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

Luo, Liheng, Liu, Dianzi, Zhu, Meiling, Liu, Yijie and Ye, Jianqiao (2018) Maximum energy conversion from human motion using piezoelectric flex transducer: A multi-level surrogate modeling strategy. Journal of Intelligent Material Systems and Structures, 29 (15). pp. 3097-3107. ISSN 1530-8138

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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.

Item Type:	Article
Uncontrolled Keywords:	multi-level optimization strategy,surrogate model,energy harvesting,design of experiments,genetic programming,piezoelectric flex transducer
Faculty \ School:	Faculty of Science > School of Mathematics (former - to 2024)
UEA Research Groups:	Faculty of Science > Research Groups > Sustainable Energy Faculty of Science > Research Groups > Materials, Manufacturing & Process Modelling
Depositing User:	Pure Connector
Date Deposited:	21 May 2018 16:30
Last Modified:	07 Nov 2024 12:40
URI:	https://ueaeprints.uea.ac.uk/id/eprint/67154
DOI:	10.1177/1045389X18783075

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