In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced order method is based on dynamic mode decomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) to enhance the future state prediction of the target function and reduce the parameter space dimensionality. The pipeline is based on high-fidelity simulations carried out by the application of finite volume method for turbulent flows, and automatic mesh morphing through radial basis functions interpolation technique. The proposed pipeline is able to save 1/3 of the overall computational resources thanks to the application of DMD. Moreover exploiting DyAS and performing the regression on a lower dimensional space results in the reduction of the relative error in the approximation of the time-varying lift coefficient by a factor 2 with respect to using only the DMD.

Enhancing CFD predictions in shape design problems by model and parameter space reduction / Tezzele, M.; Demo, N.; Stabile, G.; Mola, A.; Rozza, G.. - In: ADVANCED MODELING AND SIMULATION IN ENGINEERING SCIENCES. - ISSN 2213-7467. - 7:1(2020), pp. 1-19. [10.1186/s40323-020-00177-y]

Enhancing CFD predictions in shape design problems by model and parameter space reduction

Tezzele M.;Demo N.;Stabile G.;Mola A.;Rozza G.
2020-01-01

Abstract

In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced order method is based on dynamic mode decomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) to enhance the future state prediction of the target function and reduce the parameter space dimensionality. The pipeline is based on high-fidelity simulations carried out by the application of finite volume method for turbulent flows, and automatic mesh morphing through radial basis functions interpolation technique. The proposed pipeline is able to save 1/3 of the overall computational resources thanks to the application of DMD. Moreover exploiting DyAS and performing the regression on a lower dimensional space results in the reduction of the relative error in the approximation of the time-varying lift coefficient by a factor 2 with respect to using only the DMD.
2020
7
1
1
19
40
Tezzele, M.; Demo, N.; Stabile, G.; Mola, A.; Rozza, G.
File in questo prodotto:
File Dimensione Formato  
s40323-020-00177-y.pdf

accesso aperto

Descrizione: Open Access
Tipologia: Versione Editoriale (PDF)
Licenza: Creative commons
Dimensione 2.29 MB
Formato Adobe PDF
2.29 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11767/115042
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 25
  • ???jsp.display-item.citation.isi??? ND
social impact