Compressive sensing is used to determine the flow characteristics around a cylinder (Reynolds number and pressure/flow field) from a sparse number of pressure measurements on the cylinder. Using a supervised machine learning strategy, library elements encoding the dimensionally reduced dynamics are computed for various Reynolds numbers. Convex L1 optimization is then used with a limited number of pressure measurements on the cylinder to reconstruct, or decode, the full pressure field and the resulting flow field around the cylinder. Aside from the highly turbulent regime (large Reynolds number) where only the Reynolds number can be identified, accurate reconstruction of the pressure field and Reynolds number is achieved. The proposed data-driven strategy thus achieves encoding of the fluid dynamics using the L2 norm, and robust decoding (flow field reconstruction) using the sparsity promoting L1 norm.
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December 2013
Research Article|
December 05 2013
Compressive sensing based machine learning strategy for characterizing the flow around a cylinder with limited pressure measurements
Ido Bright;
Ido Bright
1Department of Applied Mathematics,
University of Washington
, Seattle, Washington 98195-2420, USA
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Guang Lin;
Guang Lin
2
Pacific Northwest National Laboratory
, PO Box 999, Richland, Washington 99352, USA
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J. Nathan Kutz
J. Nathan Kutz
a)
1Department of Applied Mathematics,
University of Washington
, Seattle, Washington 98195-2420, USA
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a)
Author to whom correspondence should be addressed. Electronic mail: kutz@uw.edu. Telephone: +12066853029.
Physics of Fluids 25, 127102 (2013)
Article history
Received:
April 22 2013
Accepted:
November 15 2013
Citation
Ido Bright, Guang Lin, J. Nathan Kutz; Compressive sensing based machine learning strategy for characterizing the flow around a cylinder with limited pressure measurements. Physics of Fluids 1 December 2013; 25 (12): 127102. https://doi.org/10.1063/1.4836815
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