Maximum energy conversion from human motion using piezoelectric flex transducer: A multi-level surrogate modeling strategy

Liheng Luo, Dianzi Liu, Meiling Zhu, Yijie Liu, Jianqiao Ye

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
15 Downloads (Pure)

Abstract

Conventional engineering design optimization requires a large amount of expensive experimental tests from prototypes or computer simulations, which may result in an inefficient and unaffordable design process. In order to overcome these disadvantages, a surrogate model may be used to replace the prototype tests. To construct a surrogate model of sufficient accuracy from limited number of tests/simulations, a multi-level surrogate modeling strategy is introduced in this article. First, a chosen number of points determined by optimal Latin Hypercube Design of Experiments are used to generate global-level surrogate models with genetic programming and the fitness landscape can be explored by genetic algorithms for near-optimal solutions. Local-level surrogate models are constructed then from the extended-optimal Latin Hypercube samples in the vicinity of global optimum on the basis of a much smaller number of chosen points. As a result, an improved optimal design is achieved. The efficiency of this strategy is demonstrated by the parametric optimization design of a piezoelectric flex transducer energy harvester. The optimal design is verified by finite element simulations and the results show that the proposed multi-level surrogate modeling strategy has the advantages of faster convergence and more efficiency in comparison with the conventional single-single level surrogate modeling technique.
Original languageEnglish
Pages (from-to)3097-3107
Number of pages11
JournalJournal of Intelligent Material Systems and Structures
Volume29
Issue number15
Early online date9 Jul 2018
DOIs
Publication statusPublished - 1 Sep 2018

Keywords

  • Multi-level optimization strategy
  • Surrogate model
  • Energy harvesting
  • Design of Experiments
  • Genetic Programming
  • Piezoelectric flex transducer

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