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Journal Articles Journal of Scientific Computing Year : 2021

Registration-based model reduction in complex two-dimensional geometries

Abstract

We present a general -- i.e., independent of the underlying equation -- registration procedure for parameterized model order reduction. Given the spatial domain $\Omega \subset \mathbb{R}^2$ and the manifold $\mathcal{M}= \{ u_{\mu} : \mu \in \mathcal{P} \}$ associated with the parameter domain $\mathcal{P} \subset \mathbb{R}^P$ and the parametric field $\mu \mapsto u_{\mu} \in L^2(\Omega)$, our approach takes as input a set of snapshots $\{ u^k \}_{k=1}^{n_{\rm train}} \subset \mathcal{M}$ and returns a parameter-dependent bijective mapping $\underline{\Phi}: \Omega \times \mathcal{P} \to \mathbb{R}^2$: the mapping is designed to make the mapped manifold $\{ u_{\mu} \circ \underline{\Phi}_{\mu}: \, \mu \in \mathcal{P} \}$ more amenable for linear compression methods. In this work, we extend and further analyze the registration approach proposed in [Taddei, SISC, 2020]. The contributions of the present work are twofold. First, we extend the approach to deal with annular domains by introducing a suitable transformation of the coordinate system. Second, we discuss the extension to general two-dimensional geometries: towards this end, we introduce a spectral element approximation, which relies on a partition $\{ \Omega_{q} \}_{q=1} ^{N_{\rm dd}}$ of the domain $\Omega$ such that $\Omega_1,\ldots,\Omega_{N_{\rm dd}}$ are isomorphic to the unit square. We further show that our spectral element approximation can cope with parameterized geometries. We present rigorous mathematical analysis to justify our proposal; furthermore, we present numerical results for a heat-transfer problem in an annular domain and for a potential flow past a rotating airfoil to demonstrate the effectiveness of our method.

Dates and versions

hal-03121165 , version 1 (26-01-2021)

Identifiers

Cite

Tommaso Taddei, Lei Zhang. Registration-based model reduction in complex two-dimensional geometries. Journal of Scientific Computing, 2021, ⟨10.1007/s10915-021-01584-y⟩. ⟨hal-03121165⟩
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