Antenna Design by Simulation-Driven Optimization by Slawomir Koziel

By Slawomir Koziel

This short stories a few ideas exploiting the surrogate-based optimization proposal and variable-fidelity EM simulations for effective optimization of antenna buildings. The creation of every approach is illustrated with examples of antenna layout. The authors show the ways that practitioners can receive an optimized antenna layout on the computational fee equivalent to a number of high-fidelity EM simulations of the antenna constitution. there's additionally a dialogue of the choice of antenna version constancy and its impression on functionality of the surrogate-based layout approach. 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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3a shows an example low-fidelity model response, |S21| in the frequency range 8–18 GHz, at the design x(i), as well as the low-fidelity model response at some other design x. The responses are of a double folded stub bandstop filter example considered in Koziel (2010a). Circles denote the characteristic points of Rc(x(i)), selected here to represent |S21| = −3 dB, |S21| = −20 dB, and the local |S21| maximum (at about 13 GHz). Squares denote the corresponding characteristic points for Rc(x), while the line segments represent the translation vectors (“shift”) of the characteristic points of Rc when changing the design variables from x(i) to x.

Here, both the low- and high-fidelity models are evaluated using EM simulation (Rc with coarse discretization). 5 Fig. 13) More information about physics-based surrogates can be found in Chap. 4. It should be emphasized that simple correction schemes such as those described here are often used as building blocks to construct more involved surrogate models (Koziel et al. 2006). 95 Fig. 3 Exploration Versus Exploitation The surrogate-based optimization process starts from an initial surrogate model which is updated using the high-fidelity model data that is accumulated in the optimization process.

The function R does a similar interpolation for data pairs {ω1,r1}, {ω1f,r1f − r1t}, …, {ωKf,rKf − rKt}, {ωm,rm}; here r1 = Rc(x,ω1) − Rc(xr,ω1) and rm = Rc(x,ωm) − Rc(xr,ωm). In other words, the function F translates the frequency components of the characteristic points of Rf(x(i)) to the frequencies at which they should be located according to the translation vectors tj, while the function R adds the necessary magnitude component. 3 Illustration Example The use of SPRP for antenna design will be demonstrated in further chapters.

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