Geometrically parametrized Partial Differential Equations are nowadays widely used in many different fields as, for example, shape optimization processes or patient specific surgery studies. The focus of this work is on some advances for this topic, capable of increasing the accuracy with respect to previous approaches while relying on a high cost-benefit ratio performance. The main scope of this paper is the introduction of a new technique mixing up a classical Galerkin-projection approach together with a data-driven method to obtain a versatile and accurate algorithm for the resolution of geometrically parametrized incompressible turbulent Navier-Stokes problems. The effectiveness of this procedure is demonstrated on two different test cases: a classical academic back step problem and a shape deformation Ahmed body application. The results show into details the properties of the architecture we developed while exposing possible future perspectives for this work.

Hybrid Neural Network Reduced Order Modelling for Turbulent Flows with Geometric Parameters / Zancanaro, Matteo; Mrosek, Markus; Stabile, Giovanni; Othmer, Carsten; Rozza, Gianluigi. - In: FLUIDS. - ISSN 2311-5521. - 6:8(2021), pp. 1-21. [10.3390/fluids6080296]

Hybrid Neural Network Reduced Order Modelling for Turbulent Flows with Geometric Parameters

Zancanaro, Matteo;Stabile, Giovanni
;
Rozza, Gianluigi
2021-01-01

Abstract

Geometrically parametrized Partial Differential Equations are nowadays widely used in many different fields as, for example, shape optimization processes or patient specific surgery studies. The focus of this work is on some advances for this topic, capable of increasing the accuracy with respect to previous approaches while relying on a high cost-benefit ratio performance. The main scope of this paper is the introduction of a new technique mixing up a classical Galerkin-projection approach together with a data-driven method to obtain a versatile and accurate algorithm for the resolution of geometrically parametrized incompressible turbulent Navier-Stokes problems. The effectiveness of this procedure is demonstrated on two different test cases: a classical academic back step problem and a shape deformation Ahmed body application. The results show into details the properties of the architecture we developed while exposing possible future perspectives for this work.
2021
6
8
1
21
296
10.3390/fluids6080296
https://arxiv.org/abs/2107.09591
Zancanaro, Matteo; Mrosek, Markus; Stabile, Giovanni; Othmer, Carsten; Rozza, Gianluigi
File in questo prodotto:
File Dimensione Formato  
fluids-06-00296-v2.pdf

accesso aperto

Descrizione: pdf editoriale
Tipologia: Versione Editoriale (PDF)
Licenza: Creative commons
Dimensione 3.07 MB
Formato Adobe PDF
3.07 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/20.500.11767/124193
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 10
  • ???jsp.display-item.citation.isi??? 8
social impact