Ke Qin

Date of Award


Document Type

Thesis - SCU Access Only


Santa Clara : Santa Clara University, 2022.

Degree Name

Master of Science (MS)


Mechanical Engineering

First Advisor

On Shun Pak


In this thesis, we focus on two problems relevant to the swimming of slender bodies at low Reynolds numbers (Re). In the first problem, we study the effect of shear-thinning rheology on the elastohydrodynamic swimming of a slender swimmer. Specifically, we examine how shear-thinning rheology alters the fluid- structure interaction and hence the propulsion performance of elastic swimmers at low Re. Via a simple elastic swimmer actuated magnetically, we demonstrate that shear-thinning rheology can either enhance or hinder elastohydrodynamic propulsion, depending on the intricate interplay between elastic and viscous forces as well as the magnetic actuation. We also use a reduced-order model to elucidate the mechanisms underlying the enhanced and hindered propulsion observed in different physical regimes. These results and improved understanding could guide the design of flexible micro-swimmers in both Newtonian and shear-thinning fluids.

In the second problem, we explore the use of machine learning to identify effective swimming strategies of a slender swimmer at low Re. The use of machine learning techniques in designing microscopic swimmers has drawn considerable attention in recent years. Here we apply a reinforcement learning approach to identify swimming gaits of a multi-link model swimmer. Without relying on any prior knowledge of low-Reynolds-number locomotion, we first demonstrate the use of reinforcement learning in identifying the classical swimming gaits of Purcell's swimmer for case of three links. We next examine the new swimming gaits acquired by the learning process as the number of links increases. We also consider the scenarios when only a single hinge is allowed to rotate at a time and when simultaneous rotation of multiple hinges is allowed. We contrast the difference in the locomotory gaits learned by the swimmers in these scenarios and discuss their propulsion performance. Our results demonstrate reinforcement learning as a viable tool for the design of locomotory gaits at low Reynolds numbers. We also foresee the promising use of this approach in designing micro-swimmers in shear-thinning fluids and other complex fluids.

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