Effect of shape and particle coordination on collective dynamics of granular matter
Savoie, William Carey
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Particle shape and coordination in granular materials (GM) can impact the material properties of an aggregate. GM are generally described as collections of particles which are macroscopic, discrete, a-thermal, and dissipative. While seemingly simple to understand the dynamics of systems containing only a handful of particles, larger aggregations can have complex and emergent dynamics. Materials with dynamic properties can be made if the constituent particles are capable of altering their shape and coordination. In this thesis, to elucidate principles in GM systems, we study systems both biological and robotic where particle shape and coordination on the single particle scale alters macroscopic system dynamics. We start by studying how grain shape affects reversibility for a robotic swimmer performing reciprocal strokes in GM. We find that although displacement on initial strokes for a such a swimmer are affected by the initial compaction of a GM, a bifurcation in the displacement response leads to a final displacement and eventual asymptotic reversibility independent of initial compaction. We then shift to the study of active particles, with a special focus on a robotic system featured heavily in this dissertation: smarticles. Smarticles are a shape changing 3-link robot designed in our lab. We start by examining single smarticle crawling dynamics. We apply geometric mechanics, a theoretical framework which relates body speed and shape speed for swimmers in viscous-like environments and discover it can be used for this hybrid dynamical system. Another project examines how shape change can be used as a rudimentary control scheme for swarm robotic systems without a centralized control or communication. In our final smarticle system, we reveal how material properties of a collective can be transformed by modifying entanglements between particles. Lastly, inspired by biophysical studies of ants, we use simulation to gain insights into traffic flow for densely packed, confined, task-oriented, active GM systems. Our results reveal features necessary to avoid traffic jams when faced with certain constraints such as confined conditions, minimal communication, and an over-sized workforce.