Next Article in Journal
Onset of Inertial Magnetoconvection in Rotating Fluid Spheres
Next Article in Special Issue
Spectral Early-Warning Signals for Sudden Changes in Time-Dependent Flow Patterns
Previous Article in Journal
Turbulent Bubble-Laden Channel Flow of Power-Law Fluids: A Direct Numerical Simulation Study
Previous Article in Special Issue
Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories
Open AccessArticle

An Optimized-Parameter Spectral Clustering Approach to Coherent Structure Detection in Geophysical Flows

1
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
2
Physical Oceanography Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA
*
Author to whom correspondence should be addressed.
Fluids 2021, 6(1), 39; https://doi.org/10.3390/fluids6010039
Received: 20 November 2020 / Revised: 4 January 2021 / Accepted: 8 January 2021 / Published: 12 January 2021
(This article belongs to the Special Issue Lagrangian Transport in Geophysical Fluid Flows)
In Lagrangian dynamics, the detection of coherent clusters can help understand the organization of transport by identifying regions with coherent trajectory patterns. Many clustering algorithms, however, rely on user-input parameters, requiring a priori knowledge about the flow and making the outcome subjective. Building on the conventional spectral clustering method of Hadjighasem et al. (2016), a new optimized-parameter spectral clustering approach is developed that automatically identifies optimal parameters within pre-defined ranges. A noise-based metric for quantifying the coherence of the resulting coherent clusters is also introduced. The optimized-parameter spectral clustering is applied to two benchmark analytical flows, the Bickley Jet and the asymmetric Duffing oscillator, and to a realistic, numerically generated oceanic coastal flow. In the latter case, the identified model-based clusters are tested using observed trajectories of real drifters. In all examples, our approach succeeded in performing the partition of the domain into coherent clusters with minimal inter-cluster similarity and maximum intra-cluster similarity. For the coastal flow, the resulting coherent clusters are qualitatively similar over the same phase of the tide on different days and even different years, whereas coherent clusters for the opposite tidal phase are qualitatively different. View Full-Text
Keywords: optimized-parameter spectral clustering; Lagrangian Coherent Structures; clusters; geophysical flows; unsupervised machine learning optimized-parameter spectral clustering; Lagrangian Coherent Structures; clusters; geophysical flows; unsupervised machine learning
Show Figures

Figure 1

MDPI and ACS Style

Filippi, M.; Rypina, I.I.; Hadjighasem, A.; Peacock, T. An Optimized-Parameter Spectral Clustering Approach to Coherent Structure Detection in Geophysical Flows. Fluids 2021, 6, 39. https://doi.org/10.3390/fluids6010039

AMA Style

Filippi M, Rypina II, Hadjighasem A, Peacock T. An Optimized-Parameter Spectral Clustering Approach to Coherent Structure Detection in Geophysical Flows. Fluids. 2021; 6(1):39. https://doi.org/10.3390/fluids6010039

Chicago/Turabian Style

Filippi, Margaux; Rypina, Irina I.; Hadjighasem, Alireza; Peacock, Thomas. 2021. "An Optimized-Parameter Spectral Clustering Approach to Coherent Structure Detection in Geophysical Flows" Fluids 6, no. 1: 39. https://doi.org/10.3390/fluids6010039

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop