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SUMMARY:Data assimilating mean velocity measurements into computational fl
 uid dynamics - Sean Symon (University of Southampton)
DTSTART:20230310T124500Z
DTEND:20230310T134500Z
UID:TALK195154@talks.cam.ac.uk
CONTACT:Paras Vadher
DESCRIPTION:Data assimilation is the principle of combining uncertain meas
 urements from experiments with an imperfect model to obtain a better predi
 ction than either experiments or simulations can offer independently. Data
  assimilation removes noise\, fills in missing experimental data and reduc
 es uncertainty associated with ambiguous modelling parameters in simulatio
 ns such as turbulence production or boundary conditions. In this talk\, se
 veral methods for assimilating mean velocity measurements into low-fidelit
 y computational fluid dynamics (CFD) are discussed. The experimental data 
 are obtained from particle image velocimetry (PIV) and each method introdu
 ces an unknown forcing term into the Reynolds-averaged Navier-Stokes (RANS
 ) equations. An optimisation problem is formulated whereby the unknown for
 cing is updated such that the discrepancy between the experimental mean ve
 locity and the CFD is minimised. The talk will address how data assimilati
 on can overcome several limitations of PIV such as sparsity\, limited fiel
 d of view and noise. \n
LOCATION:LR5
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