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

JOURNAL OF COMPUTATIONAL PHYSICS

卷:324

Article

Fosas de Pando, Miguel¹  Schmid, Peter J.²  Sipp, Denis³ 

[1] Univ Cadiz, Escuela Super Ingn, Dept Ingn Mecan & Diseno Ind, Av Univ Cadiz 10, Puerto Real 11519, Spain

[2] Imperial Coll London, Dept Math, London SW7 2AZ, England

[3] ONERA DAFE, 8 Rue Vertugadins, F-92190 Meudon, France

关键词: Reduced-order models;    Discrete empirical interpolation method;    Proper orthogonal decomposition;    Compressible flows;    Aeroacoustics;   

DOI  :  10.1016/j.jcp.2016.08.004

来源: Elsevier

【 摘 要 】

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. (C) 2016 Elsevier Inc. All rights reserved.

【 授权许可】

Free