An algorithm to estimate unsteady and quasi-steady pressure fields from velocity field measurements
An algorithm to estimate unsteady and quasi-steady pressure fields from velocity field measurements
Date
2013-10-10
Authors
Dabiri, John O.
Bose, Sanjeeb
Gemmell, Brad J.
Colin, Sean P.
Costello, John H.
Bose, Sanjeeb
Gemmell, Brad J.
Colin, Sean P.
Costello, John H.
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DOI
10.1242/jeb.092767
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Swimming
Flying
Wakes
Feeding
Particle image velocimetry
Flying
Wakes
Feeding
Particle image velocimetry
Abstract
We describe and characterize a method for estimating the pressure field corresponding to velocity field measurements such as those obtained by using particle image velocimetry. The pressure gradient is estimated from a time series of velocity fields for unsteady calculations or from a single velocity field for quasi-steady calculations. The corresponding pressure field is determined based on median polling of several integration paths through the pressure gradient field in order to reduce the effect of measurement errors that accumulate along individual integration paths. Integration paths are restricted to the nodes of the measured velocity field, thereby eliminating the need for measurement interpolation during this step and significantly reducing the computational cost of the algorithm relative to previous approaches. The method is validated by using numerically simulated flow past a stationary, two-dimensional bluff body and a computational model of a three-dimensional, self-propelled anguilliform swimmer to study the effects of spatial and temporal resolution, domain size, signal-to-noise ratio and out-of-plane effects. Particle image velocimetry measurements of a freely swimming jellyfish medusa and a freely swimming lamprey are analyzed using the method to demonstrate the efficacy of the approach when applied to empirical data.
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© The Author(s), 2013. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Experimental Biology 217 (2014): 331-336, doi:10.1242/jeb.092767.
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Journal of Experimental Biology 217 (2014): 331-336