The problem of understanding transport pathways in the ocean poses significant modeling and observation challenges.
In the last decade new methods from dynamical system theory have been used for the identification of the barriers to transport in the oceans, as well as for the identification of their time variability, thus providing tools for the identification of the ?Skeleton? of the flow.
“Lagrangian coherent structures (LCS) such as stable and unstable manifolds and hyperbolic trajectories act as barriers to transport and they help visualize the combined effect of the time variability and geometric features (such as eddies and jets) on transport” – in Annalisa Griffa, Angelique Haza, Tamay Ozgokmen, Anne Molcard, Vincent Taillandier, Katrin Schroeder, Yeon Chang, P.-M.Poulain, ‘Understanding transport pathways in the ocean”, Deep-Sea Re- searchII85(2013)81?95.
The performance of such methods hinges on the assumption that the velocity field is known with good accuracy. One of the methodologies to improve model performance consists in the assimilation of Lagrangian data provided by floating buoys especially designed to mimic the motions of water particles.
The proposed work consists of developing coordination and control strategies for multi-vehicle systems to identify and track LCS. This entails coupling computational models, data assimilation, and active multi-vehicle control to improve modeling performance.