Hybrid neural network reduced order modelling for turbulent flows with geometric parameters
This work addresses shape optimization and patient-specific studies in fluid dynamics, but it is incremental as it builds on existing reduced order modeling techniques.
The paper tackled geometrically parametrized turbulent flow problems by introducing a hybrid neural network reduced order model that combines Galerkin-projection with data-driven methods, achieving increased accuracy and a high cost-benefit ratio in test cases like a back step problem and an Ahmed body application.
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.