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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Maximum energy conversion from human motion using piezoelectric flex transducer
T2 - A multi-level surrogate modeling strategy
AU - Luo, Liheng
AU - Liu, Dianzi
AU - Zhu, Meiling
AU - Ye, Jianqiao
PY - 2018/9/1
Y1 - 2018/9/1
N2 - 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.
AB - 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.
KW - Multi-level optimization strategy
KW - surrogate model
KW - energy harvesting
KW - design of experiments
KW - genetic programming
KW - piezoelectric flex transducer
U2 - 10.1177/1045389X18783075
DO - 10.1177/1045389X18783075
M3 - Journal article
VL - 29
SP - 3097
EP - 3107
JO - Journal of Intelligent Material Systems and Structures
JF - Journal of Intelligent Material Systems and Structures
SN - 1045-389X
IS - 15
ER -