Lassila, T orcid.org/0000-0001-8947-1447, Manzoni, A, Quarteroni, A et al. (1 more author) (2014) Model Order Reduction in Fluid Dynamics: Challenges and Perspectives. In: Quarteroni, A and Rozza, G, (eds.) Reduced Order Methods for Modeling and Computational Reduction. MS&A - Modeling, Simulation and Applications, 9 . Springer , Cham, Germany , pp. 235-273. ISBN 978-3-319-02089-1
Abstract
This chapter reviews techniques of model reduction of fluid dynamics systems. Fluid systems are known to be difficult to reduce efficiently due to several reasons. First of all, they exhibit strong nonlinearities — which are mainly related either to nonlinear convection terms and/or some geometric variability — that often cannot be treated by simple linearization. Additional difficulties arise when attempting model reduction of unsteady flows, especially when long-term transient behavior needs to be accurately predicted using reduced order models and more complex features, such as turbulence or multiphysics phenomena, have to be taken into consideration. We first discuss some general principles that apply to many parametric model order reduction problems, then we apply them on steady and unsteady viscous flows modelled by the incompressible Navier-Stokes equations. We address questions of inf-sup stability, certification through error estimation, computational issues and — in the unsteady case — long-time stability of the reduced model. Moreover, we provide an extensive list of literature references.
Metadata
Item Type: | Book Section |
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Copyright, Publisher and Additional Information: | © 2014, Springer International Publishing Switzerland. This is a post-peer-review, pre-copyedit version of an paper published in Reduced Order Methods for Modeling and Computational Reduction. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-02090-7_9. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Proper Orthogonal Decomposition; Proper Orthogonal Decomposition Mode; Posteriori Error Estimation; Reduce Basis; Galerkin Projection |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 08 Nov 2018 09:26 |
Last Modified: | 08 Nov 2018 09:26 |
Status: | Published |
Publisher: | Springer |
Series Name: | MS&A - Modeling, Simulation and Applications |
Identification Number: | 10.1007/978-3-319-02090-7_9 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:137452 |