aluminum alloy antenna bracket. The design goal is to reduce 30 per cent of the weight while maintaining the stress levels in the original part.Design/methodology/approachTo reduce weight as much as possible, the titanium alloy with higher specific strength was selected during the process of optimization. The material distribution of the bracket was improved by the topology optimization design. The redesign for SLM was used to obtain an optimization model, which was more suitable for SLM. The component performance was improved by shape optimization. The modal analysis data of the structural optimization model were compared with those of the stochastic lightweight model to verify the structural optimization model. The scanning data were compared with those of the original model to verify whether the model was suitable for SLM.FindingsStructural optimization design for antenna bracket realized the mass decrease of 30.43 per cent and the fundamental frequency increase of 50.18 per cent. The modal analysis data of the stochastic lightweight model and the structural optimization model indicated that the optimization performance of structural optimization method was better than that of the stochastic lightweight method. The comparison results between the scanning data of the forming part and the original data confirmed that the structural optimization design for SLM lightweight component could achieve the desired forming accuracy.Originality/valueThis paper summarizes geometric constraints in SLM and derives design rules of structural optimization based on the process characteristics of SLM. SLM design rules make structural optimization design more reasonable. The combination of structural optimization design and SLM can improve the performance of lightweight antenna bracket significantly.

Efficient planning of construction operations is deemed necessary to meet project objectives. Researchers have used simulation optimization to select the optimum amount of equipment and number of crews for construction operations. However, the current state of the practice suffers from the long computation time and the presence of inferior solutions in the final Pareto front. The objective of this paper is to develop and evaluate a robust simulation optimization framework. This framework is capable of reducing the computation time, improving the quality of optimal solutions, and increasing the confidence level in the optimality of the optimal solutions. This paper proposes the integration of common random numbers and parallel computing to achieve the stated objective. The parallel computing is performed on a single multicore processor. Based on the case study, the proposed framework was able to reduce the computation time by 90.5%, achieve a speedup of 2, improve the hypervolume indicator by 3.44%, and increase the confidence level by at least 100%. The values of improvement achieved will not necessarily be the same when different hardware, simulation models, simulation software, and optimization algorithms are used. The proposed framework allows project planners to obtain superior optimal solutions faster, which will make the use of stochastic simulation optimization more appealing.

Performance analyses of naval ships based on engineering level of simulation at the initial design stage

Computational multiobjective topology optimization of silicon anode structures for lithium-ion batteries

Tuning space mapping (TSM) is one of the latest developments in space mapping technology. TSM algorithms offer a remarkably fast design optimisation with satisfactory results obtained after one or two iterations, which amounts to just a few electromagnetic simulations of the optimised microwave structure. The TSM algorithms (as exemplified by `Type-1` tuning) could be simply implemented manually. The approach may require interaction between various electromagnetic-based and circuit models, as well as handling different sets of design variables and control parameters. As a result, certain TSM algorithms (especially so-called `Type-0` tuning) may be tedious, thus, error-prone to implement. Here, we present a fully automated tuning space mapping implementation that exploits the functionality of our user-friendly space mapping software, the SMF system. The operation and performance of our new implementation is illustrated through the design of a box-section Chebyshev bandpass filter and a capacitively coupled dual-behaviour resonator filter.

Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures