Model order reduction in aerodynamics: review and applications

Gonçalo Mendonça, Frederico Afonso, Fernando Lau

Research output: Contribution to journalReview article

Abstract

The need of the aerospace industry, at national or European level, of faster yet reliable computational fluid dynamics models is the main drive for the application of model reduction techniques. This need is linked to the time cost of high-fidelity models, rendering them inefficient for applications like multi-disciplinary optimization. With the goal of testing and applying model reduction to computational fluid dynamics models applicable to lifting surfaces, a bibliographical research covering reduction of nonlinear, dynamic, or steady models was conducted. This established the prevalence of projection and least mean squares methods, which rely on solutions of the original high-fidelity model and their proper orthogonal decomposition to work. Other complementary techniques such as adaptive sampling, greedy sampling, and hybrid models are also presented and discussed. These projection and least mean squares methods are then tested on simple and documented benchmarks to estimate the error and speed-up of the reduced order models thus generated. Dynamic, steady, nonlinear, and multiparametric problems were reduced, with the simplest version of these methods showing the most promise. These methods were later applied to single parameter problems, namely the lid-driven cavity with incompressible Navier–Stokes equations and varying Reynolds number, and the elliptic airfoil at varying angles of attack for compressible Euler flow. An analysis of the performance of these methods is given at the end of this article, highlighting the computational speed-up obtained with these techniques, and the challenges presented by multiparametric problems and problems showing point singularities in their domain.

Original languageEnglish
Pages (from-to)5816-5836
Number of pages21
JournalProceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering
Volume233
Issue number15
Early online date11 Jun 2019
DOIs
Publication statusPublished - 1 Dec 2019

Keywords

  • computational fluid dynamics
  • Galerkin projection
  • least mean squares reduction
  • model order reduction
  • proper orthogonal decomposition

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