An algorithm to estimate unsteady and quasi-steady pressure fields from velocity field measurements

被引:123
作者
Dabiri, John O. [1 ,2 ]
Bose, Sanjeeb [3 ]
Gemmell, Brad J. [4 ]
Colin, Sean P. [5 ]
Costello, John H. [6 ]
机构
[1] CALTECH, Grad Aeronaut Labs, Pasadena, CA 91125 USA
[2] CALTECH, Pasadena, CA 91125 USA
[3] Stanford Univ, Ctr Turbulence Res, Stanford, CA 94305 USA
[4] Marine Biol Lab, Whitman Ctr, Woods Hole, MA 02543 USA
[5] Roger Williams Univ, Bristol, RI 02809 USA
[6] Providence Coll, Dept Biol, Providence, RI 02918 USA
基金
美国国家科学基金会;
关键词
Swimming; Flying; Wakes; Feeding; Particle image velocimetry; FLOW; VORTEX; PIV;
D O I
10.1242/jeb.092767
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:331 / 336
页数:6
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