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 ⊂ R2 and the manifold M= \ uμ : μ ∈ P \ associated with the parameter domain P ⊂ RP and the parametric field μ uμ ∈ L2(), our approach takes as input a set of snapshots \ uk \k=1n train ⊂ M and returns a parameter-dependent bijective mapping : × P R2: the mapping is designed to make the mapped manifold \ uμ μ: \, μ ∈ 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 \ q \q=1 N dd of the domain such that 1,…,N 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, a potential flow past a rotating symmetric airfoil, and an inviscid transonic compressible flow past a non-symmetric airfoil, to demonstrate the effectiveness of our method.

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