Coherent pattern identification in fluid flows
- Kristy Lynn Schlueter-Kuch.
- [Stanford, California] : [Stanford University], 2018.
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|3781 2018 S||In-library use|
- Schlueter-Kuch, Kristy Lynn, author.
- Dabiri, John O. (John Oluseun), degree supervisor.
- Darve, Eric, degree committee member.
- Ouellette, Nicholas (Nicholas Testroet), 1980- degree committee member.
- Saberi, Amin, degree committee member.
- Stanford University. Department of Mechanical Engineering.
- The motion of complex fluid flows, and material advection in them, can be understood though the identification and analysis of persistent transport features in the flow. These features, often referred to as coherent structures, act as a robust framework of material surfaces that separate the flow into distinct regions which resist mixing with surrounding regions. Understanding the evolution of these structures is a critical step in identifying the mechanisms underlying fluid transport. Material transport is central to many geophysical flow phenomena, such as the development of marine ecosystems, and the spread of pollutants including volcanic ash and oil. However, many of the techniques currently available for measuring such flows, including tracking arrays of Lagrangian drifters (e.g. ocean surface drifters and weather balloons), result in sparse and spatially irregular velocity data. This is insufficient for the use of many coherent structure detection algorithms, which rely on an assumption of initially closely-spaced fluid tracers. Additionally, current methods often focus on determination of the full boundaries of coherent sets, whereas in practice, it is often more valuable and practical to identify the complete set of trajectories that are coherent with an individual trajectory of interest. Motivated by the successes in using coherent structure analysis to study transport, and considering the limitations of measuring large-scale geophysical flows, this work details the development of an algorithm for detecting coherent patterns from potentially sparse Lagrangian flow trajectories. The method, based on principles from graph coloring and unsupervised machine learning, groups trajectories based on a measure of pairwise dissimilarity between their displacement patterns. Through the use of several analytical and experimental validation cases, this method detects coherent flow patterns using significantly less data than is required by existing techniques. Consideration of less optimal groupings of trajectories also allows for the detection of all trajectories coherent with a chosen trajectory of interest. Due to the robustness of the coherent patterns to effects such as chaotic advection, the flow structures identified prove useful in the assimilation of Lagrangian trajectory data into models of geophysical flows. Importantly, although the method is demonstrated here in the context of fluid flow kinematics, the generality of the approach allows for its potential application to other unsupervised clustering problems in dynamical systems.
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- Submitted to the Department of Mechanical Engineering.
- Thesis Ph.D. Stanford University 2018.
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