The meniscal tissue is a layered material with varying properties influenced
by collagen content and arrangement. Understanding the relationship between
structure and properties is crucial for disease management, treatment
development, and biomaterial design. The internal layer of the meniscus is
softer and more deformable than the outer layers, thanks to interconnected
collagen channels that guide fluid flow. To investigate these relationships, we
propose a novel approach that combines Computational Fluid Dynamics (CFD) with
Image Analysis (CFD-IA). We analyze fluid flow in the internal architecture of
the human meniscus across a range of inlet velocities (0.1mm/s to 1.6m/s) using
high-resolution 3D micro-computed tomography scans. Statistical correlations
are 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 of
1.6m/s, and a transition from Darcy's regime to a non-Darcian regime occurs
around an inlet velocity of 0.02m/s. Location-dependent permeability ranges
from 20-32 Darcy. Regression modelling reveals a strong correlation between
fluid velocity and tortuosity at high inlet velocities, as well as with channel
diameter at low inlet velocities. At higher inlet velocities, flow paths
deviate more from the preferential direction, resulting in a decrease in the
concentration parameter by an average of 0.4. This research provides valuable
insights into the fluid flow behaviour within the meniscus and its structural
influences.

研究 meniscus 内的液流行为及其结构影响的方法，将计算流体力学与图像分析相结合，通过高分辨率的三维微型 CT 扫描，在一定范围的入口速度下分析人类 meniscus 内部结构的液流。