Optimal design parameter discovery for nonlinear energy harvesters using neural optimization machine

Hossam Alqaleiby, Mahmoud Ayyad and Muhammad R. Hajj

Applied Energy, 2025, vol. 391, issue C, No S0306261925006713

Abstract: The growing need for microsensing technologies with less reliance on depletable batteries has attracted interest in the conversion of the vibrational energy available from many sources and in different forms into electrical power using piezoelectric transduction. Considering that the geometry of the vibration source limits the available volume to place the harvester, it is essential to determine the suitability of a specific harvester configuration to meet a specific power requirement. Toward this objective, we develop a neural optimization approach to search for the design parameters resulting in optimal performance of piezoelectric energy harvesters defined in terms of power density under specific excitation characteristics and constraints. A nonlinear harvester configuration, namely the magnetopiezoelastic energy harvester, is considered. Details of the numerical solution are presented. The solution is validated using previously published experimental data. The training of neural networks and the implementation of the optimization procedure are based on data generated from numerical simulations of hundreds of configurations. The details of setting up the neural optimization machine are presented. Differences in the training models and constraints required for regular and irregular excitations are pointed out. The significance of optimized configurations in each of the two cases is discussed.

Keywords: Energy harvesting; Magnetopiezoelastic; Neural optimization machine; Finite element method; Nonlinear PEH (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261925006713
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:391:y:2025:i:c:s0306261925006713

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2025.125941

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().