Fast Antenna Optimization Using Gradient Monitoring and Variable-Fidelity EM Models

2021 ◽  
Vol 35 (11) ◽  
pp. 1348-1349
Author(s):  
Slawomir Koziel ◽  
Anna Pietrenko-Dabrowska

Accelerated simulation-driven design optimization of antenna structures is proposed. Variable-fidelity electromagnetic (EM) analysis is used as well as the trust-region framework with limited sensitivity updates. The latter are controlled by monitoring the changes of the antenna response gradients. Our methodology is verified using three compact wideband antennas. Comprehensive benchmarking demonstrates its superiority over both conventional and surrogate-assisted algorithms.

2016 ◽  
Vol 33 (7) ◽  
pp. 2007-2018 ◽  
Author(s):  
Slawomir Koziel ◽  
Adrian Bekasiewicz

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.


2009 ◽  
Author(s):  
Emilio F. Campana ◽  
Daniele Peri ◽  
Yusuke Tahara ◽  
Manivannan Kandasamy ◽  
Frederick Stern

The use of computational methods in design engineering is growing rapidly at all stages of the design process, with the final goal of a substantial reduction of the cost and time for the development of a design. Simulations and optimization algorithms can be combined together into what is known as Simulation-Based Design (SBD) techniques. Using these tools the designers may find the minimum of some user defined objective functions with constraints, under the general mathematical framework of a Non-Linear Programming problem. There are problems of course: computational complexity, noise, robustness and accuracy of the numerical simulations, flexibility in the use of these tools; all these issues will have to be solved before the SBD methodology can become more widespread. In the paper, some derivative-based algorithms and methods are initially described, including efficient ways to compute the gradient of the objective function. Derivative-free methods - such as genetic algorithms and swarm methods are then described and compared on both algebraic tests and on hydrodynamic design problems. Both local and global hydrodynamic ship design optimization problems are addressed, defined in either a single- or a multi-objective formulation framework. Methods for reducing the computational expense are presented. Metamodels (or surrogated models) are a rigorous framework for optimizing expensive computer simulations through the use of inexpensive approximations of expensive analysis codes. The Variable Fidelity idea tries instead to alleviate the computational expense of relying exclusively on high-fidelity models by taking advantage of well-established engineering approximation concepts. Examples of real ship hydrodynamic design optimization cases are given, reporting results mostly collected through a series of projects funded by the Office of Naval Research. Whenever possible, an experimental check of the success of the optimization process is always advisable. Several examples of this testing activity are reported in the paper one is illustrated by the two pictures at the top of this page, which show the wave pattern close to the sonar dome of an Italian Navy Anti-Submarine Warfare corvette: left, the original design; right, the optimized one.


Author(s):  
Brett A. Wujek ◽  
John E. Renaud

Abstract Approximations play an important role in multidisciplinary design optimization (MDO) by offering system behavior information at a relatively low cost. Most approximate optimization strategies are sequential in which an optimization of an approximate problem subject to design variable move limits is iteratively repeated until convergence. The move limits are imposed to restrict the optimization to regions of the design space in which the approximations provide meaningful information. In order to insure convergence of the sequence of approximate optimizations to a Karush Kuhn Tucker solution a move limit management strategy is required. In this paper, issues of move-limit management are reviewed and a new adaptive strategy for move limit management is developed. With its basis in the provably convergent trust region methodology, the TRAM (Trust region Ratio Approximation Method) strategy utilizes available gradient information and employs a backtracking process using various two-point approximation techniques to provide a flexible move-limit adjustment factor. The new strategy is successfully implemented in application to a suite of multidisciplinary design optimization test problems. These implementation studies highlight the ability of the TRAM strategy to control the amount of approximation error and efficiently manage the convergence to a Karush Kuhn Tucker solution.


2000 ◽  
Vol 124 (1-2) ◽  
pp. 139-154 ◽  
Author(s):  
José F. Rodrı́guez ◽  
John E. Renaud ◽  
Brett A. Wujek ◽  
Ravindra V. Tappeta

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