Proper Orthogonal Decomposition Technique for Transonic Unsteady Aerodynamic Flows

By Kenneth C. Hall, Jeffrey P. Thomas, Earl H. Dowell. 2000

420 citations recorded by the source corpus.

Subfields: Computational Mechanics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/1d0c0a9650541e00942439657965fe2cb95c122e

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In Computational Mechanics

In Statistical and Nonlinear Physics

Citations in the corpus

Aeroelastic and Aerothermoelastic Analysis in Hypersonic Flow: Past, Present, and Future (2011, methodology): “Typical approaches for constructing aerodynamic ROMs are proper orthogonal decomposition (POD) [81–84], Volterra series [82,85], and surrogates [71,86–92].”
Reduced-Order Nonlinear Unsteady Aerodynamic Modeling Using a Surrogate-Based Recurrence Framework (2010, background): “Examples of such approaches include proper orthogonal decomposition (POD) [4–7], Volterra series [4,8], and surrogate-based approaches [9–13].”
Balanced Model Reduction via the Proper Orthogonal Decomposition (2002, background): “FromEq.( 18 ), it can be seen that thetransformation T that balances the systemis thatcontaining theeigenvectorsofthegrammian product Wco as its columns.”
The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows (2012, methodology): “Within these contexts, projection-based model-reduction methods have been successfully applied to problems in structural dynamics [3], aerodynamics [9, 10, 11, 12, 13] and aeroelasticity [14, 15, 16, 17, 18, 19], among others.”
Efficient non‐linear model reduction via a least‐squares Petrov–Galerkin projection and compressive tensor approximations (2011, methodology): “Within these contexts, the projection-based model reduction technique has been successfully applied to problems in aerodynamics [8, 9, 10, 11, 12], aeroelasticity [13, 14, 15, 16, 17], control [18], structural dynamics [19, 20, 21, 22], and non destructive evaluation and parameter estimation [23], t”
Using Automatic Differentiation to Create a Nonlinear Reduced-Order-Model Aerodynamic Solver (2010, methodology): “In recent years, the Duke University aeroelasticity research group has been active in the development of ROMs for linearized unsteady frequency-domain computational flow solvers [6,7,12].”