Efficient simulation-driven design optimization of antennas using co-kriging

We present an efficient technique for design optimization of antenna structures. Our approach exploits coarse-discretization electromagnetic (EM) simulations of the antenna of interest that are used to create its fast initial model (a surrogate) through kriging. During the design process, the predictions obtained by optimizing the surrogate are verified using high-fidelity EM simulations, and this high-fidelity data is used to enhance the surrogate through co-kriging technique that accommodates all EM simulation data into one surrogate model. The co-kriging-based optimization algorithm is simple, elegant and is capable of yielding a satisfactory design at a low cost equivalent to a few high-fidelity EM simulations of the antenna structure. To our knowledge, this is a first application of co-kriging to antenna design. An application example is provided

Low-Fidelity Model Mesh Density and the Performance of Variable-Resolution Shape Optimization Algorithms

AbstractSurrogate based optimization (SBO) provides an interesting alternative to conventional aerodynamic shape optimization methods. By shifting the optimization burden to a cheap and yet reasonably accurate surrogate model, the design cost can be substantially reduced. SBO methods exploiting physically based surrogates can be particularly efficient because underlying low-fidelity models embed some knowledge about the system under consideration (e.g., by sharing the simulation tools with the high-fidelity models) so that good accuracy and even better generalization capability can be obtained through a correction based on a very limited number of high-fidelity model samples. The major open problem here is the proper selection of the low-fidelity model. The type of simplifications made to construct the model, as well as its level of accuracy (e.g., mesh density) may be crucial for the algorithm performance both in terms of the quality of the final design and the computational cost of the design process. Here, we investigate this trade off using space mapping (SM) as an exemplary SBO technique and two dimensional airfoil shape optimization as a representative design problem. Both lift maximization and drag minimization test cases are considered

Inverse Design of Transonic Airfoils Using Variable-Resolution Modeling and Pressure Distribution Alignment

AbstractThe paper presents a computationally efficient and robust methodology for the inverse design of transonic airfoils. The approach replaces the direct optimization of an accurate, but computationally expensive, high-fidelity airfoil model by an iterative reoptimization of a corrected low-fidelity model. The low-fidelity model is based on the same governing fluid flow equations as the high-fidelity one, but uses coarser discretization and relaxed convergence criteria. The shape-preserving response prediction technique is utilized to align the pressure distribution of the low-fidelity model with that of the high-fidelity model. This alignment process is particularly suitable since a target pressure distribution is specified in the inverse design problem. The method is applied to constrained inverse airfoil design in inviscid transonic flow. The results show that the proposed method is able to match the target pressure distributions closely and requiring over 90 percent lower computational cost than when using only the high-fidelity model

Suppressing Side-Lobes of Linear Phased Array of Micro-Strip Antennas with Simulation-Based Optimization

A simulation-based optimization approach to design of phase excitation tapers for linear phased antenna arrays is presented. The design optimization process is accelerated by means of Surrogate-Based Optimization (SBO); it uses a coarse-mesh surrogate of the array element for adjusting the array's active reflection coefficient responses and a fast surrogate of the antenna array radiation pattern. The primary optimization objective is to minimize side-lobes in the principal plane of the radiation pattern while scanning the main beam. The optimization outcome is a set of element phase excitation tapers versus the scan angle. The design objectives are evaluated at the high fidelity level of description using simulations of the discrete electromagnetic model of the entire array so that the effects of element coupling and other possible interaction within the array structure are accounted for. At the same time, the optimization process is fast due to SBO. Performance and numerical cost of the approach are demonstrated by optimizing a 16-element linear array of microstrip antennas. Experimental verification has been carried out for a manufactured prototype of the optimized array. It demonstrates good agreement between the radiation patterns obtained from simulations and from physical measurements (the latter constructed through superposition of the measured element patterns).The authors would like to thank Computer Simulation Technology AG, Darmstadt, Germany, for making CST Microwave Studio available. This work was supported in part by the Icelandic Centre for Research (RANNIS) under grant no. 141272051.Peer reviewe

Efficient simulation-driven design optimization of antennas using co-kriging

We present an efficient technique for design optimization of antenna structures. Our approach exploits coarse-discretization electromagnetic (EM) simulations of the antenna of interest that are used to create its fast initial model (a surrogate) through kriging. During the design process, the predictions obtained by optimizing the surrogate are verified using high-fidelity EM simulations, and this high-fidelity data is used to enhance the surrogate through co-kriging technique that accommodates all EM simulation data into one surrogate model. The co-kriging-based optimization algorithm is simple, elegant and is capable of yielding a satisfactory design at a low cost equivalent to a few high-fidelity EM simulations of the antenna structure. To our knowledge, this is a first application of co-kriging to antenna design. An application example is provided

The state of the art of microwave CAD: EM-based optimization and modeling

ABSTRACT: We briefly review the current state of the art of microwave CAD technologies. We look into the history of design optimization and CAD-oriented modeling of microwave circuits as well as electromagnetics-based optimization techniques. We emphasize certain direct approaches that utilize efficient sensitivity evaluations as well as surrogate-based optimization approaches that greatly enhance electromagnetics-based optimization performance. On the one hand, we review recent adjoint methodologies, on the other we focus on space mapping implementations, including the original, aggressive, implicit, output, tuning, and related developments. We illustrate our presentation with suitable examples and applications