Identification of Lagrangian Coherent Structures Around Swimming Jellyfish from Experimental Time-Series Data

The unique body kinematics of jellyfish embodies the most intriguing form of biological propulsion, which makes jellyfish a promising resource for developing new locomotion systems. Instead of the conventional Eulerian method, we take an unprecedented Lagrangian approach by tracking individual fluid...

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Bibliographic Details
Main Author: Zhang, Zhonglin Johnny
Format: Others
Published: 2008
Online Access:https://thesis.library.caltech.edu/10642/1/ZhonglinJZhangThesis_seniorthesis.pdf
Zhang, Zhonglin Johnny (2008) Identification of Lagrangian Coherent Structures Around Swimming Jellyfish from Experimental Time-Series Data. Senior thesis (Minor), California Institute of Technology. doi:10.7907/KK45-ZV02. https://resolver.caltech.edu/CaltechThesis:01112018-113614173 <https://resolver.caltech.edu/CaltechThesis:01112018-113614173>
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Summary:The unique body kinematics of jellyfish embodies the most intriguing form of biological propulsion, which makes jellyfish a promising resource for developing new locomotion systems. Instead of the conventional Eulerian method, we take an unprecedented Lagrangian approach by tracking individual fluid particles around a swimming jellyfish over a finite time interval. Specifically, we utilize the Lagrangian coherent structures (LCS) in the flow field to investigate the flow characteristics around a jellyfish. LCS are separatrices or invariant manifolds, which separate the flow field into distinct regions. To locate the LCS in the flow, we employ the concept of the finite-time Lyapunov Exponent (FTLE), which measures the rate at which particles diverge from each other, and LCS are identified as the high-value ridges in the FTLE field. This method is implemented and validated by analysis on two-dimensional vortex dipole flow, two-dimensional experimental time-series data, and Hill’s vortex sphere. This method is expected to extract LCS from three-dimensional experimental time-series data.