House, D, Skene, C orcid.org/0000-0003-0994-2013, Ribeiro, JHM et al. (2 more authors) (2022) Sketch-Based Resolvent Analysis. In: AIAA AVIATION 2022 Forum. AIAA AVIATION 2022 Forum, 27 Jun - 01 Jul 2022, Chicago, IL, USA. American Institute of Aeronautics and Astronautics ISBN 978-1-62410-635-4
Abstract
A significant hurdle in the adoption of resolvent analysis is the singular value decomposition (SVD) of the large linear operators involved. A matrix sketching algorithm is used to extract the primary forcing and response modes, with their associated gain. The formulation of an iterative algorithm is shown to be able to calculate the SVD of the resolvent operator with greater accuracy. The sources of error due to the selection of a test vector are discussed and it is shown that an accurate calculation of the forcing and response modes can be obtained by utilizing a test vector corresponding to a single point. The strength of this algorithm is shown by calculating the resolvent modes for a flow over a NACA 0012 airfoil at a Reynolds number of 23,000. This method is shown to converge for an arbitrary selection of test vector, obtaining results in agreement with past studies of this flow.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. This is an author produced version of a conference paper published in AIAA AVIATION 2022 Forum. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Applied Mathematics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Jun 2022 12:51 |
Last Modified: | 30 Jun 2022 12:51 |
Status: | Published |
Publisher: | American Institute of Aeronautics and Astronautics |
Identification Number: | 10.2514/6.2022-3335 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:188533 |