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CATEGORIES:Institute for Energy and Environmental Flows (IEEF
)
SUMMARY:Inverse problems in fluid dynamics for enhanced ve
locimetry - Alexandros Kontogiannis (University of
Cambridge)
DTSTART;TZID=Europe/London:20230216T113000
DTEND;TZID=Europe/London:20230216T123000
UID:TALK195559AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/195559
DESCRIPTION:We formulate a digital twin approach to the recons
truction of noisy and sparse velocity images. The
method learns the most probable fluid dynamics mod
el that fits the data by solving a Bayesian invers
e Navierâ€“Stokes boundary value problem. This joint
ly reconstructs and segments the velocity field\,
and at the same time infers hidden quantities such
as the hydrodynamic pressure and the wall shear s
tress. Using a Bayesian framework\, we regularize
the problem by introducing a priori information ab
out the unknown parameters in the form of Gaussian
random fields. This further allows us to estimate
the uncertainties of the unknowns by approximatin
g their posterior covariance with a quasi-Newton m
ethod. Although this method has been developed for
magnetic resonance velocimetry (flow-MRI)\, it ex
tends to other velocimetry methods such as ultraso
und Doppler velocimetry\, particle image velocimet
ry (PIV) and scalar image velocimetry (SIV).
LOCATION:Open Plan Area\, Institute for Energy and Environm
ental Flows\, Madingley Rise CB3 0EZ
CONTACT:Catherine Pearson
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