Fluid-structure interactions in low-Reynolds-number flows: patterned surfaces and elastic boundaries

Abstract

This thesis explores two systems related to fluid-structure interactions in low-Reynolds-number flows. We approach the questions through a combination of simplified laboratory scale experiments and analytical models, dimensional analysis, and scaling arguments. First, we consider viscous peeling of an elastic sheet at a permeable interface. We design an experiment where an elastic block is patterned on one side with micropillars, which is in contact with a rigid, adhesive substrate. Fluid is injected into the permeable layer, first spreading into the pores, and, after a critical pressure is reached, the pillars are peeled from the substrate, forming a fluid-filled blister. With laboratory experiments, we study the formation and propagation of this fluid-filled blister. We measure the critical pressure required to initiate peeling and the fraction of injected fluid stored in the porous layer compared to the blister for varying experimental conditions. We investigate the time evolution of the shape of the fluid-filled cavity and compare the dynamics with existing scaling laws for the growth of a blister at an impermeable interface. After the blister is formed and injection is stopped, the pressurized fluid can drain into the porous surroundings. We develop a model for the bending and tension-driven relaxation of a blister on a porous substrate. The second topic of this thesis the motion of particles nearby patterned surfaces. First, we look at the sedimentation of a particle near a wall patterned with corrugations which are tilted with respect to gravity. In this configuration, we observe a helical particle trajectory and a drift of the particle along the surface corrugations in the transverse direction. We find an optimal drift and identify a universal transport behavior. Furthering this work, we study pressure-driven flow in a microchannel with a corrugated wall. Measuring the flow field using particle image velocimetry and 3d single particle tracking, we observe helical streamlines nearby the corrugated surface. Finally, we extend the previous idea to look at the inverse problem of identifying surface topography via flow-field measurement. We show that a U-Net, a type of convolutional neural network, can be used to infer the surface structure via flow-field measurements.