Elbow Joint Contact Mechanics: Multibody and Finite Element Methods

Only a few millimeter thick articular cartilage is a very specialized connective tissue which withstands high compressive and shear forces while protecting the bone from excessive loading, and provides a smooth articulation for the joint. Better understanding of elbow cartilage contact mechanics can provide a valuable insight into cartilage degeneration mechanisms and osteoarthritis development. Computational modeling is a very efficient tool that helps us gain better understanding of joint biomechanics, particularly elbow joint contact mechanics. This tool can predict parameters that are not feasible to measure experimentally, decrease the cost of physical experiment, help develop better rehabilitation and surgical protocols, and finally improve patient care. The objectives of the study presented here were first, to develop subject specific finite element (FE) models of the isolated ulno-humeral joint of the elbow and validate these models against experiment measurements. Second, to develop multibody (MB) models of the same joints with the humerus cartilage represented with discrete rigid bodies interacting with the ulna cartilage with deformable contacts. Third, to optimize the deformable contact parameters used in the MB models to validated FE models and assess the effect of grid sizes on the contact predictions. These models allow for the prediction of cartilage contact characteristics including maximum and average contact pressure (MPa), and contact area (mm2) under different loading conditions and during activities in the anatomic elbow joint. Finally, the results from optimization indicated that the selection of contact parameters is very critical for accurate prediction of contact mechanics within the MB models of ulno humeral joints.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Mechanical Engineering (UMKC)

Grantor dc:publisher

University of Missouri--Kansas City

Year dc:date.issued

2017

Author dc:creator

• Sharifi Renani, Mohsen

Advisor dc:contributor.advisor

• Stylianou, Antonis P.

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/10355/60584

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/60584