Search results

The purpose of this paper is to investigate strategies and algorithms for expedited design optimization and explicit size reduction of compact ultra-wideband (UWB) antennas.

Formulation of the compact antenna design problem aiming at explicit size reduction while maintaining acceptable electrical performance is presented. Algorithmic frameworks are described suitable for handling various design situations and involving simulation models without and with response gradients available. Numerical and experimental case studies are provided demonstrating feasibility of solving real-world miniaturized antenna design tasks.

It is possible, through appropriate combination of the global and local optimization methods, surrogate modeling techniques and response correction methods, to find optimum dimensions of antenna structures that minimize antenna size while maintaining acceptable electrical performance. Design optimization can be performed at practically feasible computational costs.

The study summarizes recent advances in miniaturization-oriented design optimization of UWB antennas. The presented techniques reach far 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.

The proposed design problem formulation and algorithmic frameworks proved useful for rapid design of compact UWB antenna structures, which is extremely challenging when using conventional methods. To the knowledge, this is the first attempt to efficient solving of this type of design problems, especially in the context of explicit antenna size reduction.

A technique for accelerated design optimization of antenna input characteristics is developed and comprehensively validated using real-world wideband antenna structures. Comparative study using a conventional trust-region algorithm is provided. Investigations of the effects of the algorithm control parameters are also carried out.

An optimization methodology is introduced that replaces finite differentiation (FD) by a combination of FD and selectively used Broyden updating formula for antenna response Jacobian estimations. The updating formula is used for directions that are sufficiently well aligned with the design relocation that occurred in the most recent algorithm iteration. This allows for a significant reduction of the number of full-wave electromagnetic simulations necessary for the algorithm to converge; hence, it leads to the reduction of the overall design cost.

Incorporation of the updating formulas into the Jacobian estimation process in a selective manner considerably reduces the computational cost of the optimization process without compromising the design quality. The algorithm proposed in the study can be used to speed up direct optimization of the antenna structures as well as surrogate-assisted procedures involving variable-fidelity models.

This study sets a direction for further studies on accelerating procedures for the local optimization of antenna structures. Further investigations on the effects of the control parameters on the algorithm performance are necessary along with the development of means to automate the algorithm setup for a particular antenna structure, especially from the point of view of the search space dimensionality.

The proposed algorithm proved useful for a reduced-cost optimization of antennas and has been demonstrated to outperform conventional algorithms. To the authors’ knowledge, this is one of the first attempts to address the problem in this manner. In particular, it goes beyond traditional approaches, especially by combining various sensitivity estimation update measures in an adaptive fashion.

The purpose of this study is to propose a framework for expedited antenna optimization with numerical derivatives involving gradient variation monitoring throughout the optimization run and demonstrate it using a benchmark set of real-world wideband antennas. A comprehensive analysis of the algorithm performance involving multiple starting points is provided. The optimization results are compared with a conventional trust-region (TR) procedure, as well as the state-of-the-art accelerated TR algorithms.

The proposed algorithm is a modification of the TR gradient-based algorithm with numerical derivatives in which a monitoring of changes of the system response gradients is performed throughout the algorithm run. The gradient variations between consecutive iterations are quantified by an appropriately developed metric. Upon detecting stable patterns for particular parameter sensitivities, the costly finite differentiation (FD)-based gradient updates are suppressed; hence, the overall number of full-wave electromagnetic (EM) simulations is significantly reduced. This leads to considerable computational savings without compromising the design quality.

Monitoring of the antenna response sensitivity variations during the optimization process enables to detect the parameters for which updating the gradient information is not necessary at every iteration. When incorporated into the TR gradient-search procedures, the approach permits reduction of the computational cost of the optimization process. The proposed technique is dedicated to expedite direct optimization of antenna structures, but it can also be applied to speed up surrogate-assisted tasks, especially solving sub-problems that involve performing numerous evaluations of coarse-discretization models.

The introduced methodology opens up new possibilities for future developments of accelerated antenna optimization procedures. In particular, the presented routine can be combined with the previously reported techniques that involve replacing FD with the Broyden formula for directions that are satisfactorily well aligned with the most recent design relocation and/or performing FD in a sparse manner based on relative design relocation (with respect to the current search region) in consecutive algorithm iterations.

Benchmarking against a conventional TR procedure, as well as previously reported methods, confirms improved efficiency and reliability of the proposed approach. The applications of the framework include direct EM-driven design closure, along with surrogate-based optimization within variable-fidelity surrogate-assisted procedures. To the best of the authors’ knowledge, no comparable approach to antenna optimization has been reported elsewhere. Particularly, it surmounts established methodology by carrying out constant supervision of the antenna response gradient throughout successive algorithm iterations and using gathered observations to properly guide the optimization routine.

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.

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.

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.

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.

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.

