6 research outputs found
Data assimilation method to de-noise and de-filter particle image velocimetry data
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 erro