Three-Dimensional Transonic Aeroelasticity Using Proper Orthogonal Decomposition-Based Reduced-Order Models

By Jeffrey P. Thomas, Earl H. Dowell, Kenneth C. Hall. 2001

206 citations recorded by the source corpus.

Subfields: Computational Mechanics, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/31b40f0ca7e00baac2f7526509790ff3ca5c5953

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In Computational Mechanics

In Statistical and Nonlinear Physics

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Citations in the corpus

Reduced order unsteady aerodynamic modeling for stability and control analysis using computational fluid dynamics (2014, methodology): “[62] applied POD-based models to an inviscid flow transonic wing configuration and reduced the CFD model with over three quarters of a million degrees of freedom to a system with just a few dozen degrees of freedom, while still retaining the accuracy of the unsteady aerodynamics of the full system r”
The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows (2012, unknown): “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].”