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UBC Theses and Dissertations

A simple approach to viscoelastic material selection for impact absorption Kimanzi, Stephen Makau


Engineering materials are broadly classified as elastic or viscous but many materials exhibit a combination of both behaviors. This subgroup of materials is called viscoelastic because it combines the energy absorbing properties of elastic materials and the energy dissipation properties of viscous materials. This combination of properties makes them ideal for many applications including impact absorption. However, the complex and extensive mathematical nature of viscoelastic characterization and modeling has limited the majority of viscoelastic applications to tedious experimental work based on a trial and error. The problem with this approach is that experimental setups are not always available and are often time consuming when multiple material options are available. Therefore, this research takes on the mathematical modeling of viscoelastic materials for impact absorption to create reliable design curves that can readily predict viscoelastic material behavior based on a set of initial conditions. By modeling the impact problem as a second order mass-spring-damper system, this work uses the Kelvin-Voigt viscoelastic model to relate experimentally measurable material parameters to those derived from the impact model. Use is made of the Ashby method to create an impact performance equation with acceleration as the performance metric. A Dynamic Mechanical Analyzer is used to obtain the material parameters for varying frequencies and design curves are generated using these parameters and the Ashby performance metric. Drop tower validation experiments are then conducted to compare the predicted acceleration values based on the impact model to actual impacts. The predicted-to-actual peak acceleration ratio approaches unity with lower peak drop tower strains. The results of this research show that viscoelastic mathematical modeling is not as complicated as once thought and that with a few assumptions, models can produce remarkably accurate results thereby cutting down on exhaustive experimental work.

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