Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Robust optimal design of low frequency metamaterials considering the fabrication imperfections and parameter fluctuations

Robust optimal design of low frequency metamaterials considering the fabrication imperfections... The purpose of this paper is to develop a robust optimization methodology for metamaterial (MM) unit designs to minimize the effect of manufacturing and operational uncertainties.Design/methodology/approachA new robustness quantification function, applicable to both convex and nonconvex relationships between the mean and the standard deviation, is introduced. A distance-based local radial basis function network surrogate model is proposed to substitute the global radial basis function network to reduce the heavy computational cost without any scarification on the solution accuracy.FindingsThe optimized results of a prototype MM unit demonstrate the feasibility and merit of the proposed methodology. The proposed methodology outperforms the existing ones in both performance and robust parameters in the design of a prototype MM unit.Originality/valueIt provides a robust optimization methodology for MM units when considering the imperfections in fabrications and fluctuations in operation and environment conditions in engineering applications. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png COMPEL: Theinternational Journal for Computation and Mathematics in Electrical and Electronic Engineering Emerald Publishing

Robust optimal design of low frequency metamaterials considering the fabrication imperfections and parameter fluctuations

Download PDF
12 pages

Loading next page...
 
/lp/emerald-publishing/robust-optimal-design-of-low-frequency-metamaterials-considering-the-9qkac26QXE
Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0332-1649
eISSN
0332-1649
DOI
10.1108/compel-01-2022-0034
Publisher site
See Article on Publisher Site

Abstract

The purpose of this paper is to develop a robust optimization methodology for metamaterial (MM) unit designs to minimize the effect of manufacturing and operational uncertainties.Design/methodology/approachA new robustness quantification function, applicable to both convex and nonconvex relationships between the mean and the standard deviation, is introduced. A distance-based local radial basis function network surrogate model is proposed to substitute the global radial basis function network to reduce the heavy computational cost without any scarification on the solution accuracy.FindingsThe optimized results of a prototype MM unit demonstrate the feasibility and merit of the proposed methodology. The proposed methodology outperforms the existing ones in both performance and robust parameters in the design of a prototype MM unit.Originality/valueIt provides a robust optimization methodology for MM units when considering the imperfections in fabrications and fluctuations in operation and environment conditions in engineering applications.

Journal

COMPEL: Theinternational Journal for Computation and Mathematics in Electrical and Electronic Engineering – Emerald Publishing

Published: Jan 12, 2023

Keywords: Genetic algorithm; Metamaterials; Surrogate model; Design optimization methodology; Robust design

References

  • A kriging-assisted light beam search method for multi-objective electromagnetic inverse problems
    An, S.; Yang, S.; Mohammed, O.A.
  • Extremely subwavelength planar magnetic metamaterials
    Bingham, C.M.; Mak, K.M.; Caira, N.W.; Padilla, W.J.
  • Robust method to retrieve the constitutive effective parameters of metamaterials
    Chen, X.; Grzegorczyk, T.M.; Wu, B.I.; Pacheco, J.; Jin, A.K.
  • A fast and elitist multiobjective genetic algorithm: NSGA-II
    Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T.
  • A paretian approach to optimal design with uncertainties: application in induction heating
    Di Barba, P.; Dughiero, F.; Forzan, M.; Sieni, E.
  • Handling sensitivity in multiobjective design optimization of MFH inductors
    Di Barba, P.; Dughiero, F.; Forzan, M.; Sieni, E.
  • Numerical methods for metamaterial design
    Diest, K.
  • Robust optimization using a gradient index: MEMS applications
    Han, J.S.; Kwak, B.M.
  • A fast robust optimization methodology based on polynomial chaos and evolutionary algorithm for inverse problems
    Ho, S.L.; Yang, S.
  • Robust optimization utilizing the second-order design sensitivity information
    Kim, N.H.; Kim, D.H.; Kim, D.W.; Kim, H.G.; Lowther, D.A.; Sykulski, J.K.
  • Robust optimization considering tolerances of design variables
    Lee, K.H.; Park, G.J.
  • Global and local meta-models for the robust design of electrical machines
    Li, M.; Silva, R.C.P.; Lowther, D.A.
  • The weighted sum method for multi-objective optimization: new insights
    Marler, R.T.; Arora, J.S.
  • Extraction of material parameters for metamaterials using a full-wave simulator [education column]
    Numan, A.B.; Sharawi, M.S.
  • Max-min surrogate-assisted evolutionary algorithm for robust design
    Ong, Y.S.; Nair, P.B.; Kai, Y.L.
  • Robust design with Surrogate-Assisted evolutionary algorithm: Does it work
    Silva, R.C.P.; Li, M.; Ghorbanian, V.; Guimarães, F.G.; Lowther, D.A.; Korošec, P.; Melab, N.; Talbi, E.G.
  • A versatile surrogate-assisted memetic algorithm for optimization of computationally expensive functions and its engineering applications
    Tenne, Y.; Armfield, S.W.; Yang, A.; Shan, Y.; Bui, L.T.
  • A global optimization algorithm based on C1 piecewise response surface patches
    Yao, Y.; Koh, C.S.; Yang, S.
  • Robustness-based design optimization under data uncertainty
    Zaman, K.; Mcdonald, M.; Mahadevan, S.; Green, L.