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Analytical Modelling and Optimization of a Piezoelectric Cantilever Energy Harvester with In-Span Attachment

1
Laboratoire Génie de Production, National School of Engineering in Tarbes (ENIT), Toulouse INP, University of Toulouse, 47, Avenue d’Azereix, 65000 Tarbes, France
2
Department of Automatic Control and Micro-Mechatronic Systems, FEMTO-ST Institute, Université Bourgogne Franche-Comté, CNRS, 24 rue Alain Savary, 25000 Besançon, France
*
Author to whom correspondence should be addressed.
Micromachines 2020, 11(6), 591; https://doi.org/10.3390/mi11060591
Received: 25 May 2020 / Revised: 10 June 2020 / Accepted: 10 June 2020 / Published: 13 June 2020
In this paper, the location of masses and of a piezoelectric patch for energy harvesting reported onto a vibrating cantilever beam is studied and optimized. To this aim, a genetic algorithm is adapted and utilized to optimize the voltage amplitude generated by the piezoelectric patches by choosing attachment mass, attachment mass moment of inertia, attachment location, piezoelectric patch location and force location on the beam as parameters. While an analytical approach is proposed to evaluate the voltage amplitude, a multi-layer perceptron neural network is trained by the derived characteristic matrix to obtain an approximate function for natural frequencies based on the attachment parameters. The trained network is then used in the core of genetic algorithm to find the best optimization variables for any excitation frequency. Numerical simulation by COMSOL Multiphysics finite element software validates the calculated voltage by analytical approach. The optimization method successfully matches the natural frequency of the beam with the excitation frequency which therefore maximizes the output energy. On the other hand, the superiority of the optimized design over the conventional configuration in harvesting the energy at high frequency excitation is also approved. View Full-Text
Keywords: energy harvesting; cantilever configuration; piezoelectric patch; neural network; genetic algorithm energy harvesting; cantilever configuration; piezoelectric patch; neural network; genetic algorithm
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Homayouni-Amlashi, A.; Mohand-Ousaid, A.; Rakotondrabe, M. Analytical Modelling and Optimization of a Piezoelectric Cantilever Energy Harvester with In-Span Attachment. Micromachines 2020, 11, 591.

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