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Download Antenna Design by Simulation-Driven Optimization by Slawomir Koziel PDF

By Slawomir Koziel

This short reports a couple of concepts exploiting the surrogate-based optimization suggestion and variable-fidelity EM simulations for effective optimization of antenna buildings. The creation of every process is illustrated with examples of antenna layout. The authors display the ways that practitioners can receive an optimized antenna layout on the computational price reminiscent of a couple of high-fidelity EM simulations of the antenna constitution. there's additionally a dialogue of the choice of antenna version constancy and its effect on functionality of the surrogate-based layout method. This quantity is appropriate for electric engineers in academia in addition to undefined, antenna designers and engineers facing computationally-expensive layout problems.

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Therefore, approximation surrogates are mostly suitable to build multiple-use library models. Their use for ad hoc antenna optimization is rather limited. 2 Physics-Based Surrogate Models A physics-based surrogate is created by correcting (or enhancing) the underlying low-fidelity model that is a simplified representation of the structure under design. , filters (Bandler et al. 2004a, b). In case of antennas, the only universally available way of obtaining low-fidelity models is through coarse-discretization EM simulation.

1 illustrates the basic types of SM surrogates. Elementary SM transformations described above can be combined into more involved models (Koziel et al. 2006). For example, the surrogate utilizing the input, output, and frequency SM types takes the following form: Rs ( x, p ) = Rs ( x, c, d , F ) = Rc× f ( x + c, F ) + d . In general, selection of the optimal surrogate for a given problem is not a trivial task (Koziel and Bandler 2007; Koziel et al. 2008a). 30 4 Methodologies for Variable-Fidelity Optimization of Antenna Structures EM Model Evaluation Initial Design Coarse-Discretization Model xinit Optimize CoarseDiscretization Model x(0) EM Solver Rcd High-Fidelity Model Sample Design Space Space Mapping Algorithm Evaluate CoarseDiscretization Model x(0) i=0 x(i) Set Up Coarse Model Coarse Model Setup R c Response Surface Approximation Coarse Model Coarse Model Evaluation Evaluate Fine Model x(i) Rf (x(i)) Update Surrogate Model (Parameter Extraction) Rs(i) Optimize Surrogate Model x(i+1) Termination Condition?

3) (Bandler et al. 1995) and possible misalignment of high- and low-fidelity model ranges (Alexandrov and Lewis 2001), led to numerous improvements, including parametric SM (cf. 3). 2 Aggressive Space Mapping A popular version of SM based on the original concept is aggressive SM (ASM) (Bandler et al. 1995). 2) is equivalent to reducing the residual vector f = f(xf) = P(xf) − xc* to zero. 4) for xf. The first step of the ASM algorithm is to find xc*. , P(xf(j)) = arg min{xc: Rf(xf(j)) − Rf(xc)||}.

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