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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Machine augmented composite materials for damping purposes

McCutcheon, David Matthew 17 February 2005 (has links)
In this study the energy dissipation performance of machine augmented composite (MAC) materials is investigated. MAC materials are formed by inserting simple machines into a matrix material. In this work the machines take the form of fluid filled tubes, and the tube cross-sectional geometry induces fluid flow when it is deformed in its plane. This flow dissipates mechanical energy, and thus provides the composite material with attractive damping properties. The objective of this study is to gain insight into the geometry, the material property combinations, and the boundary conditions that are effective in producing high damping MAC materials. Particular attention is given to tube geometry and to dimensionless parameters that govern the energy dissipation efficiency of a MAC lamina. An important dimensionless parameter is the ratio of solid elastic moduli to the product of the driving frequency and the fluid dynamic viscosity. This is a measure of the ratio of elastic forces in the solid material to the viscous forces in the fluid material that makes up a MAC lamina. Governing equations and simulation methods are discussed. Simplified equations are derived to predict the pressure generated when a tube/matrix cell is squeezed with zero pressure end conditions. Transient, three dimensional finite element models are also used to predict the performance of the damping MAC materials with zero pressure at the ends of the tubes. For the geometry and material properties considered, the highest energy dissipation efficiency predicted by these models is approximately 0.8 out of a maximum of 1.0.

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