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

• Main Authors: Matteo Zancanaro, Markus Mrosek, Giovanni Stabile, Carsten Othmer, Gianluigi Rozza
• Format: Article
• Language: English
• Published: MDPI AG 2021-08-01
• Series: Fluids
• Subjects: reduced order models; geometrical parametrization; projection-based methods; data-driven approaches; turbulence closures; mesh motion
• Online Access: https://www.mdpi.com/2311-5521/6/8/296

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.