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

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>1/09/2018
<mark>Journal</mark>Journal of Intelligent Material Systems and Structures
Issue number15
Volume29
Number of pages11
Pages (from-to)3097-3107
Publication StatusPublished
<mark>Original language</mark>English

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 paper. First, a chosen number of points determined by Optimal Latin Hypercube (OLH) 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-OLH 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 (PFT) 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 level surrogate modeling technique.