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University of Minnesota

Net Flow Through Soft Porous Media Generated by a Periodic Mean-Zero Pressure Gradient

Abstract

dc:description.abstract

In this thesis, a soft porous media experiment is developed to investigate the effect of applying periodic mean-zero pressure gradients to a soft porous medium. These periodic mean-zero pressure gradients are composed of square waves, with a high magnitude positive pressure drop applied for 1/3 of the period and a negative pressure drop with half the magnitude applied for 2/3 of the period. We investigate the effect of varying the pressure magnitude and period. This setup uses a novel tracking method to quantify the porous media and fluid velocities simultaneously, and operates with high levels of accuracy and repeatability. The periodic mean-zero pressure gradients we investigate are shown to generate substantial net flow, in the direction associated with the smaller pressure magnitude portion of the period. The system is also shown to demonstrate hysteretic behavior, where complex packing features form and persist in the solid phase, influencing the results of the system for subsequent experiments. Results from this experiment have important implications for fluid flow in biological tissues, such as interstitial transport in the brain and body, as it demonstrates that small scale, periodic mean-zero pressure gradients can drive significant amounts of net transport through a deformable porous medium.

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Stein, Jacob

Subjects

dc:subject × 4

Rights

Language dc:language.iso
en

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/11299/256984
OAI identifier oai:identifier
oai:conservancy.umn.edu:11299/256984

Chain of custody

source
Harvested from
University of Minnesota
Base URL
conservancy.umn.edu/server/oai/request
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Stein, Jacob. Net Flow Through Soft Porous Media Generated by a Periodic Mean-Zero Pressure Gradient. 2023. https://hdl.handle.net/11299/256984