A finite element model to analyze the dynamic characteristics of galloping based piezoelectric energy harvester

Dash, Rakesha Chandra; Maiti, Dipak Kumar; Singh, B. N.

doi:10.1080/15376494.2021.1921316

Cited by 11 publications

(3 citation statements)

References 23 publications

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“…(2020); for EH dynamic stability analysis and stochastic dynamics, see Dash et al. (2023) and Dash (2023). Most of the aforementioned studies have been focused on EH performance enhancements, and few of them paid attention to practical design issues, such as EH's extreme response and fatigue life.…”

Section: Introductionmentioning

confidence: 99%

Energy harvester reliability study by Gaidai reliability method

Gaidai,

Sun,

Wang

2024

Climate Resilience

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This study validates a novel structural reliability method, particularly suitable for high‐dimensional green energy harvesting device dynamic systems, versus a well‐established bivariate statistical method, known to accurately predict two‐dimensional system extreme response contours. Classic reliability methods dealing with time series do not always have an advantage of dealing easily with dynamic system high dimensionality, along with complex cross‐correlations among different system components. Energy harvesters constitute an important part of modern offshore green energy engineering; hence, proper experimental study along with safety and reliability analysis are of practical design and engineering importance. To study the performance of galloping energy harvesters, a series of laboratory wind tunnel tests have been conducted, selecting different wind speeds. This study illustrates the usage of the advocated novel reliability method, by analyzing bivariate statistics of experimental galloping energy harvester's dynamics. The bivariate statistics was extracted from available experimental results, more specifically for the device's voltage‐force dataset. Advantage of the proposed methodology being that relatively short experimental data record may still yield meaningful design results, provided proper statistical methods have been applied. Safety and reliability are important engineering concerns for all kinds of green energy devices. In the case of measured device's structural response, an accurate prediction of system failure or damage probability is possible, as illustrated in this study. Distinctive advantage of advocated novel semi‐analytical reliability methodology being the fact that it can tackle dynamic systems with practically unlimited number of dimensions (or components), along with complex nonlinear cross‐correlations between different system key components.

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Section: Introductionmentioning

confidence: 99%

Energy harvester reliability study by Gaidai reliability method

Gaidai,

Sun,

Wang

2024

Climate Resilience

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“…A recent study (Dash et al, 2020, 2022), the effects of different order representations of the aerodynamic force on the behavior of GPEH system is investigated. A finite element model is proposed to investigate the dynamic characteristics of the GPEH system (Dash et al, 2021a, 2021b). The inclination effects of the square prism on the system performance of GPEH are numerically investigated by Dash et al (2021c).…”

Section: Introductionmentioning

confidence: 99%

Uncertainty analysis of galloping based piezoelectric energy harvester system using polynomial neural network

Dash

Sharma

Maiti

et al. 2022

Journal of Intelligent Material Systems and Structures

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This paper deals with the impact of uncertain input parameters on the electrical power generation of galloping-based piezoelectric energy harvester (GPEH). A distributed parameter model for the system is derived and solved by using Newmark beta numerical integration technique. Nonlinear systems tend to behave in a completely different manner in response to a slight change in input parameters. Due to the complex manufacturing process and various technical defects, randomness in system properties is inevitable. Owing to the presence of randomness within the system parameters, the actual power output differs from the expected one. Therefore, stochastic analysis is performed considering uncertainty in aerodynamic, mechanical, and electrical parameters. A polynomial neural network (PNN) based surrogate model is used to analyze the stochastic power output. A sensitivity analysis is conducted and highly influenced parameters to the electric power output are identified. The accuracy and adaptability of the PNN model are established by comparing the results with Monte Carlo simulation (MCS). Further, the stochastic analyses of power output are performed for various degrees of randomness and wind velocities. The obtained results showed that the influence of the electromechanical coefficient on power output is more compared to other parameters.

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“…Once the FIV-PEH is placed in a flow field with a sufficiently high flow speed, FIVs may occur, and electrical power can then be produced [33][34][35]. Considering the cut-in wind speed, the start-oscillation condition, and the structural design complexity, VIV and galloping harvesters are pretty efficient in harnessing energy from wind flows [36,37].…”

Section: Introductionmentioning

confidence: 99%

Investigation of hybridized bluff bodies for flow-induced vibration energy harvesting

Wang¹,

Wang²,

Hu³

2022

J. Phys. D: Appl. Phys.

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Small-scale wind energy harvesting based on flow-induced vibration mechanisms has attracted lots of research interest in recent years. Vortex-induced vibration (VIV) and galloping energy harvesters usually outperform each other in different wind speed ranges. To combine the advantages of VIV and galloping harvesters, this paper explored the idea of using a hybridized bluff body constituted of two cylindrical and one cuboid segment for wind energy harvesting. The total length of the hybridized bluff body was fixed. The cuboid segment length was varied to investigate the effect on the flow-induced vibration behavior of the bluff body. The results show that when the cuboid segment length is small, the bluff body exhibits VIV-like behavior in the low wind speed range and galloping-like behavior in the high-speed range. In the medium wind speed range, there appears galloping-VIV coupling. However, if the cuboid segment becomes large, the galloping-VIV coupling phenomenon disappears; the hybridized bluff body behaves just like a cuboid one and only exhibits galloping motion. In addition to experiments, CFD simulations were also conducted to provide more insights into the aerodynamics of the hybridized bluff body. The simulation results reveal that introducing hybridization into the bluff body changes the vorticity flow behind it and the vortex shedding behavior. The vortex shedding effect, in turn, affects the vibration of the bluff body, as well as the performance of the harvester.

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A finite element model to analyze the dynamic characteristics of galloping based piezoelectric energy harvester

Cited by 11 publications

References 23 publications

Energy harvester reliability study by Gaidai reliability method

Energy harvester reliability study by Gaidai reliability method

Uncertainty analysis of galloping based piezoelectric energy harvester system using polynomial neural network

Investigation of hybridized bluff bodies for flow-induced vibration energy harvesting

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