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A Comparative Study on the Optimal Modeling of Laminated Glass

Author(s): ORCID

Medium: journal article
Language(s): English
Published in: Civil Engineering Journal, , n. 11, v. 9
Page(s): 2896-2913
DOI: 10.28991/cej-2023-09-11-018
Abstract:

This study addresses the challenging task of modeling laminated glass responses to extreme loading scenarios for the design and analysis of protective structures. The primary objective is to seek an optimal modeling approach that balances accuracy and computational efficiency. To achieve this, the failure modeling of laminated glass layups comprising thin and thick panels with three and eleven layers is investigated under blast loading conditions. Various simulation techniques are employed, including the finite element method (FEM) with element erosion/deletion, smoothed particle hydrodynamics (SPH), and a hybrid approach involving the conversion of elements into particles. The feasibility and limitations of each technique are examined, considering both accuracy and computational cost. Experimental results from arena and shock tube testing scenarios assess the deployed modeling techniques and the presented comparisons. Emphasis is placed on mesh sensitivity and the significance of adaptive meshing in capturing fracture patterns. The present paper suggests that utilizing hybrid techniques results in optimal modeling outcomes. Furthermore, the stability of the modeling results under diverse blast conditions is confirmed. This article contributes to the field by offering insights into modeling laminated glass responses to extreme loading, emphasizing the use of hybrid techniques to strike a balance between accuracy and computational efficiency. This research enhances the understanding of protective structure design and analysis, highlighting the critical importance of computational methods in this context. Doi: 10.28991/CEJ-2023-09-11-018 Full Text: PDF

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.28991/cej-2023-09-11-018.
  • About this
    data sheet
  • Reference-ID
    10756584
  • Published on:
    08/01/2024
  • Last updated on:
    08/01/2024
 
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