Adaptive data-driven model order reduction for unsteady aerodynamics

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Abstract

A data-driven adaptive reduced order modelling approach is presented for the reconstruction of impulsively started and vortex-dominated flows. A residual-based error metric is presented for the first time in the framework of the adaptive approach. The residual-based adaptive Reduced Order Modelling selects locally in time the most accurate reduced model approach on the basis of the lowest residual produced by substituting the reconstructed flow field into a finite volume discretisation of the Navier−Stokes equations. A study of such an error metric was performed to assess the performance of the resulting residual-based adaptive framework with respect to a single-ROM approach based on the classical proper orthogonal decomposition, as the number of modes is varied. Two- and three-dimensional unsteady flows were considered to demonstrate the key features of the method and its performance.
Original languageEnglish
Article number130
Number of pages30
JournalFluids
Volume7
Issue number4
Early online date6 Apr 2022
DOIs
Publication statusPublished - 6 Apr 2022

Keywords

  • data-driven reduced order modelling
  • unsteady aerodynamics
  • vortex-dominated flows

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