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Characterization of different beam shapes for piezoelectric energy harvesting
Open accessAbstract
This paper deals with the analysis of different beam shapes for piezoelectric energy harvesters. The theory is based on the well-established Rayleigh–Ritz method for piezoelectric compound structures. It is validated by experiments with triangular-shaped and rectangular-shaped beams. It turns out that triangular-shaped beams are more effective than rectangular-shaped ones in terms of curvature homogeneity independent of the proof mass. This effect is opposed by the adverse mass distribution and the increased stiffness of triangular-shaped beams. Therefore, the overall efficiency is only weakly influenced by the beam shape. Nevertheless triangular-shaped beams drastically outperform rectangular ones in terms of tolerable excitation amplitude and maximum output power.
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Citations
Cited by
Safaei, M., Sodano, H. A., & Anton, S. R. (2019) · Smart Materials and Structures · doi:10.1088/1361-665x/ab36e4 · Cited by 874
Piezoelectric Energy Harvesting Solutions
Caliò, R., Rongala, U., Camboni, D., Milazzo, M., Stefanini, C., De Petris, G., & Oddo, C. (2014) · Sensors · doi:10.3390/s140304755 · Cited by 390
Metadata
- Authors
- Goldschmidtboeing, F., & Woias, P.
- Venue
- Journal of Micromechanics and Microengineering
- Publisher
- IOP Publishing
- Published
- 2008-09-29
- Volume
- 18
- Issue
- 10
- Pages
- 104013
- ISSN
- 0960-1317 (ISSN-L)
- 1361-6439
- OpenAlex
- W2004856163
- Cited by
- 319
- References
- 0
- FWCI
- 23.47
- Type
- journal article
- Language
- en
- Open access
- bronze
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- The paper
- Goldschmidtboeing, F., & Woias, P. (2008). Characterization of different beam shapes for piezoelectric energy harvesting. Journal of Micromechanics and Microengineering. https://doi.org/10.1088/0960-1317/18/10/104013
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.