Modal Analysis of Fluid Flows: Applications and Outlook

By Kunihiko Taira, Maziar S. Hemati, Steven L. Brunton, Yiyang Sun, Karthik Duraisamy, Shervin Bagheri, Scott T. M. Dawson, C. Yeh. AIAA Journal, 2019

515 citations recorded by the source corpus.

We present applications of modal analysis techniques to study, model, and control canonical aerodynamic flows. To illustrate how modal analysis techniques can provide physical insights in a complementary manner, we selected four fundamental examples of cylinder wakes, wall-bounded flows, airfoil wakes, and cavity flows. We also offer brief discussions on the outlook for modal analysis techniques, in light of rapid developments in data science.

Subfields: Computational Mechanics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/f12784ffab2fe690068a5d29b586e89aa6640663

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In Computational Mechanics

In Statistical and Nonlinear Physics

Citations in the corpus

Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders (2020, background): “planation of POD and its relationship with other dimension-reduction techniques in [46].”
Machine Learning in Aerodynamic Shape Optimization (2022, unknown): “For the ith mode with eigenvalue μi, the frequency and growth rate are fi = Im(lnμi)/2π∆t and gi = Re(lnμi)/∆t, which can also be expressed as fi = ∠μi/2π∆t and fi = |μi|/∆t [213].”
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics (2019, background): “In recent years, machine learning has been widely applied in the field of fluid dynamics, and is highly expected for its strong ability to account for nonlinearity (Brunton & Noack 2015; Kutz 2017; Taira et al. 2019; Brunton et al. 2019).”