The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Modal Analysis of Fluid Flows: An Overview
Open accessAbstract
Simple aerodynamic configurations under even modest conditions can exhibit complex flows with a wide range of temporal and spatial features. It has become common practice in the analysis of these flows to look for and extract physically important features, or modes, as a first step in the analysis. This step typically starts with a modal decomposition of an experimental or numerical dataset of the flow field, or of an operator relevant to the system. We describe herein some of the dominant techniques for accomplishing these modal decompositions and analyses that have seen a surge of activity in recent decades. For a non-expert, keeping track of recent developments can be daunting, and the intent of this document is to provide an introduction to modal analysis that is accessible to the larger fluid dynamics community. In particular, we present a brief overview of several of the well-established techniques and clearly lay the framework of these methods using familiar linear algebra. The modal analysis techniques covered in this paper include the proper orthogonal decomposition (POD), balanced proper orthogonal decomposition (Balanced POD), dynamic mode decomposition (DMD), Koopman analysis, global linear stability analysis, and resolvent analysis.
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Trefethen, L. N., & Bau, III (1997) · doi:10.1137/1.9780898719574 · Cited by 3,301
Numerical Methods for Large Eigenvalue Problems: Revised Edition
Saad, Y. (2011) · doi:10.1137/1.9781611970739 · Cited by 2,364
Cited by
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Brunton, S. L., Noack, B. R., & Koumoutsakos, P. (2020) · Annual Review of Fluid Mechanics · doi:10.1146/annurev-fluid-010719-060214 · Cited by 3,063
Self-citationDigital twin: values, challenges and enablers from a modeling perspective
Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
Murata, T., Fukami, K., & Fukagata, K. (2020) · Journal of Fluid Mechanics · doi:10.1017/jfm.2019.822 · Cited by 359
Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning
Brunton, S. L., Nathan Kutz, J., Manohar, K., Aravkin, A. Y., Morgansen, K., Klemisch, J., Goebel, N., Buttrick, J., Poskin, J., Blom-Schieber, A. W., et al. (2021) · AIAA Journal · doi:10.2514/1.j060131 · Cited by 270
Self-citationGoebel, D. M., Becatti, G., Mikellides, I. G., & Lopez Ortega, A. (2021) · Journal of Applied Physics · doi:10.1063/5.0051228 · Cited by 95
Metadata
- Authors
- Taira, K., Brunton, S. L., Dawson, S. T. M., Rowley, C. W., Colonius, T., McKeon, B. J., Schmidt, O. T., Gordeyev, S., Theofilis, V., & Ukeiley, L. S.
- Venue
- AIAA Journal
- Published
- 2017-12
- Volume
- 55
- Issue
- 12
- Pages
- 4013-4041
- ISSN
- 0001-1452 (ISSN-L)
- 1533-385X
- OpenAlex
- W2963448313
- Cited by
- 1,894
- References
- 2
- FWCI
- 70.97
- Type
- journal article
- Language
- en
- Open access
- green
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- Taira, K., Brunton, S. L., Dawson, S. T. M., Rowley, C. W., Colonius, T., McKeon, B. J., Schmidt, O. T., Gordeyev, S., Theofilis, V., & Ukeiley, L. S. (2017). Modal Analysis of Fluid Flows: An Overview. AIAA Journal. https://doi.org/10.2514/1.j056060
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