## Real-time planar flow velocity measurements using an optical flow algorithm implemented on GPU

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arXiv:1306.2461 [physics.flu-dyn], (11 Jun 2013)

@article{2013arXiv1306.2461G,

author={Gautier}, N. and {Aider}, J.},

title={"{Real-time planar flow velocity measurements using an optical flow algorithm implemented on GPU}"},

journal={ArXiv e-prints},

archivePrefix={"arXiv"},

eprint={1306.2461},

primaryClass={"physics.flu-dyn"},

keywords={Physics – Fluid Dynamics, I.2.9 I.2.10 I.3.1},

year={2013},

month={jun},

adsurl={http://adsabs.harvard.edu/abs/2013arXiv1306.2461G},

adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

This paper presents a high speed implementation of an optical flow algorithm which computes planar velocity fields in an experimental flow. Real-time computation of the flow velocity field allows the experimentalist to have instantaneous access to quantitative features of the flow. This can be very useful in many situations: fast evaluation of the performances and characteristics of a new setup, design optimization, easier and faster parametric studies, etc. It can also be a valuable measurement tool for closed-loop flow control experiments where fast estimation of the state of the flow is needed. The algorithm is implemented on a Graphics Processing Unit (GPU). The accuracy of the computation is shown. Computation speed and scalability are highlighted along with guidelines for further improvements. The system architecture is flexible, scalable and can be adapted on the fly in order to process higher resolutions or achieve higher precision. The set-up is applied on a Backward-Facing Step (BFS) flow in a hydrodynamic channel. For validation purposes, classical Particle Image Velocimetry (PIV) is used to compare with instantaneous optical flow measurements. Important flow characteristics, such as the dynamics of the recirculation bubble, are well recovered in real time. Accuracy of real-time optical flow measurements is comparable to off-line PIV computations.

June 13, 2013 by hgpu