ArticleMicromachines2023
Piezoelectric Energy Harvester Response Statistics.
Article in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
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- Parametric Aerodynamic Study of Galloping Piezoelectric Energy Harvester with Arcuate Protruding and Depressed Features.Sensors (Basel, Switzerland) · 2025Article
- Effects of Aerodynamic Parameters on Performance of Galloping Piezoelectric Energy Harvester Based on Cross-Sectional Shape Evolutionary Approach.Micromachines · 2025Article
- Performing Magnetic Boundary Modulation to Broaden the Operational Wind Speed Range of a Piezoelectric Cantilever-Type Wind Energy Harvester.Micromachines · 2024Article
- HIV deathrate prediction by Gaidai multivariate risks assessment method.Immunity, inflammation and disease · 2024Article
- Future worldwide coronavirus disease 2019 epidemic predictions by Gaidai multivariate risk evaluation method.Analytical science advances · 2024Article
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- Neuro-Evolutionary Framework for Design Optimization of Two-Phase Transducer with Genetic Algorithms.Micromachines · 2023Article
- Oil tanker under ice loadings.Scientific reports · 2023Article
- Gaidai-Xing reliability method validation for 10-MW floating wind turbines.Scientific reports · 2023Article
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- Offloading operation bivariate extreme response statistics for FPSO vessel.Scientific reports · 2023Article
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- COVID-19 Epidemic Forecast in Brazil.Bioinformatics and biology insights · 2023Article
- Gaidai reliability method for long-term coronavirus modelling.F1000Research · 2022Article
- Multi-regional COVID-19 epidemic forecast in Sweden.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Safety and reliability are essential engineering concerns for energy-harvesting installations. In the case of the piezoelectric galloping energy harvester, there is a risk that excessive wake galloping may lead to instability, overload, and thus damage. With this in mind, this paper studies bivariate statistics of the extreme, experimental galloping energy harvester dynamic response under realistic environmental conditions. The bivariate statistics were extracted from experimental wind tunnel results, specifically for the voltage-force data set. Authors advocate a novel general-purpose reliability approach that may be applied to a wide range of dynamic systems, including micro-machines. Both experimental and numerically simulated dynamic responses can be used as input for the suggested structural reliability analysis. The statistical analysis proposed in this study may be used at the design stage, supplying proper characteristic values and safeguarding the dynamic system from overload, thus extending the machine's lifetime. This work introduces a novel bivariate technique for reliability analysis instead of the more general univariate design approaches.
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