Title | An Online Stabilization Method for Parametrized Viscous Flows |
Publication Type | Book Chapter |
Year of Publication | 2024 |
Authors | Ali, S, Ballarin, F, Rozza, G |
Book Title | Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators. |
Publisher | Springer, Cham |
ISBN Number | 978-3-031-55060-7 |
Abstract | The purpose of this work is to investigate the inf-sup stability of reduced basis (RB) method applied to parametric Stokes problem. While performing the Galerkin projection on the reduced space, the inf-sup approximation stability has always been a challenge for the RB community, even if the construction of reduced basis is done using a stable high-fidelity method. In this work we propose a new online stabilization strategy for RB approximation of parametrized Stokes problem. In this strategy, a stable high-fidelity method is used to construct the RB spaces, and then, online solution is improved by a post processing based on rectification method [8, 13, 16]. This approach involves the computation of less expensive (but less consistent) FE approximation during the online stage and hence the improvement of online solutions using a RB-based rectification method. The consistency of the RB solution is also improved. We compare this approach with existing offline-online stabilization approach presented in our earlier work [2]. All the numerical simulations are carried out using RBniCS [4, 14], an open-source reduced order modelling library, built on top of FEniCS [15]. |
URL | https://link.springer.com/chapter/10.1007/978-3-031-55060-7_1 |
DOI | 10.1007/978-3-031-55060-7_1 |
An Online Stabilization Method for Parametrized Viscous Flows
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