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Ph.D. Thesis Defense Announcement
Pore Space Architecture of Particulate Materials: Characterization and Applications
by
Nimisha Roy
Advisor:
Dr. J. David Frost (CEE)
Committee Members:
Dr. Umit Catalyurek (CSE), Dr. Elizabeth Cherry (CSE), Dr. Mahdi Roozbahani (CSE), Dr. Giaocchino
Viggiani (Univ. Grenoble Alpes)
Date & Time: Monday, November 08, 2021, at 12:00 PM
Location: Sustainable Education Building (SEB), Room 122/ Virtual via Zoom:
https://us02web.zoom.us/j/89856743663?pwd=dFR6UzVVWlRiMDBkZHhzWWdybXZBUT09
ABSTRACT
The behavior of particulate materials is of overarching importance across
multiple science and engineering fields, given its ubiquitous presence in nature. These
materials are typically composed of two phases, solids and voids, and are therefore described as
complex multi-phase materials that exhibit non-linear responses when subjected to varying boundary
conditions. While the attributes of the solid phase of particulate materials have been extensively
characterized both experimentally and numerically, there is much less understanding of the
attributes and behavior of the pore phase. Furthermore, classical pore models incorporate
idealized assumptions of feature geometries, limiting the accuracy of the information that
can be obtained from these features. This study aims to advance digital
characterization capabilities for particulate microstructures, focusing on characterizing
the geometry and topology of the highly complex pore space within packed particle
systems. A new and robust computational algorithm is proposed that quantifies various
characteristics of the three-dimensional pore space of a given particulate media, which is
unimpeded by assumptions of feature shapes or user dependency. The method is validated against
packings of known pore geometries and implemented on real, simulated, and fabricated
microstructures of different packing densities, particle sizes, shapes, gradation, and
following different specimen preparation techniques to measure its ability in capturing multi-scale
responses of microstructures.
The study also leverages the emergence of machine learning techniques to scale up the findings to
real- world field-scale applications comprising particle-pore systems with 10's of millions of
particles. In this regard, the use of deep learning tools for the rapid estimation of
pore space properties from three- dimensional images is sought. Finally, the developed
techniques and tools are implemented on real granular soils to strengthen the
understanding of macro-geomechanical phenomena. The findings highlight the importance of
accounting for pore space properties when interpreting the macroscopic
response of granular assemblies subjected to external mechanical and precipitational loading.
