JOURNAL ARTICLE
Size-Dependent Frequency Analysis of Higher-Order Microplates with FGP Core and Polymeric CNTRC Faces Considering Piezoelectricity.
Published In: International Journal of Structural Stability & Dynamics, 2024, v. 24, n. 15. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Wang, Xiaonan; Kumar, Abhinav 3 of 3
Abstract
The present study examines a microplate with a porous structure and two nanocomposite piezoelectric layers. All the layers' properties are graded functionally, bonded to each other, and supported by an elastic foundation that can withstand both normal and shear loads. Additionally, carbon nanotubes are used to increase the electro-mechanical performance of the piezoelectric patches, which are exposed to an externally applied electric voltage. Using a higher-order trigonometric shear deformation theory and von Karman's assumptions, the kinematic relations are demonstrated. The governing motion equations are derived using Hamilton's principle and variational technique, and the modified couple stress theory is employed to take the scale effect into account. An analytical method based on Fourier series functions is used to solve the differential motion equations, and the impact of diverse factors such as porosity percentage, pore distribution patterns, carbon nanotubes distribution patterns, and other key parameters on the normalized frequencies of the model is analyzed after verifying the accuracy of the results. The findings of this research may aid in the development and production of smart structures and devices with increased efficiency. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Structural Stability & Dynamics. 2024/08, Vol. 24, Issue 15, p1
- Document Type:Article
- Subject Area:Film
- Publication Date:2024
- ISSN:0219-4554
- DOI:10.1142/S0219455424501694
- Accession Number:178853729
- Copyright Statement:Copyright of International Journal of Structural Stability & Dynamics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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