Rapid design optimization of antennas using space mapping and response surface approximation models

Koziel, Slawomir; Ogurtsov, Stanislav

doi:10.1002/mmce.20555

Cited by 24 publications

(8 citation statements)

References 39 publications

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“…For example, the surrogate models have been utilized for solving wind farm layout optimization problems [28], and used for predicting surface hardness in the carburization quenching processes [29]. The estimation of the output of numerical simulations using surrogate modeling is effective and powerful way for the selection of the appropriate parameters in the complex output surface of the simulation results [30], [31]. Surrogate models describe the relationship between the input and the output by statistically learning from the given data; so that the utilization of machine learning techniques is one of the most effective way to construct the surrogate model [32].…”

Section: A Outline Of the Surrogate Modelmentioning

confidence: 99%

Operational Planning of a Residential Fuel Cell System for Minimizing Expected Operational Costs Based on a Surrogate Model

et al. 2020

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This study proposes a novel operational planning method for polymer electrolyte fuel cell cogeneration systems (PEFC-CGSs). PEFC-CGSs provide hot water by utilizing waste heat produced in the electricity generation process, and hot water is stored in an attached tank. Generating and storing hot water based on an optimal operational plan according to household demand leads to further energy saving; therefore, operational planning methods based on household demand prediction have received significant attention. However, the improvement in the demand prediction accuracy does not necessarily lead to efficient PEFC-CGS operation in terms of operational costs; in other words, the accuracy in the demand prediction does not directly indicate the resulting operational efficiency. In this study, the authors propose a novel approach based on a surrogate model for deriving an appropriate plan that minimizes the expected operational costs among the operational plan candidates. In the proposed scheme, the error between expected and actual operational costs explicitly represents the relevance of the operational plan, so that the optimal operational plan can be selected directly from the perspective of the resulting operational efficiency. The practicality of the proposed approach is evaluated with the existing demand prediction-based approach via numerical simulations using real-world measurements of multiple customers in Japan. The proposed method reveals 30% reduction of the excessive operational costs by avoiding the inefficient operation of the auxiliary gas-heater in the experiments and will further enhance the value of introducing highly efficient residential fuel cell system that contributes to a low-carbon society. INDEX TERMS Cost minimization, machine learning, operational planning, polymer electrolyte fuel cell cogeneration systems, surrogate model.

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Section: A Outline Of the Surrogate Modelmentioning

confidence: 99%

Operational Planning of a Residential Fuel Cell System for Minimizing Expected Operational Costs Based on a Surrogate Model

et al. 2020

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“…In SBO, the optimization burden is shifted to a computationally cheap surrogate model. A typical surrogate is a suitably corrected coarsely discretized EM-based model or circuit representation of a structure; however, SBO methods may be combined with response surface approximation (RSA) models [7], which can reduce evaluation cost of the surrogate model while maintaining good generalization capability, typical for physics-based low-fidelity models.…”

Section: Introductionmentioning

confidence: 99%

Simulation-driven design of planar filters using response surface approximations and space mapping

Koziel

Bekasiewicz

2014

2014 44th European Microwave Conference

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A robust approach for low-cost design optimization of planar microwave filters is presented. Our technique exploits response surface approximation (RSA) models of filter cells as well as space mapping (SM) as an optimization engine. The RSA models are constructed beforehand using sampled EMsimulation data of the cells of interest. The filter structure of a required order is subsequently assembled by cascading selected models. The process of adjusting the geometry parameters of the cells is realized by means of surrogate-based optimization with the combined RSA model as an underlying low-fidelity model, which is corrected using input SM and frequency scaling. The design optimization cost is very low and corresponds to a few EM simulations of the entire filter structure. The proposed technique is illustrated using several examples of bandpass filters constructed using three types of basic cells: stubs, transmission lines, and stepped impedance resonators.

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“…To some extent, these problems can be alleviated by using the adjoint sensitivity techniques [5], [6]. The surrogate-based optimization (SBO) techniques such as space mapping [7] and shape-preserving response prediction [8], also in combination with response surface approximation models [9]- [11], have proved to be computationally more efficient than the traditional approaches for simulation-drive optimization. In SBO, direct optimization of the high-fidelity antenna model is replaced by an iterative correction and re-optimization of the underlying low-fidelity model, which, in case of antennas, usually comes from coarse-discretization simulations [12].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Design of Antennas Using Variable-Fidelity Simulations and Surrogate Models

Koziel

Ogurtsov

2013

IEEE Trans. Antennas Propagat.

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152

131

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A computationally-efficient procedure for multi-objective design of antenna structures is presented. Our approach exploits the multi-objective evolutionary algorithm (MOEA) working with a fast antenna surrogate model obtained with kriging interpolation of coarse-discretization simulation data. Response correction techniques are subsequently applied to refine the designs obtained by MOEA. Our methodology allows us to obtain-at a low computational cost-a set of designs corresponding to various trade-offs between the antenna size and the refection coefficient. Two illustration examples are considered: (i) an UWB monocone with two objectives being reduction of the antenna size and minimization of the antenna reflection coefficient in the bandwidth of interest, and (ii) a planar Yagi antenna with the objectives being an increase of the end-fire gain and minimization of the reflection coefficient, both in the bandwidth of interest.Index Terms-Antenna design, computer-aided design (CAD), evolutionary algorithms, multi-objective optimization, surrogate models.

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Rapid design optimization of antennas using space mapping and response surface approximation models

Cited by 24 publications

References 39 publications

Operational Planning of a Residential Fuel Cell System for Minimizing Expected Operational Costs Based on a Surrogate Model

Operational Planning of a Residential Fuel Cell System for Minimizing Expected Operational Costs Based on a Surrogate Model

Simulation-driven design of planar filters using response surface approximations and space mapping

Multi-Objective Design of Antennas Using Variable-Fidelity Simulations and Surrogate Models

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