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Biomechanical Analysis of Ramming Behavior in Ovis Canadensis

  • Parimal MaityEmail author
  • Srinivasan Arjun Tekalur
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

Horns have been hypothesized to help in absorbing shock and protecting the brain during ramming events. I general, horn is made of a a-keratin sheath covering a porous bone. The objective of the present study is to investigate the shock-absorptive role of the keratinous sheath and bony core of horns; particularly in Bighorn Sheep (Ovis Canadensis). A three dimensional complex structure of the bighorn sheep horn was successfully constructed and modeled using a computed tomography (CT) scan and Finite Element (FE) method, respectively. Computed tomography was also used to identify the porosity in the inner core of the horn. The horn was subjected to quasi-static loading of 3400 N (764.35 lbf) simulating the effect of both the composite nature of the horn and the porosity in the inner core. Three different 3-dimensional quasi-static analyses, a part of simplified homogenous horn, simplified composite horn and complex structure of horn were studied. The computed stresses, deflections and strain energy were compared for three different models. It was noticed that strain energy due to elastic deformation of the complex horn structure was more, as compared to simplified horn structure and composite horn structure models; whereas the ability of force transmission was found to be more in composite and complex structures of horn. This phenomenon was elucidated through the stress distribution in structure. This study will help designers in choosing appropriate material combination for successful design of protective structures against a similar impact.

Keywords

Inner Core Frontal Sinus Maximum Principal Stress Spongy Bone Minimum Principal Stress 
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 2011

Authors and Affiliations

  1. 1.Composite Vehicle Research Center, Department of Mechanical EngineeringMichigan State UniversityLansingUSA

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