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Data-driven reduced order modeling for flows with moving geometries using shifted POD

Abstract : A new approach, based on the shifted Proper Orthogonal Decomposition (sPOD), for modelling and optimizing flows with moving geometries is presented. This novel model order reduction technique is shown to be efficient for advection dominated systems and its approximation properties are assessed. Moreover its applicability for path optimization of moving geometries will be shown.
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https://hal.archives-ouvertes.fr/hal-03396325
Contributor : Kai Schneider Connect in order to contact the contributor
Submitted on : Friday, October 22, 2021 - 4:32:56 PM
Last modification on : Tuesday, October 26, 2021 - 4:00:32 AM

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  • HAL Id : hal-03396325, version 1

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Philipp Krah, Miriam Goldack, Thomas Engels, Kai Schneider, Julius Reiss. Data-driven reduced order modeling for flows with moving geometries using shifted POD. 10th Vienna International Conference on Mathematical Modelling, (MATHMOD 2022), Feb 2022, Vienna, Austria. ⟨hal-03396325⟩

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