Investigation of non-linear projection for POD based reduced order models for Aerodynamics

By Patrick LeGresley, Juan José Palacios Alonso. 2001

166 citations recorded by the source corpus.

Subfields: Computational Mechanics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/cd4b7d6b0f5ea5cd00b8c1f60884801e176afcc1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Marco Fossati
2. Roger Zimmermann
3. Tino Franz
4. S. Görtz
5. N. Karcher

In Statistical and Nonlinear Physics

1. Marco Fossati
2. Roger Zimmermann
3. Tino Franz
4. S. Görtz
5. N. Karcher

Citations in the corpus

• Evaluation of aerodynamic loads via reduced order methodology (2015, methodology): “Intrusive methods elaborate on the mathematics of the projection step to reformulate the original system of partial differential equations (PDEs) as a system of ordinary differential equations (ODEs), with the modal coefficients as unknowns [3–6].”
• Interpolation-based reduced-order modelling for steady transonic flows via manifold learning (2014, methodology): “…a Galerkin projection framework (POD-Galerkin) (Lucia, Beran, and Silva 2004), or it is combined with a CFD flux residual minimisation scheme (LeGresley and Alonso 2001; Zimmermann and Görtz 2012), or it is coupled with an interpolation method (POD+I) (Ly and Tran 2001; BuiThanh, Damodaran,…”