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Model Reduction for Flow Analysis and Control
Open accessAbstract
Advances in experimental techniques and the ever-increasing fidelity of numerical simulations have led to an abundance of data describing fluid flows. This review discusses a range of techniques for analyzing such data, with the aim of extracting simplified models that capture the essential features of these flows, in order to gain insight into the flow physics, and potentially identify mechanisms for controlling these flows. We review well-developed techniques, such as proper orthogonal decomposition and Galerkin projection, and discuss more recent techniques developed for linear systems, such as balanced truncation and dynamic mode decomposition (DMD). We then discuss some of the methods available for nonlinear systems, with particular attention to the Koopman operator, an infinite-dimensional linear operator that completely characterizes the dynamics of a nonlinear system and provides an extension of DMD to nonlinear systems.
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Trefethen, L. N., & Bau, III (1997) · doi:10.1137/1.9780898719574 · Cited by 3,301
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Shallow neural networks for fluid flow reconstruction with limited sensors
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Metadata
- Authors
- Rowley, C. W., & Dawson, S. T. M.
- Venue
- Annual Review of Fluid Mechanics
- Publisher
- Annual Reviews
- Published
- 2017-01-03
- Volume
- 49
- Issue
- 1
- Pages
- 387-417
- ISSN
- 0066-4189 (ISSN-L)
- 1545-4479
- OpenAlex
- W2346717862
- Cited by
- 792
- References
- 1
- FWCI
- 9.19
- Type
- journal article
- Language
- en
- Open access
- bronze
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