Dynamic stability analysis of inclined galloping based piezoelectric energy harvester employing finite element method

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

doi:10.1080/15397734.2021.1968898

Cited by 8 publications

(3 citation statements)

References 26 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%

“…Yang et al (2019) and Wang et al (2020) have investigated GPEH nonlinear response, utilizing device's bi-and tristable properties. For EH vortex-induced vibration EH in random flow fields, see Adhikari et al (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%

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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%

Section: Introductionmentioning

confidence: 99%

Energy harvester reliability study by Gaidai reliability method

Gaidai,

Sun,

Wang

2024

Climate Resilience

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“…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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Artificial Neural Network-Based Model for Galloping-Based Piezoelectric Energy Harvester

Dash

2024

Lecture Notes in Mechanical Engineering

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Dynamic stability analysis of inclined galloping based piezoelectric energy harvester employing finite element method

Cited by 8 publications

References 26 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

Artificial Neural Network-Based Model for Galloping-Based Piezoelectric Energy Harvester

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