Simulation driven design helped Lockheed Martin Aeronautical Systems solve a fatigue‐related problem on the cargo door of the C5 transport plane. Cracks in the area of the door’s upper hinge had led the Air Force to impose a special visual inspection of the door prior to each ADS mission. Use of dynamics analysis software enabled Lockheed Martin to quickly find several solutions by allowing engineers to evaluate many alternatives without testing prototypes. Within four months of taking on the project, Lockheed Martin presented the Air Force with the three best options indicated by the analysis. These included altering the hydraulic door actuator by means of a restrictive device, beefing up the surrounding structure, and redesigning the basic door mechanism to improve the interaction between the actuator and the door.

The purpose of this paper is to exploit a database of pre-existing designs to accelerate parametric optimization of antenna structures is investigated.

The usefulness of pre-existing designs for rapid design of antennas is investigated. The proposed approach exploits the database existing antenna base designs to determine a good starting point for structure optimization and its response sensitivities. The considered method is suitable for handling computationally expensive models, which are evaluated using full-wave electromagnetic (EM) simulations. Numerical case studies are provided demonstrating the feasibility of the framework for the design of real-world structures.

The use of pre-existing designs enables rapid identification of a good starting point for antenna optimization and speeds-up estimation of the structure response sensitivities. The base designs can be arranged into subsets (simplexes) in the objective space and used to represent the target vector, i.e. the starting point for structure design. The base closest base point w.r.t. the initial design can be used to initialize Jacobian for local optimization. Moreover, local optimization costs can be reduced through the use of Broyden formula for Jacobian updates in consecutive iterations.

The study investigates the possibility of reusing pre-existing designs for the acceleration of antenna optimization. The proposed technique enables the identification of a good starting point and reduces the number of expensive EM simulations required to obtain the final design.

The proposed design framework proved to be useful for the identification of good initial design and rapid optimization of modern antennas. Identification of the starting point for the design of such structures is extremely challenging when using conventional methods involving parametric studies or repetitive local optimizations. The presented methodology proved to be a useful design and geometry scaling tool when previously obtained designs are available for the same antenna structure.

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.

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.

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.

The present study sets a direction for further studied on expedited optimization of computationally expensive simulation models for miniaturized microwave components.

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.

This 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.

The 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.

The 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.

The 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.

The 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.

A framework for reliable modeling of high-frequency structures by nested kriging with an improved sampling procedure is developed and extensively validated. A comprehensive benchmarking including conventional kriging and previously reported design of experiments technique is provided. The proposed technique is also demonstrated in solving parameter optimization task.

The keystone of the proposed approach is to focus the modeling process on a small region of the parameter space (constrained domain containing high-quality designs with respect to the selected performance figures) instead of adopting traditional, hyper-cube-like domain defined by the lower and upper parameter bounds. A specific geometry of the domain is explored to improve a uniformity of the training data set. In consequence, the predictive power of the model is improved.

Building the model in a constrained domain allows for a considerable reduction of a training data set size without a necessity to either narrow down the parameter ranges or to reduce the parameter space dimensionality. Improving uniformity of training data set allocation permits further reduction of the computational cost of setting up the model. The proposed technique can be used to expedite the parameter optimization and enables locating good initial designs in a straightforward manner.

The developed framework opens new possibilities inaccurate surrogate modeling of high-frequency structures described by a large number of geometry and/or material parameters. Further extensions can be investigated such as the inclusion of the sensitivity data into the model or exploration of the particular geometry of the model domain to further reduce the computational overhead of training data acquisition.

The efficiency of the proposed method has been demonstrated for modeling and parameter optimization of high-frequency structures. It has also been shown to outperform conventional kriging and previous constrained modeling approaches. To the authors’ knowledge, this approach to formulate and handle the modeling process is novel and permits the establishment of accurate surrogates in highly dimensional spaces and covering wide ranges of parameters.

The purpose of this paper is to investigate strategies for expedited dimension scaling of electromagnetic (EM)-simulated microwave and antenna structures, exploiting the concept of variable-fidelity inverse surrogate modeling.

A fast inverse surrogate modeling technique is described for dimension scaling of microwave and antenna structures. The model is established using reference designs obtained for cheap underlying low-fidelity model and corrected to allow structure scaling at high accuracy level. Numerical and experimental case studies are provided demonstrating feasibility of the proposed approach.

It is possible, by appropriate combination of surrogate modeling techniques, to establish an inverse model for explicit determination of geometry dimensions of the structure at hand so as to re-design it for various operating frequencies. The scaling process can be concluded at a low computational cost corresponding to just a few evaluations of the high-fidelity computational model of the structure.

The present study is a step toward development of procedures for rapid dimension scaling of microwave and antenna structures at high-fidelity EM-simulation accuracy.

The proposed modeling framework proved useful for fast geometry scaling of microwave and antenna structures, which is very laborious when using conventional methods. To the authors’ knowledge, this is one of the first attempts to surrogate-assisted dimension scaling of microwave components at the EM-simulation level.