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Article

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

1
MathLab, Mathematics Area, SISSA, International School for Advanced Studies, Via Bonomea 265, I-34136 Trieste, Italy
2
Volkswagen AG, Innovation Center Europe, 38436 Wolfsburg, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Robert Martinuzzi
Fluids 2021, 6(8), 296; https://doi.org/10.3390/fluids6080296
Received: 30 June 2021 / Revised: 5 August 2021 / Accepted: 11 August 2021 / Published: 22 August 2021
(This article belongs to the Special Issue Reduced Order Models for Computational Fluid Dynamics)
Geometrically parametrized partial differential equations are currently widely used in many different fields, such as shape optimization processes or patient-specific surgery studies. The focus of this work is some advances on 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 combining 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 provide insight into details about the properties of the architecture we developed while exposing possible future perspectives for this work. View Full-Text
Keywords: reduced order models; geometrical parametrization; projection-based methods; data-driven approaches; turbulence closures; mesh motion; automotive reduced order models; geometrical parametrization; projection-based methods; data-driven approaches; turbulence closures; mesh motion; automotive
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MDPI and ACS Style

Zancanaro, M.; Mrosek, M.; Stabile, G.; Othmer, C.; Rozza, G. Hybrid Neural Network Reduced Order Modelling for Turbulent Flows with Geometric Parameters. Fluids 2021, 6, 296. https://doi.org/10.3390/fluids6080296

AMA Style

Zancanaro M, Mrosek M, Stabile G, Othmer C, Rozza G. Hybrid Neural Network Reduced Order Modelling for Turbulent Flows with Geometric Parameters. Fluids. 2021; 6(8):296. https://doi.org/10.3390/fluids6080296

Chicago/Turabian Style

Zancanaro, Matteo, Markus Mrosek, Giovanni Stabile, Carsten Othmer, and Gianluigi Rozza. 2021. "Hybrid Neural Network Reduced Order Modelling for Turbulent Flows with Geometric Parameters" Fluids 6, no. 8: 296. https://doi.org/10.3390/fluids6080296

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