# Antenna Design by Simulation-Driven Optimization by Slawomir Koziel, Stanislav Ogurtsov

By Slawomir Koziel, Stanislav Ogurtsov

This short experiences a couple of concepts exploiting the surrogate-based optimization inspiration 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 reveal the ways that practitioners can receive an optimized antenna layout on the computational fee such as 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 effect 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 difficulties.

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Additional resources for Antenna Design by Simulation-Driven Optimization

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2c and d where we see no essential differences of the gain patterns of the two models. 7 c d 30 60 60 90 10 0 47 −10 −20 [dB] −20 −10 0 90 10 30 0 30 60 90 10 60 0 −10 −20 [dB] −20 −10 0 90 10 Fig. , HFSS (2010), CST Microwave Studio (2013), and FEKO (2011), are extensively used in the modern antenna design in both industry and academia (Kempel 2007). Not long time ago, discrete EM simulators were used mostly for design verification purposes. Nowadays, due to the progress in computing hardware as well as development of computational electromagnetic methods, the discrete EM simulators turn to be indispensable for the entire design process starting from a concept estimation step.

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.

The last two exploit radial basis function surrogates rather than kriging. Chapter 4 Methodologies for Variable-Fidelity Optimization of Antenna Structures In this chapter, we formulate and discuss several surrogate-based optimization techniques and algorithms that may be useful for computationally efficient antenna optimization. All methods presented here exploit variable-fidelity EM simulations. In particular, in order to optimize the high-fidelity EM model Rf of the antenna structure under consideration, an auxiliary low-fidelity model Rc is utilized that is normally based on coarse-discretization EM evaluation of the same structure.