Date of Award

2025-08-01

Degree Name

Master of Science

Department

Mechanical Engineering

Advisor(s)

Zhengtao Z. Gan

Abstract

Understanding the directional properties of porous media is essential for accurately predicting flow behavior, reactive transport, and fluid-solid interactions in systems ranging from geothermal reservoirs to energy storage devices and biological tissues. Directional variations in permeability - reflecting a medium's response to flow at different angular orientations - are particularly important for complex, inherently anisotropic geometries. In this study, we employ a Lattice Boltzmann (LBM) model to calculate directional permeabilities from porous media images subjected to varying flow inlet angles. Three classes of porous media were investigated: (1) synthetic media with circular grains, serving as isotropic baselines; (2) synthetic media with elliptical grains to introduce controlled anisotropy; and (3) micro-CT images of sandstone, characterized by naturally irregular grain distributions. For the synthetic cases, key geometric parameters were varied to diversify the medium structures. Using our LBM flow simulations, permeability was evaluated at 10° intervals across 360°, yielding 36 data points per sample. This systematic approach produced a comprehensive dataset capturing unique functional relationships between flow angle and permeability for each media class. Our primary goal is to analyze the anisotropy of these porous structures. While linear transformations of principal permeabilities can predict directional permeability in isotropic media, their applicability to anisotropic cases remains underexplored. We further aim to train machine learning models to predict permeability as a function of inlet flow angle given an image of the porous medium, and to compare model performance across the three classes of obstruction patterns. These findings deepen our understanding of how geometric anisotropy influences directional transport in porous media and pave the way for more accurate predictive models. Such advances have broad implications for filtration, energy storage, and subsurface fluid flow applications.

Language

en

Provenance

Received from ProQuest

File Size

88 p.

File Format

application/pdf

Rights Holder

Soumya Shouvik Bhattacharjee

Download

Share

COinS