Bulletin of the American Physical Society
71st Annual Meeting of the APS Division of Fluid Dynamics
Volume 63, Number 13
Sunday–Tuesday, November 18–20, 2018; Atlanta, Georgia
Session KP1: Poster Session (3:20-4:05pm)
3:20 PM,
Monday, November 19, 2018
Georgia World Congress Center
Room: Level 1, Exhibit Hall B2 by the GFM videos
Abstract ID: BAPS.2018.DFD.KP1.164
Abstract: KP1.00164 : A space-time integral minimisation method for the reconstruction of velocity fields from measured scalar fields.*
Presenter:
Jurriaan Gillissen
(University College London)
Authors:
Jurriaan Gillissen
(University College London)
Alexandre Vilquin
(Bordeaux University)
Hamid Kellay
(Bordeaux University)
Roland Bouffanais
(Singapore University of Technology and Design)
Dick Yue
(Massachusetts Institute of Technology)
Scalar image velocimetry (SIV) reconstructs velocity vectors from scalar field measurements. The usual technique involves minimising a cost functional, that penalises the deviation from the scalar conservation equation. This approach requires the scalar measurements to be sufficiently resolved and relatively noise free, such that space and time derivatives can be accurately evaluated. To alleviate these requirements, we propose an improved SIV scheme, which does not require evaluating the space and time derivatives of the measured scalar field. Improved velocity reconstruction is demonstrated for a two dimensional, synthetic field. We furthermore apply the scheme to interferograms of the thickness field of a turbulent soap film. The reconstructed velocity fluctuations are within 10 % of laser Doppler velocimetry measurements.
*This research is supported by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise programme. The Center for Environmental Sensing and Modeling is an interdisciplinary research group of the Singapore MIT Alliance for Research and Technology.
To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2018.DFD.KP1.164
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