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
Collections
View/ Open
Date
2022Author
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