The DOI matches its Crossref record.
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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In the citing paper
“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]
Citations
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Machine Learning for Fluid Mechanics
Brunton, S. L., Noack, B. R., & Koumoutsakos, P. (2020) · Annual Review of Fluid Mechanics · doi:10.1146/annurev-fluid-010719-060214 · Cited by 3,063
“Experimental flow field measurements from particle image velocimetry (PIV) (Willert & Gharib 1991; Adrian 1991) provide a compelling application where there is a tension between local flow resolution and the size of the imaging domain.”
BackgroundPIVlab – Towards User-friendly, Affordable and Accurate Digital Particle Image Velocimetry in MATLAB
Thielicke, W., & Stamhuis, E. J. (2014) · Journal of Open Research Software · doi:10.5334/jors.bl · Cited by 2,408
“Detailed results of these tests are reported in [29] 2 .”
BackgroundVortex organization in the outer region of the turbulent boundary layer
Adrian, R. J., Meinhart, C. D., & Tomkins, C. D. (2000) · Journal of Fluid Mechanics · doi:10.1017/s0022112000001580 · Cited by 1,715
“The interrogation spots of the PIV measurements reported here are overlapped by 50%, to minimize aliasing of the velocity signal (Adrian 1991) and to provide velocity information at every point on a 1 mm× 1 mm grid.”
BackgroundMethodResultInfluentialSelf-citationSoil deformation measurement using particle image velocimetry (PIV) and photogrammetry
White, D. J., Take, W. A., & Bolton, M. D. (2003) · Géotechnique · doi:10.1680/geot.2003.53.7.619 · Cited by 1,289
Tomographic particle image velocimetry
Elsinga, G. E., Scarano, F., Wieneke, B., & van Oudheusden, B. W. (2006) · Experiments in Fluids · doi:10.1007/s00348-006-0212-z · Cited by 1,275
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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