Nonlinear model-order reduction for compressible flow solvers using the Discrete Empirical Interpolation Method

Miguel Fosas de Pando, Peter J. Schmid, Denis Sipp

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Nonlinear model reduction for large-scale flows is an essential component in many fluid applications such as flow control, optimization, parameter space exploration and statistical analysis. In this article, we generalize the POD–DEIM method, introduced by Chaturantabut & Sorensen [1], to address nonlocal nonlinearities in the equations without loss of performance or efficiency. The nonlinear terms are represented by nested DEIM-approximations using multiple expansion bases based on the Proper Orthogonal Decomposition. These extensions are imperative, for example, for applications of the POD–DEIM method to large-scale compressible flows. The efficient implementation of the presented model-reduction technique follows our earlier work [2] on linearized and adjoint analyses and takes advantage of the modular structure of our compressible flow solver. The efficacy of the nonlinear model-reduction technique is demonstrated to the flow around an airfoil and its acoustic footprint. We could obtain an accurate and robust low-dimensional model that captures the main features of the full flow.
Original languageEnglish (US)
Pages (from-to)194-209
Number of pages16
JournalJournal of Computational Physics
Volume324
DOIs
StatePublished - Nov 1 2016
Externally publishedYes

ASJC Scopus subject areas

  • Physics and Astronomy (miscellaneous)
  • Computer Science Applications

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