By Slawomir Koziel, Stanislav Ogurtsov
ISBN-10: 3319043668
ISBN-13: 9783319043661
This short studies a few recommendations exploiting the surrogate-based optimization suggestion and variable-fidelity EM simulations for effective optimization of antenna buildings. The creation of every technique is illustrated with examples of antenna layout. The authors show the ways that practitioners can receive an optimized antenna layout on the computational fee comparable 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 appropriate for electric engineers in academia in addition to undefined, antenna designers and engineers facing computationally-expensive layout difficulties.
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Extra info for Antenna Design by Simulation-Driven Optimization
Sample text
3. Because of possible simplifications, the low-fidelity model Rc is faster than Rf, typically it can be made 10–50 times faster; however, model Rc is obviously not as accurate as Rf. Therefore, the low-fidelity model cannot simply replace the high-fidelity model in the design optimization process. 4 shows the high- and low-fidelity model responses at a specific design for the antenna of Fig. 3 obtained with different meshes, as well as the relationship between mesh coarseness and simulation time.
4 that the two “finest” coarse-discretization models (with ~400,000 and ~740,000 mesh cells) represent the high-fidelity model response (shown as a thick solid line) quite properly. The model with ~270,000 cells can be considered as a borderline one. 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.
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. It is important that the low-fidelity model is physics based so that the effect of the design parameter variations on the model response is similar for both models. In the context of antenna design, this property is generally ensured by using coarse-discretization low-fidelity models evaluated using the same EM solver as for the high-fidelity models.
Antenna Design by Simulation-Driven Optimization by Slawomir Koziel, Stanislav Ogurtsov
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