Journal of Materials Science

, Volume 43, Issue 20, pp 6604–6618 | Cite as

Vibration damping in sandwich panels

Stretching the Endurance Boundary of Composite Materials: Pushing the Performance Limit of Composite Structures


Currently, there is incomplete knowledge of the damping level and its sources in satellite structures and a suitable method to model it constitutes a necessary step for reliable dynamic predictions. As a first step of a damping characterization, the damping of honeycomb structural panels, which is identified as a main contributor to global damping, has been considered by ALCATEL SPACE. In this work, the inherent vibration damping mechanism in sandwich panels, including those with both aluminium and carbon fibre-reinforced plastic (CFRP) skins, is considered. It is first shown how the theoretical modal properties of the sandwich panel can be predicted from the stiffness and damping properties of its constituent components using the basic laminate theory, a first-order shear deformation theory and a simple discretization method. Next, a finite-element transcription of this approach is presented. It is shown to what extent this method can be implemented using a finite-element software package to predict the overall damping value of a sandwich honeycomb panel for each specific mode. Few of the many theoretical models used to predict natural frequencies of plates are supported by experimental data and even fewer for damping values. Therefore, in a second, experimental part, the Rayleigh–Ritz method and NASTRAN (finite-element software used by ALCATEL SPACE) predicted modal characteristics (frequency and damping) are compared with the experimentally obtained values for two specimens of typical aluminium core honeycomb panels (aluminium and CFRP skins) used by ALCATEL SPACE as structural panels. It is shown through these results that the method (theoretical and finite element) is satisfactory and promising.


Mode Shape Transverse Shear Sandwich Plate Sandwich Panel Ritz Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Mechanical EngineeringUniversity of BristolBristolUK
  2. 2.Alcatel SpaceEtablissement de CannesCannes La Bocca CedexFrance
  3. 3.University of KermanKermanIran

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