MEASURING GEOMETRICAL TORTUOSITY OF POROUS MEDIA FROM 3D COMPUTED TOMOGRAPHY IMAGES
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Tortuosity is an important parameter that has a significant impact on many environmental processes and applications. Flow in porous media, diffusion of gases in complex pore structures, and transmembrane flux in water desalination are examples of the application of the micro-scale parameter. The main objectives of this thesis are to develop functional relationships that relate tortuosity to geometrical and topological parameters of porous media using three-dimensional (3D) computed tomography images, and select the best model that has the best capability to predict geometrical tortuosity. The objectives were achieved by implementing Random Paths MATLAB code that was developed in this work and compared with available Tort3D MATLAB code using high resolution 3D synchrotron computed tomography images of representative porous media. Tortuosity factors were computed from random tortuous paths of connected voxels (Random Paths Code) and tortuous paths derived from 3D medial surface of void space (Tort3D Code). Tortuosity factors were related to geometrical and topological parameters including porosity (∅), median grain diameter (d50), uniformity coefficient (Cu), coefficient of gradation (Cc), sphericity index (Si), roundness index (Ri), and specific surface area (SSA). Tort3D code was validated by comparing measured with predicted tortuosity factors from models reported in the literature. The two codes measured geometrical tortuosity of different sand systems effectively. However, they provided different tortuosity values, since they were developed using different concepts. Models were developed and predicted tortuosity values were compared with measured tortuosity values. Good agreement was found between predicted and measured tortuosity values with low error (less than 20%). Model 3 that considers ∅, d50, Cu, and Cc has best capability to predict tortuosity compared with other developed models.
- Environmental Engineering Master Program [15 items ]