This thesis sets out to uncover the unknown mechanisms that control the complex, heterogeneous function of the meniscus. This has been accomplished through the use of modern pore network extraction (PNE) techniques to characterise the pore space within the meniscus, a soft tissue situated inside the knee which is fundamental for the correct functioning of the joint. While these PNE techniques were not primarily designed for use in high-porosity materials, this project demonstrates that they can be adapted for use in complex biological materials with the inclusion of bespoke adaptations developed in this study. Current literature has highlighted notable variance between results depending on the method selected to segment the pore space. To ensure a fully comprehensive analysis, a selection of three segmentation methods were assessed for comparison. This project found that the choice of the segmentation method does in fact have a notable impact on the characterisation of the pore space and the decision of which method to implement should be dependent on the goal and scope of the study. Pore network modelling (PNM) was initially designed not only for the characterisation of the pore space, but for the prediction of transport properties. As the current literature shows significant inconsistencies in the results of fluid properties, such as permeability, it was decided to employ more stable and trustworthy methods, namely, tortuosity quantification and CFD analysis. Again, to ensure a comprehensive analysis of parameters, three different methods of determining geometric tortuosity have been implemented in this project. These methods each require varying amounts of information from the pore space to function which, as such, affect memory usage, run-time and tortuosity values. The tortuosity analysis demonstrated that the structure of the meniscus creates twofold anisotropy, both in the orthogonal direction and along the Z-Direction. This anisotropic effect was confirmed by the novel approach of coupling computational fluid dynamics (CFD) methods with modern image analysis techniques (CFD-IA). The CFD-IA analysis demonstrated that in CFD simulations, fluid paths orientation between 270-330$\degree$ along the Z-direction had lower tortuosities than others. This CFD-IA also demonstrated the ability to highlight the potential limitations of PNM and statistically quantify the relationship between architectural parameters and fluid velocity and Reynolds Number.
Permanent link to this resource: https://doi.org/10.24384/ct82-xm70
Waghorne, Jack
Supervisors: Olde Scheper, Tjeerd; Barrera, Olga
Faculty of Technology, Design and EnvironmentSchool of Engineering, Computing and Mathematics
Year: 2023
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