Fast design optimization of UWB antenna with WLAN Band-Notch

In this paper, a methodology for rapid design optimization of an ultra-wideband ( UWB) monopole antenna with a lower WLAN band-notch is presented. The band-notch is realized using an open loop resonator implemented in the radiation patch of the antenna. Design optimization is a two stage process, with the first stage focused on the design of the antenna itself, and the second stage aiming at identification of the appropriate dimensions of the resonator with the purpose of allocating the band-notch in the desired frequency range. Both optimization stages are realized using surrogate-based optimization involving variable-fidelity electromagnetic ( EM) simulation models as well as an additive response correction ( first stage), and sequential approximate optimization ( second stage). The final antenna design is obtained at the CPU cost corresponding to only 23 high-fidelity EM antenna simulations

Cost-efficient modeling of antenna structures using Gradient Enhanced Kriging

Reliable yet fast surrogate models are indispensable in the design of contemporary antenna structures. Data-driven models, e.g., based on Gaussian Processes or support-vector regression, offer sufficient flexibility and speed, however, their setup cost is large and grows very quickly with the dimensionality of the design space. In this paper, we propose cost-efficient modeling of antenna structures using Gradient-Enhanced Kriging. In our approach, the training data set contains, apart from the EM-simulation responses of the structure at hand, also derivative data at the respective training locations obtained at little extra cost using adjoint sensitivity techniques. We demonstrate that introduction of the derivative information into the model allows for considerable reduction of the model setup cost (in terms of the number of training points required) without compromising its predictive power. The Gradient-Enhanced Kriging technique is illustrated using a dielectric resonator antenna structure. Comparison with conventional Kriging interpolation is also provided

Trawl-door Shape Optimization by Space-mapping-corrected CFD Models and Kriging Surrogates

AbstractTrawl-doors are a large part of the fluid flow resistance of trawlers fishing gear and has considerable effect on the fuel consumption. A key factor in reducing that consumption is by implementing computational models in the design process. This study presents a robust two dimensional computational fluid dynamics models that is able to capture the nonlinear flow past multi-element hydrofoils. Efficient optimization algorithms are applied to the design of trawl-doors using problem formulation that captures true characteristics of the design space where lift-to-drag ratio is maximized. Four design variables are used in the optimization process to control the fluid flow angle of attack, as well as position and orientation of a leading-edge slat. The optimization process involves both multi-point space mapping, and mixed modeling techniques that utilize space mapping to create a physics-based surrogate model. The results demonstrate that lift-to-drag maximization is more appropriate than lift-constraint drag minimization in this case and that local search using multi-point space mapping can yield satisfactory design at low computational cost. By using global search with mixed modeling a solution with higher quality is obtained, but at a higher computational cost than local search

Computationally Efficient Two-Objective Optimization of Compact Microwave Couplers through Corrected Domain Patching

Finding an acceptable compromise between various objectives is a necessity in the design of contemporary microwave components and circuits. A primary reason is that most objectives are at least partially conflicting. For compact microwave structures, the design trade-offs are normally related to the circuit size and its electrical performance. In order to obtain comprehensive information about the best possible trade-offs, multi-objective optimization is necessary that leads to identifying a Pareto set. Here, a framework for fast multi-objective design of compact micro-strip couplers is discussed. We use a sequential domain patching (SDP) algorithm for numerically efficient handling of the structure bandwidth and the footprint area. Low cost of the process is ensured by executing SDP at the low-fidelity model level. Due to its bi-objective implementation, SDP cannot control the power split error of the coupler, the value of which may become unacceptably high along the initial Pareto set. Here, we propose a procedure for correction of the S-parameters’ characteristics of Pareto designs. The method exploits gradients of power split and bandwidth estimated using finite differentiation at the patch centres. The gradient data are used to correct the power split ratio while leaving the operational bandwidth of the structure at hand intact. The correction does not affect the computational cost of the design process because perturbations are pre-generated by SDP. The final Pareto set is obtained upon refining the corrected designs to the high-fidelity EM model level. The proposed technique is demonstrated using two compact microstrip rat-race couplers. Experimental validation is also provided

Surrogate-based Airfoil Design with Space Mapping and Adjoint Sensitivity

AbstractThis paper presents a space mapping algorithm for airfoil shape optimization enhanced with adjoint sensitivities. The surrogate-based algorithm utilizes low-cost derivative information obtained through adjoint sensitivities to improve the space mapping matching between a high-fidelity airfoil model, evaluated through expensive CFD simulations, and its fast surrogate. Here, the airfoil surrogate model is constructed though low-fidelity CFD simulations. As a result, the design process can be performed at a low computational cost in terms of the number of high-fidelity CFD simulations. The adjoint sensitivities are also exploited to speed up the surrogate optimization process. Our method is applied to a constrained drag minimization problem in two-dimensional inviscid transonic flow. The problem is solved for several low-fidelity model termination criteria. The results show that when compared with direct gradient-based optimization with adjoint sensitivities, the proposed approach requires 49-78% less computational cost while still obtaining a comparable airfoil design

Explicit Size-Reduction-Oriented Design of a Compact Microstrip Rat-Race Coupler using Surrogate-Based Optimization Methods

In this paper, an explicit size reduction of a compact rat-race coupler implemented in a microstrip technology is considered. The coupler circuit features a simple topology with a densely arranged layout that exploits a combination of high- and low-impedance transmission line sections. All relevant dimensions of the structure are simultaneously optimized in order to explicitly reduce the coupler size while maintaining equal power split at the operating frequency of 1 ?GHz and sufficient bandwidth for return loss and isolation characteristics. Acceptable levels of electrical performance are ensured by using a penalty function approach. Two designs with footprints of 350 ?mm2 and 360 ?mm2 have been designed and experimentally validated. The latter structure is characterized by 27% bandwidth. For the sake of computational efficiency, surrogate-based optimization principles are utilized. In particular, we employ an iterative construction and re-optimization of the surrogate model involving a suitably corrected low-fidelity representation of the coupler structure. This permits rapid optimization at the cost corresponding to a handful of evaluations of the high-fidelity coupler model