Antenna Design by Simulation-Driven Optimization by Slawomir Koziel

By Slawomir Koziel

This short studies a few options exploiting the surrogate-based optimization suggestion and variable-fidelity EM simulations for effective optimization of antenna buildings. The advent 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 fee akin 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 impression on functionality of the surrogate-based layout method. 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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Other figures of the antennas are not so sensitive to the model fidelity, as illustrated in Fig. 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.

S22| distinguished from |S11| using circles. Design specifications denoted by thick horizontal lines. (a) Rf and Rc responses at the beginning of the iteration as well as original design specifications; (b) Rf and Rc responses and modified design specifications that reflect the differences between the responses; (c) low-fidelity model optimized to meet the modified specifications; (d) high-fidelity model at the low-fidelity model optimum shown versus original specifications. Horizontal lines indicate the design specifications models is not as important as the shape similarity.

Here, xc* is the optimal design of Rc defined as xc* = argmin{xc: U(Rc(xc))}, whereas xf# can be considered as a reasonable estimate of xf*. 2 Space Mapping 27 where Rc(P(xf)) is an enhanced low-fidelity model (or, the surrogate). 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.

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