Adaptive reduced order modelling for steady aerodynamics flows

Giuseppe Fortunato, Gaetano Pascarella, Gabriel R. Barrenechea, Marco Fossati

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

1 Citation (Scopus)


The present work introduces an Adaptive Reduced Order technique, that aims at reconstructing the flow field around lifting bodies by adaptively selecting a Reduced Order Method that provides the highest accuracy among the existing model reduction approaches. The proposed adaptive approach automatically selects the most accurate order reduction methods between the classical snapshot Proper Orthogonal Decomposition and the Isomap Manifold Learning. The choice of the best method is performed by estimating the reconstruction error as different reduced order solutions are used. Problems of relevance to the aerodynamics field are considered to evaluate the performances of the proposed approach such as the parametric study of airfoils and wings aerodynamic performance.

Original languageEnglish
Title of host publicationAIAA Scitech 2021 Forum
Place of PublicationReston, VA
Number of pages16
Publication statusPublished - 21 Jan 2021
EventAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021 - Virtual, Online
Duration: 11 Jan 202115 Jan 2021

Publication series

NameAIAA Scitech 2021 Forum


ConferenceAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021
CityVirtual, Online


  • aerodynamic loads
  • aerodynamic performance
  • lifting body
  • CFD simulation
  • high aspect ratio
  • flow conditions
  • Navier Stokes equations
  • Reynolds Averaged Navier Stokes (RANS) equations
  • supercritical airflows
  • singular value decomposition


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