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 experiences a couple of ideas 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 show the ways that practitioners can receive an optimized antenna layout on the computational expense 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 effect on functionality of the surrogate-based layout procedure. 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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1 shows the flowchart of the design process. 3 Shape-Preserving Response Prediction Shape-preserving response prediction (SPRP) (Koziel 2010a) is one of the recent SBO techniques that exploit the knowledge embedded in the low-fidelity model in order to predict the high-fidelity model response. Unlike space mapping, SPRP does not use any extractable parameters, and, therefore, it is more suitable for antenna design. 1). To construct the surrogate model, SPRP assumes that the change of the high-fidelity model response due to adjustments of the design variables can be predicted using the actual changes of the low-fidelity model response.

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. It should be stressed that the low-fidelity model is not modified in any way, that is, no changes are applied to it in order to align it with the high-fidelity model.

2011c) ( ) i ( DC = éê Rc x( ) - Rc x ( ë i -1) ) ( ) ( Rc x( ) - Rc x( ¼ i max{i - n , 0}) )ùûú . 20) The matrix S(0) is typically taken as the identity matrix Im. 21) where UΔC, ∑ΔC, and VΔC are the factors in the singular value decomposition of ΔC. The matrix ∑ΔC† is the result of inverting the nonzero entries in ∑ΔC, leaving the zeroes invariant (Echeverria and Hemker 2005). Some mild general assumptions on the model responses are made in theory (Echeverria 2007a) so that every pseudoinverse introduced is well defined.

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Antenna Design by Simulation-Driven Optimization by Slawomir Koziel

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