Published March 2020
| Published
Journal Article
Open
Guide to Spectral Proper Orthogonal Decomposition
Abstract
This paper discusses the spectral proper orthogonal decomposition and its use in identifying modes, or structures, in flow data. A specific algorithm based on estimating the cross-spectral density tensor with Welch's method is presented, and guidance is provided on selecting data sampling parameters and understanding tradeoffs among them in terms of bias, variability, aliasing, and leakage. Practical implementation issues, including dealing with large datasets, are discussed and illustrated with examples involving experimental and computational turbulent flow data.
Additional Information
© 2019 by Oliver T. Schmidt and Tim Colonius. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. Received 18 July 2019; revision received 10 December 2019; accepted for publication 16 December 2019; published online Open Access 27 January 2020. TC gratefully acknowledges support from Office of Naval Research grant N00014-16-1-2445. We would like to thank Aaron Towne for his important contributions to our present understanding of spectral proper orthogonal decomposition, and Guillaume Brès for generating the turbulent jet databases and for his patience in helping us use them. TC would also like to thank Jon Freund, Takao Suzuki, Peter Jordan, Joel Delville, Andre Cavalieri, Charles Tinney, and William George for helpful and/or entertaining discussions about proper orthogonal decomposition over the years.Attached Files
Published - 1.j058809.pdf
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1.j058809.pdf
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Additional details
- Eprint ID
- 101745
- Resolver ID
- CaltechAUTHORS:20200306-133727805
- Office of Naval Research (ONR)
- N00014-16-1-2445
- Created
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2020-03-06Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field