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Data assimilation of mean velocity from 2D PIV measurements of flow over an idealized airfoil

Symon, Sean and Dovetta, Nicolas and McKeon, Beverley J. and Sipp, Denis and Schmid, Peter J. (2017) Data assimilation of mean velocity from 2D PIV measurements of flow over an idealized airfoil. Experiments in Fluids, 58 (5). Art. No. 61. ISSN 0723-4864. doi:10.1007/s00348-017-2336-8.

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Data assimilation can be used to combine experimental and numerical realizations of the same flow to produce hybrid flow fields. These have the advantages of less noise contamination and higher resolution while simultaneously reproducing the main physical features of the measured flow. This study investigates data assimilation of the mean flow around an idealized airfoil (Re = 13,500) obtained from time-averaged two-dimensional particle image velocimetry (PIV) data. The experimental data, which constitute a low-dimensional representation of the full flow field due to resolution and field-of-view limitations, are incorporated into a simulation governed by the two-dimensional, incompressible Reynolds-averaged Navier–Stokes (RANS) equations with an unknown momentum forcing. This forcing, which corresponds to the divergence of the Reynolds stress tensor, is calculated from a direct-adjoint optimization procedure to match the experimental and numerical mean velocity fields. The simulation is projected onto the low-dimensional subspace of the experiment to calculate the discrepancy and a smoothing procedure is used to recover adjoint solutions on the higher dimensional subspace of the simulation. The study quantifies how well data assimilation can reconstruct the mean flow and the minimum experimental measurements needed by altering the resolution and domain size of the time-averaged PIV.

Item Type:Article
Related URLs:
URLURL TypeDescription ReadCube access
Symon, Sean0000-0001-9085-0778
McKeon, Beverley J.0000-0003-4220-1583
Sipp, Denis0000-0002-2808-3886
Schmid, Peter J.0000-0002-6585-8871
Additional Information:© 2017 Springer-Verlag Berlin Heidelberg. Received: 6 September 2016. Revised: 13 March 2017. Accepted: 20 March 2017. Support from a National Science Foundation Graduate Fellowship (S.S.) is gratefully acknowledged. The authors would also like to thank Professors Tim Colonius and Anthony Leonard for discussions about the direct-adjoint optimization framework as well as Kevin Rosenberg for his assistance in implementing the optimization procedure on a cluster. Finally, the authors wish to thank the referees for their suggestions which improved the clarity of the paper.
Funding AgencyGrant Number
NSF Graduate Research FellowshipUNSPECIFIED
Issue or Number:5
Record Number:CaltechAUTHORS:20170426-063702442
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Official Citation:Symon, S., Dovetta, N., McKeon, B.J. et al. Exp Fluids (2017) 58: 61. doi:10.1007/s00348-017-2336-8
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:76930
Deposited By: Ruth Sustaita
Deposited On:26 Apr 2017 16:16
Last Modified:15 Nov 2021 17:03

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