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Title: Computational modelling of fluid load support in articular cartilage
Author: An, Shuqiang
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2013
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Natural articular cartilage is known to be an excellent bearing material with very low friction coefficient and wear rate. Theoretical and experimental studies have demonstrated that the interstitial fluid of cartilage is pressurized considerably under loading to support the applied load, leading to the low friction coefficient. In this process, collagen fibrils play a vital role to resist the lateral expansion of cartilage and enhance the pressurization. The proportion of the total load supported by fluid pressurization in cartilage, called the fluid load support, is therefore an important parameter in biotribology and determined as the main aspect of this thesis. Fibril-reinforced cartilage models were set up to account for the high pressurization of interstitial fluid. However, the orientation of collagen fibrils were idealized or simplified; and the models implementing realistic fibril orientation derived from DT-MRI data did not include viscous effects in both the solid matrix and the fibril. This study overcome the limitation of previous models and the major finding were: • The peak value of fluid load support in both 2D and 3D fibrilreinforced models implementing DT-MRI data was increased to greater than 90% from around 60% in the isotropic poroelastic model, due to the reinforcement by collagen fibril. • The implementation of the realistic fibril orientation in the 3D fibrilreinforced model increased the value of peak pore pressure (by 15%) compared to the uniformly reinforced model while the peak contact pressure was 4.3% lower, making the peak value of fluid load support increase from 80.4% to 96.7%. • The rationality to define the principal eigenvector as orientation of the corresponding primary collagen fibril in the fibril-reinforced models was verified by the related results. • The feasibility and reliability of the methodologies to implement DTMRI data to the fibril-reinforced models were both confirmed in the modelling process.
Supervisor: Fisher, John ; Jin, Zhongmin ; Jones, Alison Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available