Advancing the theory and applications of Lagrangian Coherent Structures methods for oceanic surface flows

Margaux Filippi, Sc.D., 2019
Irina Rypina, Co-advisor
Thomas Peacock, Co-advisor

Ocean surface transport is at the core of many environmental disasters, including plastic pollution or oil spills. Understanding and predicting flow transport, however, remains a scientific challenge, because it operates on multiple length- and time-scales set by the underlying dynamics. This thesis investigates the present-day abilities to describe and understand the organization of flow transport at the ocean surface, including the abilities to detect the underlying key structures, the regions of stirring and regions of coherence within the flow. The field of dynamical system theory has recently adapted several clustering algorithms from machine learning for the detection of Lagrangian Coherent Structures (LCS). The robustness and applicability of these tools is yet to be proven, especially for geophysical flows.

An updated, parameter-free spectral clustering approach is developed and a noise-based cluster coherence metric is introduced. This method, along with several common LCS approaches, is experimentally tested in two tidally-driven channel flows: in Scott Reef, Australia and around Martha’s Vineyard, Massachusetts. The Finite-Time Lyapunov Exponent and spectral clustering analyses were particularly helpful in describing key flow features and how they were impacted by tidal forcing, identifying transport barriers and convergence zones periodically occurring with the tide.