Gillissen, JJJ;
Bouffanais, R;
Yue, DKP;
(2019)
Data assimilation method to de-noise and de-filter particle image velocimetry data.
Journal of Fluid Mechanics
, 877
pp. 196-213.
10.1017/jfm.2019.602.
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Abstract
We present a variational data assimilation method in order to improve the accuracy of velocity fields v˜, that are measured using particle image velocimetry (PIV). The method minimises the space-time integral of the difference between the reconstruction u and v˜, under the constraint, that u satisfies conservation of mass and momentum. We apply the method to synthetic velocimetry data, in a two-dimensional turbulent flow, where realistic PIV noise is generated by computationally mimicking the PIV measurement process. The method performs optimally when the assimilation integration time is of the order of the flow correlation time. We interpret these results by comparing them to onedimensional diffusion and advection problems, for which we derive analytical expressions for the reconstruction error
| Type: | Article |
|---|---|
| Title: | Data assimilation method to de-noise and de-filter particle image velocimetry data |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1017/jfm.2019.602 |
| Publisher version: | https://doi.org/10.1017/jfm.2019.602 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10083216 |
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