A framework for estimating potential fluid flow from digital imagery

Luttman, Aaron; Bollt, Erik M.; Basnayake, Ranil; Kramer, Sean; Tufillaro, Nicholas B.

Citeable URL: https://ir.library.oregonstate.edu/concern/articles/pv63g186n

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Attribute Name	Values
Creator	Luttman, Aaron Bollt, Erik M. Basnayake, Ranil Kramer, Sean Tufillaro, Nicholas B.
Abstract	Given image data of a fluid flow, the flow field, < u, v >, governing the evolution of the system can be estimated using a variational approach to optical flow. Assuming that the flow field governing the advection is the symplectic gradient of a stream function or the gradient of a potential function-both falling under the category of a potential flow-it is natural to re-frame the optical flow problem to reconstruct the stream or potential function directly rather than the components of the flow individually. There are several advantages to this framework. Minimizing a functional based on the stream or potential function rather than based on the components of the flow will ensure that the computed flow is a potential flow. Next, this approach allows a more natural method for imposing scientific priors on the computed flow, via regularization of the optical flow functional. Also, this paradigm shift gives a framework-rather than an algorithm-and can be applied to nearly any existing variational optical flow technique. In this work, we develop the mathematical formulation of the potential optical flow framework and demonstrate the technique on synthetic flows that represent important dynamics for mass transport in fluid flows, as well as a flow generated by a satellite data-verified ocean model of temperature transport. (C) 2013 AIP Publishing LLC.
Resource Type	Article
DOI	https://doi.org/10.1063/1.4821188
Date Available	2013-11-14T18:24:33+00:00
Date Issued	2013-09-11
Citation	Luttman, A., Bollt, E. M., Basnayake, R., Kramer, S., & Tufillaro, N. B. (2013). A framework for estimating potential fluid flow from digital imagery. Chaos (Woodbury, N.Y.), 23(3), 033134. doi:10.1063/1.4821188
Journal Title	Chaos
Journal Volume	23
Journal Issue/Number	3
Keyword	Motion L curve Optical flow Models Ill-posed problems Regularization parameter selection
Rights Statement	Copyright Not Evaluated
Funding Statement (additional comments about funding)	Aaron Luttman, Erik M. Bollt, Ranil Basnayake, and Sean Kramer work was supported by the U.S. Office of Naval Research under Grant No. N00014-09-1-0647.
Publisher	AIP Publishing LLC
Peer Reviewed	Yes
Language	English [eng]
Replaces	http://hdl.handle.net/1957/44124
Additional Information	description.provenance : Made available in DSpace on 2013-11-14T18:24:33Z (GMT). No. of bitstreams: 1 TufillaroNicholasBCEOASFrameworkEstimatingPotential.pdf: 5358448 bytes, checksum: 7263e9006ce690a8f6e3d32c9b1aa00d (MD5) Previous issue date: 2013-09-11 description.provenance : Submitted by Deborah Campbell (deborah.campbell@oregonstate.edu) on 2013-11-14T18:24:33Z No. of bitstreams: 1 TufillaroNicholasBCEOASFrameworkEstimatingPotential.pdf: 5358448 bytes, checksum: 7263e9006ce690a8f6e3d32c9b1aa00d (MD5)

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