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

Data assimilation of mean velocity from 2D PIV measurements of flow over an idealized airfoil
Data assimilation of mean velocity from 2D PIV measurements of flow over an idealized airfoil
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.
0723-4864
Symon, Sean
2e1580c3-ba27-46e8-9736-531099f3d850
Dovetta, Nicolas
62a23f20-8ccc-49ed-97d7-a7a1cfb26ec1
McKeon, Beverley J.
4623066f-492f-4944-a541-151a6a130402
Sipp, Denis
58cb1e91-b79e-4efe-aef6-929384921418
Schmid, Peter J.
67225b59-07a3-4ea8-b65b-df8bb6a17ecd
Symon, Sean
2e1580c3-ba27-46e8-9736-531099f3d850
Dovetta, Nicolas
62a23f20-8ccc-49ed-97d7-a7a1cfb26ec1
McKeon, Beverley J.
4623066f-492f-4944-a541-151a6a130402
Sipp, Denis
58cb1e91-b79e-4efe-aef6-929384921418
Schmid, Peter J.
67225b59-07a3-4ea8-b65b-df8bb6a17ecd

Symon, Sean, Dovetta, Nicolas, McKeon, Beverley J., 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), [61]. (doi:10.1007/s00348-017-2336-8).

Record type: Article

Abstract

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.

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More information

Accepted/In Press date: 20 March 2017
Published date: 22 April 2017

Identifiers

Local EPrints ID: 444775
URI: http://eprints.soton.ac.uk/id/eprint/444775
ISSN: 0723-4864
PURE UUID: f78cf270-7ebe-4aa0-b5f1-7f8735943c71

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Date deposited: 04 Nov 2020 17:31
Last modified: 04 Nov 2020 17:31

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Contributors

Author: Sean Symon
Author: Nicolas Dovetta
Author: Beverley J. McKeon
Author: Denis Sipp
Author: Peter J. Schmid

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