By Slawomir Koziel
This short studies a couple of recommendations exploiting the surrogate-based optimization inspiration and variable-fidelity EM simulations for effective optimization of antenna buildings. The advent of every approach 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 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 technique. 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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Extra resources for Antenna Design by Simulation-Driven Optimization
The two remaining models could be considered as poor ones, particularly the model with ~20,000 cells; its response is essentially unreliable. 5 Frequency [GHz] 5 103 102 4 10 105 106 The number of mesh cells 107 Fig. 4 Antenna of Fig. 3 Additional Simplifications of Low-Fidelity Antenna Models In addition to a coarser mesh, other simplifications can be made in the low-fidelity models. Possible computational simplifications include: (a) Shrinking the computational domain and applying simple absorbing boundaries with the finite-volume methods implemented in the EM software in use (HFSS 2010, CST 2013, Taflove and Hagness 2006, and Lin 2002).
2 SPRP Formulation Rigorous formulation of SPRP uses the following notation concerning the responses: Rf(x) = [Rf(x,ω1) … Rf(x,ωm)]T and Rc(x) = [Rc(x,ω1) … Rc(x,ωm)]T, where ωj, j = 1, …, m, is the frequency sweep. Let pjf = [ωjf rjf]T, pjc0 = [ωjc0 rjc0]T, and pjc = [ωjc rjc]T, j = 1, …, K, denote the sets of characteristic points of Rf(x(i)), Rc(x(i)), and Rc(x), respectively. 3 Shape-Preserving Response Prediction a ay ax 33 b us GND az ys ws w0 X Y X hs Z Fig. 4 DRA (Petosa 2007): (a) top and (b) side views respective point.
This approach, both easy to implement and efficient, is exploited by adaptively adjusted design specifications (AADS) technique (Koziel 2010b) described in this section. AADS consists of the following two simple steps that can be iterated if necessary: 1. Modify the original design specifications in order to take into account the difference between the responses of Rf and Rc at their characteristic points. 2. Obtain a new design by optimizing the low-fidelity model with respect to the modified specifications.