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

By Slawomir Koziel

ISBN-10: 3319043668

ISBN-13: 9783319043661

ISBN-10: 3319043676

ISBN-13: 9783319043678

This short reports a couple of suggestions exploiting the surrogate-based optimization thought and variable-fidelity EM simulations for effective optimization of antenna constructions. The advent of every strategy is illustrated with examples of antenna layout. The authors show the ways that practitioners can receive an optimized antenna layout on the computational expense comparable to 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 impact 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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Therefore, it appears that in optimization there should be a balance between exploitation and exploration. As suggested by Forrester and Keane (2009), this trade-off could be formulated in the context of surrogate-based optimization, for 24 3 Surrogate-Based Optimization example, by means of a bi-objective optimization problem with a global measure of the error associated to the surrogate as a second objective function. , balancing the need for exploration versus exploitation, are often referred to as efficient global optimization (EGO) techniques.

An example of a substrate integrated half-mode 5 GHz antenna shown in Fig. 1 illustrates differences in its responses evaluated with models of different fidelity as well as sensitivity of the antenna responses on the model fidelity. Both of the models are defined, discretized, and simulated using CST MWS (CST Microwave Studio 2013). 33 GHz 8 core CPU with 8 GB RAM computer. A quite dense discretization of the model, which turns in a substantial simulation time, is a result of ensuring no feasible changes of the response versus discretization density.

13) with the model parameters c(i) obtained by minimizing ||Rf(x(i)) − Rc(x(i) + c(i))||. 5 shows an example of a filter structure evaluated using EM simulation (high-fidelity model), its circuit equivalent (low-fidelity model), and the corresponding |S21| responses before and after applying the ISM correction. In many cases, the major type of discrepancy between the low- and high-fidelity models is a frequency shift. In these situations, misalignment between the models can be reduced by using frequency scaling or frequency space mapping (Koziel et al.

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Antenna Design by Simulation-Driven Optimization by Slawomir Koziel


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