Reduced-Order Modeling for Aerodynamic Applications and MDO (Dr. Stefan Görtz)

This lecture was given by Dr. Stefan Görtz, German Aerospace Center (DLR), Germany in the framework of the von Karman Lecture Series on Machine Learning for Fluid Mechanics organized by the von Karman Institute and the Université libre de Bruxelles in February 2020.
Parametric reduced order models (ROMs) for both steady and unsteady aerodynamic applications are presented. The focus is on compressible, turbulent flows with shocks. We consider ROMs combining proper orthogonal decomposition (POD), Isomap, which is a manifold learning method, and autoencoder networks with interpolation methods as well as physics-based ROMs, where an approximate solution is found in the POD-subspace by minimizing the corresponding steady or unsteady flow-solver residual. The ROMs are used to predict unsteady gusts loads for rigid aircraft as well as static aeroelastic loads in the context of multidisciplinary design optimization (MDO) where the structural model is to be sized for the (aerodynamic) loads. They are also used in a process where an a priori identification of the critical load cases is of interest and the sheer number of load cases to be considered does not lend itself to high-fidelity CFD. The different ROM methods are applied to 2D and 3D test cases at transonic flow conditions where shock waves occur and in particular to a commercial full aircraft configuration.