JOURNAL ARTICLE

Moving–Load Dynamic Analysis of Thick Sandwich Plates with Titanium Alloy Face Sheets and a Porosity–Dependent FG–GPLRC Core.

  • Published In: International Journal of Structural Stability & Dynamics, 2026, v. 26, n. 11. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wang, Xia; Li, Zhanjun 3 of 3

Abstract

An analysis is done in this research on the dynamical behavior of thick sandwich plates subjected to a moving load. The considered sandwich plate is composed of a porous aluminum core augmented with graphene platelets (GPLs) and titanium alloy face sheets. The core of sandwich plate is identified as a porosity-dependent composite and consists of six layers with each of them having different values of porosity. The time-dependent equations of motion are established for the sandwich plate by implementing the principle of virtual work. The governing equations are numerically solved for different boundary conditions utilizing the Ritz technique. The Newmark time marching scheme is also applied to obtain the temporal evaluation of displacement field in the plate volume. The verification example demonstrates the effectiveness and accuracy of the applied formulation. Novel numerical examples are presented to study the influences of porosity distribution pattern and its coefficient on the dynamic characteristics of the sandwich plate. Also, the effects of graphene's weight fraction, plate's geometric parameters and its boundary conditions are examined. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Structural Stability & Dynamics. 2026/05, Vol. 26, Issue 11, p1
  • Document Type:Article
  • Subject Area:Geology
  • Publication Date:2026
  • ISSN:0219-4554
  • DOI:10.1142/S0219455426500823
  • Accession Number:192347538
  • 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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