Get Antenna Design by Simulation-Driven Optimization PDF

By Slawomir Koziel

ISBN-10: 3319043668

ISBN-13: 9783319043661

ISBN-10: 3319043676

ISBN-13: 9783319043678

This short reports a few thoughts exploiting the surrogate-based optimization thought and variable-fidelity EM simulations for effective optimization of antenna buildings. The advent of every technique is illustrated with examples of antenna layout. The authors display the ways that practitioners can receive an optimized antenna layout on the computational rate akin 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 effect on functionality of the surrogate-based layout approach. 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

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A popular choice of RSA is kriging interpolation (Queipo et al. 2005). Allocation of the base points is usually executed using Latin hypercube sampling (Beachkofski and Grandhi 2002). Steps 6–12 describe the flow of the SM algorithm. 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.

26) This kind of correction is also known as output space mapping (Koziel et al. 2006). , x* = argmin{x(K) − d ≤ x ≤ x(K) + d: U(q(x) + [Rf(x*) − Rc·K(x*)])} (each iteration requires only one evaluation of Rf). 7 Multi-fidelity Design Optimization 43 d1 x(0) d2 x(3) x* x(1) x(2) Fig. 12 Operation of the multi-fidelity design optimization procedure for K = 3 (three coarse-­ discretization models). The design x(j) is obtained as the optimal solution of the model Rc·j, j = 1, 2, 3. 26) The design optimization procedure can be summarized as follows (input arguments are initial design x(0) and the number of coarse-discretization models K): 1.

5 shows the responses of the high- and lowfidelity model of the DRA at a certain reference design, the construction of the SPRP surrogate, and the agreement between the SPRP-predicted and the actual high-fidelity model response. 4 Practical Issues It should be emphasized that the physics-based low-fidelity model is critical for the method’s performance. On the other hand, SPRP can be characterized as a nonparametric, nonlinear, and design-variable-dependent response correction. Its important feature is that SPRP does not use any extractable parameters which are normally found by solving a separate nonlinear minimization problem.

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Antenna Design by Simulation-Driven Optimization by Slawomir Koziel

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