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Smart microswimmers in complex flows

Abstract
Zooplankton are playing a pivotal role in balancing the life on Earth. By grazing on phytoplankton and then serving as food source to larger aquatic animals, they pave the way of redistributing the Sun’s energy and formthe second level of the aquatic food chain. They are also important for the global carbon cycle and, as a result, their effect upon the climate is another reason that highlights their importance. Being small, they experience the flow as viscous. Nevertheless, they are able to each day migrate long distances efficiently. Their daily vertical migration is the largest natural migration of biomass on Earth, which is not well understood. In this thesis,we used a model to analyze the optimal navigation strategies for vertical migration of planktonic microswimmers in turbulent flows. Passive strategies for vertical swimming, such as gyrotaxis, where the swimmer is bottom-heavy and hence obtains a tendency to point upwards, do not have as good performance in turbulent flows as they have in quiescent flows. We present here active mechanisms that a microswimmer, similar to a juvenile copepod, can exploit to significantly increase its vertical migration efficiency in turbulent flows. We find that the modeled swimmer utilizes different mechanisms in two and three spatial dimensions. In two dimensions, they mimic longer swimmers by actively reorienting. This results in an increase in the rate of upwelling region sampling, which leads to a significant increase in swimming speed against gravity. On the other hand, in three dimensions, it turns out that actively keeping the swimming direction aligned against gravity is more efficient. Both mechanisms are found to be robust to moderate perturbations of the flow and swimmer parameters, and they explain how swimmers that do not benefit from passive gyrotaxis can obtain notable vertical migration rates.
University
University of Gothenburg. Faculty of Science
Institution
Institute of Physics
URI
https://hdl.handle.net/2077/71497
Collections
  • Licentiatavhandlingar / Institutionen för fysik
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Lic. Thesis (9.307Mb)
Date
2022
Author
Mousavi, Navid
Keywords
microswimmer
turbulent flow
optimal navigation
diel vertical migration
reinforcement learning
Publication type
licentiate thesis
ISBN
978-91-8009-831-1 (PRINT)
978-91-8009-832-8 (PDF)
Language
eng
Metadata
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