Adaptive data-driven model order reduction for unsteady aerodynamics

Nagy, Peter and Fossati, Marco (2022) Adaptive data-driven model order reduction for unsteady aerodynamics. Fluids, 7 (4). 130. ISSN 2311-5521 (https://doi.org/10.3390/fluids7040130)

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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.

ORCID iDs

Nagy, Peter and Fossati, Marco ORCID logoORCID: https://orcid.org/0000-0002-1165-5825;
CORE (COnnecting REpositories)