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 language | English |
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Pages (from-to) | 3097-3107 |
Number of pages | 11 |
Journal | Journal of Intelligent Material Systems and Structures |
Volume | 29 |
Issue number | 15 |
Early online date | 9 Jul 2018 |
DOIs | |
Publication status | Published - 1 Sept 2018 |
Keywords
- Multi-level optimization strategy
- Surrogate model
- Energy harvesting
- Design of Experiments
- Genetic Programming
- Piezoelectric flex transducer
Profiles
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Dianzi Liu
- School of Engineering, Mathematics and Physics - Associate Professor in Solid Mechanics & Structural Optimization
- Materials, Manufacturing & Process Modelling - Member
- Sustainable Energy - Member
Person: Research Group Member, Academic, Teaching & Research