Microswimmer Navigation in Turbulence

Abstract

This thesis provides a summary of our exploration of navigation strategies for microswimmers with limited control and local sensing capabilities, drawing inspiration from copepods. Our research concentrates on two critical biologically inspired tasks essential for the survival of planktonic swimmers. First, we delve into the optimal vertical navigation for these swimmers. Many planktonic swimmers perform daily and seasonal vertical migrations quite efficiently in the turbulent pelagic environment. Given their abilities to navigate, we uncover active reorientation strategies that enable such microswimmers to double their vertical migration compared to those relying only on passive strategies such as gravitaxis. Moreover, we demonstrate the potential for these swimmers to translate even faster than their propulsion speed by leveraging the background flow. Second, we investigate optimal navigation strategies to avoid high strain rates in turbulent flows. This is important since the most crucial source of information for these species to survive is the flow disturbances. Here, we uncover intriguing phenomena such as emergent counter-current swimming behavior, which allows the swimmers to persist in low-strain regions of complex turbulent flow fields for extended periods. Our findings contribute to a deeper understanding of planktonic microswimmers; organisms that play vital roles in sustaining life on Earth by regulating climate, participating in the carbon cycle, influencing oceanic albedo, and serving as the foundation of aquatic food webs. Moreover, they have implications for developing optimal autonomous navigation policies for biologically inspired micro and nanorobots, with applications such as directed drug delivery. Given the interdisciplinary relevance of our results, the thesis includes a comprehensive introduction providing the necessary background knowledge for a deeper understanding of the appended papers.

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Keywords

microswimmer, turbulent flow, optimla navigation, reinforcement learning

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