By Slawomir Koziel
This short experiences a couple of thoughts exploiting the surrogate-based optimization proposal and variable-fidelity EM simulations for effective optimization of antenna constructions. The creation of every strategy is illustrated with examples of antenna layout. The authors display the ways that practitioners can receive an optimized antenna layout on the computational rate similar 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 technique. This quantity is acceptable for electric engineers in academia in addition to undefined, antenna designers and engineers facing computationally-expensive layout problems.
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Extra resources for Antenna Design by Simulation-Driven Optimization
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. At the same time, the low-fidelity model contains only about 26,000 mesh cells, and its simulation time is only 26 s with the same computer. 2a, is substantially misaligned with that of the high-fidelity one. The response of the latter indicates that antenna geometry needs to be tuned for 5 GHz operation.
As explained in Chap. , HFSS 2010, CST Microwave Studio 2013, and FEKO 2011). Examples of antenna structures for which full-wave discrete simulation is the only modeling possibility include but are not limited to ultra-wideband (UWB) antennas (Schantz 2005), dielectric resonator antennas (DRAs) (Petosa 2007), and antenna arrays with strong element coupling (Balanis 2005). In the design optimization process, the low-fidelity model is to be simulated multiple times, either at a separate stage to create an auxiliary response surface surrogate (Koziel and Ogurtsov 2011a) or as a part of the SBO algorithm run to yield a prediction of the high-fidelity model optimum (Bandler et al.
At xk(K) = [x1(K) … xk(K) + sign(k )·dk … xn(K)]T, k = −n, −n + 1, …, n − 1, n. We use the following notation: R(k) = Rc·K(xk(K)). This data can be used to refine the final design without directly optimizing Rf. Instead, an approximation model involving R(k) is set up and optimized in the neighborhood of x(K) defined as [x(K) − d, x(K) + d], where d = [d1 d2 … dn]T. The size of the neighborhood can be selected based on sensitivity analysis of Rc·1 (the cheapest of the coarse-discretization models); usually d equals a few percent of x(K).