Effect of Poling Orientation in Performance of Piezoelectric Materials

Adhikari, Jitendra; Kumar, Rajeev; Narain, Vikas; Jain, S. C.

doi:10.1007/978-981-16-0550-5_166

Cited by 2 publications

(2 citation statements)

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“…By utilizing nodal coordinates and thickness, any arbitrary position within the structure can be represented as ( Figure a). [ 34 ]

\begin{equation}\left\{ { \def\eqcellsep{&}\begin{array}{@{}*{1}{c}@{}} x\\ y\\ z \end{array} } \right\} = \sum_{i = 1}^{nel} {{N_i}\left\{ {\left\{ { \def\eqcellsep{&}\begin{array}{@{}*{1}{c}@{}} {{x_i}}\\ {{y_i}}\\ {{z_i}} \end{array} } \right\} + \frac{1}{2}{t_e}{h_i}\left\{ { \def\eqcellsep{&}\begin{array}{@{}*{1}{c}@{}} {{l_{3i}}}\\ {{m_{3i}}}\\ {{n_{3i}}} \end{array} } \right\}} \right\}} \end{equation}

…”

Section: Finite Element Analysis Of Piezoelectric Tilementioning

confidence: 99%

“…By utilizing nodal coordinates and thickness, any arbitrary position within the structure can be represented as (Figure 3a). [34] Where N i is the shape function, nel is the number of nodes per element, h i is the nodal thickness, and t e is the natural coordinate along thickness direction. The mid-surface of node i has x i , y i and z i as the global coordinates, and l 3i , m 3i and n 3i are direction cosines of unit vector V 3i.…”

Section: Geometry and Displacement Fieldmentioning

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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“…By utilizing nodal coordinates and thickness, any arbitrary position within the structure can be represented as ( Figure a). [ 34 ]