Finite Element Model for Damping Optimization of Viscoelastic Sandwich Plate Structures

Author(s):  
J.S. Moita ◽  
A.L. Araújo ◽  
C.M. Mota Soares ◽  
C.A. Mota Soares
2011 ◽  
Vol 105-107 ◽  
pp. 1768-1772 ◽  
Author(s):  
Mohammad Mahdi Kheirikhah ◽  
Seyyed Mohammad Reza Khalili ◽  
Keramat Malekzadeh Fard

In the present paper, an accurate 3D finite element model is presented for bucking analysis of soft-core rectangular sandwich plates. The sandwich plate is composed of three layers: top and bottom skins and core layer. Finite element model of the problem has been constructed in the ANSYS 11.0 standard code area. The effect of geometrical parameters of the sandwich plate is studied. Comparison of the present results with those of plate theories confirms the accuracy of the proposed model. The overall buckling loads calculated by FE model are higher than that of the accurate results and the maximum discrepancy is less than 10 percent.


2011 ◽  
Vol 18 (2) ◽  
pp. 159-169 ◽  
Author(s):  
José S. Moita ◽  
Aurélio L. Araújo ◽  
Pedro G. Martins ◽  
Cristóvão M. Mota Soares ◽  
Carlos A. Mota Soares

2021 ◽  
pp. 095605992110016
Author(s):  
Ali Aborehab ◽  
Mohamed Kamel ◽  
Ahmed Farid Nemnem ◽  
Mohammed Kassem

The honeycomb sandwich structures have a crucial participation in aerospace industry, especially in the design of satellite structures due to their exceptional mechanical properties. The equivalent finite element modeling of such structures is initially presented through the implementation of modal analysis via the three-layered sandwich theory. Subsequently, the computational results are validated by carrying out an experimental modal testing. In addition, sensitivity analysis based upon design of experiments and parameters correlation, is executed for the sake of selecting the most appropriate design parameters for the optimization problem. Finally, finite element model updating of a honeycomb sandwich plate is thoroughly introduced using three optimization algorithms including genetic algorithms, adaptive-multiple optimization, and response surface method. A good agreement between the previously-mentioned optimization algorithms is obtained. Meanwhile, response surface method and its related design of experiments tool succeed in avoiding such time-consuming process and reduce the involved computational expense with an acceptable accuracy.


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