Classifying vortex wakes using neural networks
Open accessAbstract
Unsteady flows contain information about the objects creating them. Aquatic organisms offer intriguing paradigms for extracting flow information using local sensory measurements. In contrast, classical methods for flow analysis require global knowledge of the flow field. Here, we train neural networks to classify flow patterns using local vorticity measurements. Specifically, we consider vortex wakes behind an oscillating airfoil and we evaluate the accuracy of the network in distinguishing between three wake types, 2S, 2P + 2S and 2P + 4S. The network uncovers the salient features of each wake type.
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At the meter scale, the flow environment is characterized by long-lived coherent vortex structures, distinct from the uniform turbulence experienced at smaller scales.
“At this scale, the flow environment is characterized by long-lived coherent vortex structures56–58, distinct from the uniform turbulence experienced at smaller scales59,60.”
From Sensing flow gradients is necessary for learning autonomous underwater navigation (2025) · cites this paper as [57]
The Introduction (page unavailable) describes long-lived vortical flows left by moving objects and organized vortex wakes, supporting the coherent-vortex portion of the claim. It does not establish that this characterizes the environment specifically at the meter scale. The smaller-scale turbulence comparison is attached to different citations in the citing sentence. (LLM, conf=0.84)
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- Authors
- Colvert, B., Alsalman, M., & Kanso, E.
- Affiliations
- University of Southern California (US)
- Corresponding author
- Kanso, Eva
- Venue
- Bioinspiration & Biomimetics
- Publisher
- IOP Publishing
- Published
- 2018-02-16
- Volume
- 13
- Issue
- 2
- Pages
- 025003
- ISSN
- 1748-3182 (ISSN-L)
- 1748-3190
- OpenAlex
- W2751013712
- Cited by
- 95
- References
- 50
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 13 · 2023: 16 · 2022: 15 · 2021: 14 · 2020: 10 · 2019: 6 · 2018: 2
- FWCI
- 6.50
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Bioinspiration & Biomimetics (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Office of Naval Research Global (N00014-14-1-0421, N00014-17-1-2287)
- Army Research Office (W911NF-16-1-0074)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Biomimetic flight and propulsion mechanisms
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
- Physical Sciences › Engineering › Computational Mechanics › Fluid Dynamics and Vibration Analysis
- Keywords
- wake vortices, artificial neural networks, vorticity measurement, oscillating airfoil, unsteady flow, flow sensing
- MeSH terms
- Machine Learning, Computer Simulation, Neural Networks, Computer, Biomimetics, Physical Phenomena, Hydrodynamics
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