The optimal design of a piezoelectric energy harvester for smart pavements

Huang, Kangxu; Zhang, He; Jiang, Jiqing; Zhang, Yangyang; Zhou, Yuhui; Sun, Liangfeng; Zhang, Yinnan

doi:10.1016/j.ijmecsci.2022.107609

Cited by 20 publications

(4 citation statements)

References 60 publications

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“…PZT and its various structural forms have been shown in several investigations to possess higher piezoelectric characteristics under human impact loads compared to other kinds of piezoelectric ceramics. [28] In practical applications, the number of embedded piezoelectric devices should be increased in order to collect the mechanical energy generated by thousands of human steps, which can provide significant energy for lowpower electronics, self-powered sensors, and remote electrical equipment in transportation infrastructures. This study also examines the impact of mountainous and flat terrains on the performance of piezoelectric tiles.…”

Section: Methodsmentioning

confidence: 99%

“…The loading mode was governed by a sinusoidal wavefunction, which has been shown to be the most accurate form for human loading mode. [28] The piezoelectric tile has a piezoelectric patch adhered in the center of an aluminum (150 mm × 150 mm × 1 mm) or copper (150 mm × 150 mm × 0.5 mm) substrate (Figure 1d-f). The piezoelectric patch was comprised of PZT-5J and had dimensions of 50 mm × 20 mm × 0.4 mm.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Piezoelectric Tile Performance in Flat and Mountainous Terrains Through Deep Neural Network

Adhikari,

Singh,

Sagar

et al. 2023

Advcd Theory and Sims

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Piezoelectric tiles harvest mechanical vibrations and convert them into electrical energy, making them an attractive energy‐harvesting technology. However, their performance is heavily influenced by the terrain where they are installed. Traditional experimental methods for predicting their performance on different terrains are time‐consuming, so a computational approach is necessary to improve efficiency. To address this, a machine learning‐based approach is proposed using Artificial Neural network (ANN) and Deep neural network (DNN) with the Tanh activation function to predict piezoelectric tile performance in diverse terrains. The models are trained on an experimental dataset consisting of four terrains, including Flat terrain (FT) and Hilly Terrain (HT) 1, 2, 3 with road angles of 0, 3, 6, and 10 degrees. A finite element model is also established to optimize the piezoelectric tile and estimate the suitable parameter range to prevent damage to the tile during experiments. The results indicate that the DNN model performs better than the ANN model, achieving high accuracy in predicting piezoelectric tile performance on different terrains. These findings suggest that machine learning can provide a time and cost‐effective way for predicting the performance of piezoelectric tiles in varied terrains, thereby facilitating betters installation and maintenance decisions for piezoelectric tile systems.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prediction of Piezoelectric Tile Performance in Flat and Mountainous Terrains Through Deep Neural Network

Adhikari,

Singh,

Sagar

et al. 2023

Advcd Theory and Sims

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“…A multitude of endeavors have been undertaken to harvest energy and facilitate the monitoring of structural integrity to overcome this specific challenge. These endeavors encompass several technologies, including thermoelectric technology, [ 24 , 25 , 26 , 27 ] magnetic coupling, [ 28 , 29 , 30 ] piezoelectric nanogenerators (PENG), [ 31 , 32 , 33 ] and TENG. [ 34 , 35 , 36 , 37 ] Due to the instability of thermoelectric technology, [ 38 ] the size of magnetic coupling [ 39 ] and the small signal of PENG, [ 40 ] TENG emerges as a highly auspicious novel technology employed in the realm of digital twins inside the domain of civil engineering infrastructure 4.0.…”

Section: Introductionmentioning

confidence: 99%

Triboelectric Nanogenerator‐Enabled Digital Twins in Civil Engineering Infrastructure 4.0: A Comprehensive Review

Pang,

He,

Liu

et al. 2024

Advanced Science

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The emergence of digital twins has ushered in a new era in civil engineering with a focus on achieving sustainable energy supply, real‐time sensing, and rapid warning systems. These key development goals mean the arrival of Civil Engineering 4.0.The advent of triboelectric nanogenerators (TENGs) demonstrates the feasibility of energy harvesting and self‐powered sensing. This review aims to provide a comprehensive analysis of the fundamental elements comprising civil infrastructure, encompassing various structures such as buildings, pavements, rail tracks, bridges, tunnels, and ports. First, an elaboration is provided on smart engineering structures with digital twins. Following that, the paper examines the impact of using TENG‐enabled strategies on smart civil infrastructure through the integration of materials and structures. The various infrastructures provided by TENGs have been analyzed to identify the key research interest. These areas encompass a wide range of civil infrastructure characteristics, including safety, efficiency, energy conservation, and other related themes. The challenges and future perspectives of TENG‐enabled smart civil infrastructure are briefly discussed in the final section. In conclusion, it is conceivable that in the near future, there will be a proliferation of smart civil infrastructure accompanied by sustainable and comprehensive smart services.

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“…The harvested energy can be employed to power LED lights and charge mobile devices. The study conducted by [22] developed a piezoelectric energy harvester for smart pavements. The proposed system can power low-power electronics, self-powered sensors, and remote electrical equipment in transportation infrastructures.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Analysis and Piezoelectric Energy Harvesting from a Nonideal Portal Frame System including Nonlinear Energy Sink Effect

et al. 2023

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This paper investigates, through numerical simulations, the application of piezoelectric materials in energy generation. The mathematical model describes a U-shaped portal frame system, excited by an engine with unbalanced mass and coupled to a nonlinear energy sink (NES), which is used as a passive vibration absorber. The influence of the piezoelectric material parameters used in the energy collection and the dimensioning parameters of the NES system is deeply analyzed in this paper. Numerical simulations are presented considering all combinations of the parameters of the piezoelectric material model and the NES. The system dynamics were analyzed through phase diagrams and the 0–1 test. The estimation of energy collection was carried out by calculating the average power. The numerical results show that a more significant potential for energy generation is obtained for certain combinations of parameters, as well as chaotic behavior in some cases.

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The optimal design of a piezoelectric energy harvester for smart pavements

Cited by 20 publications

References 60 publications

Prediction of Piezoelectric Tile Performance in Flat and Mountainous Terrains Through Deep Neural Network

Prediction of Piezoelectric Tile Performance in Flat and Mountainous Terrains Through Deep Neural Network

Triboelectric Nanogenerator‐Enabled Digital Twins in Civil Engineering Infrastructure 4.0: A Comprehensive Review

Dynamic Analysis and Piezoelectric Energy Harvesting from a Nonideal Portal Frame System including Nonlinear Energy Sink Effect

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