Airfoil design optimization using reduced order models based on proper orthogonal decomposition

By Patrick LeGresley, Juan José Palacios Alonso. 2000

178 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/2abe64371577d794bea2ec0e90de6d7dd950b421

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In Computational Mechanics

In Statistical and Nonlinear Physics

In Statistics and Probability

Citations in the corpus

Active Subspaces of Airfoil Shape Parameterizations (2017, background): “The proper orthogonal decomposition (POD) is another type of subspace-based dimension reduction that is often used in design problems [29].”
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”
Geometric Filtration Using Proper Orthogonal Decomposition for Aerodynamic Design Optimization (2010, methodology): “It has been used in the derivation of reduced-order models for the purposes of control [20–23] and optimization [24–26].”
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.”
Reduced Basis Approximation and A Posteriori Error Estimation for Parametrized Partial Differential Equations (2007, methodology): “(We note that the greedy sampling methods described in this book are similar in objective to, but very different in approach from, more well-known Proper Orthogonal Decomposition (POD) methods [8, 23, 58, 75, 77, 97, 126, 127, 128, 142, 143, 156] typically applied in the temporal domain.”