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The meniscal tissue is a layered material with varying properties influencedby collagen content and arrangement. Understanding the relationship betweenstructure and properties is crucial for disease management, treatmentdevelopment, and biomaterial design. The internal layer of the meniscus issofter and more deformable than the outer layers, thanks to interconnectedcollagen channels that guide fluid flow. To investigate these relationships, wepropose a novel approach that combines Computational Fluid Dynamics (CFD) withImage Analysis (CFD-IA). We analyze fluid flow in the internal architecture ofthe human meniscus across a range of inlet velocities (0.1mm/s to 1.6m/s) usinghigh-resolution 3D micro-computed tomography scans. Statistical correlationsare observed between architectural parameters (tortuosity, connectivity,porosity, pore size) and fluid flow parameters (Re number distribution,permeability). Some channels exhibit Re values of 1400 at an inlet velocity of1.6m/s, and a transition from Darcy's regime to a non-Darcian regime occursaround an inlet velocity of 0.02m/s. Location-dependent permeability rangesfrom 20-32 Darcy. Regression modelling reveals a strong correlation betweenfluid velocity and tortuosity at high inlet velocities, as well as with channeldiameter at low inlet velocities. At higher inlet velocities, flow pathsdeviate more from the preferential direction, resulting in a decrease in theconcentration parameter by an average of 0.4. This research provides valuableinsights into the fluid flow behaviour within the meniscus and its structuralinfluences.

On the characteristics of natural hydraulic dampers: An image-based approach to study the fluid flow behaviour inside the human meniscal tissue