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Particle-Imaging Techniques for Experimental Fluid Mechanics
Abstract
The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniques to extract ...Read More
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“For the verification of the oil-painting-based flow visualizations we used the method of particle image velocimetry (PIV) [26].”
From Diving-Flight Aerodynamics of a Peregrine Falcon (Falco peregrinus) (2014) · cites this paper as [26]
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Metadata
- Authors
- Adrian, R. J.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Corresponding author
- Adrian, Ronald J.
- ORCID
- Adrian, Ronald J.
- Venue
- Annual Review of Fluid Mechanics
- Publisher
- Annual Reviews
- Published
- 1991-01
- Volume
- 23
- Issue
- 1
- Pages
- 261-304
- ISSN
- 0066-4189 (ISSN-L)
- 1545-4479
- OpenAlex
- W2118944048
- Cited by
- 3,405
- References
- 0
- Citations by year
- 2026: 108 · 2025: 86 · 2024: 94 · 2023: 108 · 2022: 91 · 2021: 99 · 2020: 122 · 2019: 116 · 2018: 102 · 2017: 134 · 2016: 106 · 2015: 126 · 2014: 128 · 2013: 146 · 2012: 136
- FWCI
- 48.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annual Review of Fluid Mechanics (publishedVersion)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › Fluid Dynamics and Turbulent Flows
- Physical Sciences › Chemical Engineering › Fluid Flow and Transfer Processes › Particle Dynamics in Fluid Flows
- Physical Sciences › Engineering › Computational Mechanics › Fluid Dynamics and Heat Transfer
- Keywords
- particle imaging, particle image velocimetry, flow visualization, experimental fluid mechanics, machine learning, spatial and temporal scales
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