Reduced-Order Modeling of Vehicle Aerodynamics via Proper Orthogonal Decomposition
Abstract Aerodynamic optimization of the exterior vehicle shape is a highly multidisciplinary task involving, among others, styling and aerodynamics. The often differing priorities of these two disciplines give rise to iterative loops between stylists and aerodynamicists. Reduced-order modeling (ROM) has the potential to shortcut these loops by enabling aerodynamic evaluations in real time. In this study, we aim to assess the performance of ROM via proper orthogonal decomposition (POD) for a real-life industrial test case, with focus on the achievable accuracy for the prediction of fields and aerodynamic coefficients. To that end, we create a training data set based on a six-dimensional parameterization of a Volkswagen passenger production car by computing 100 variants with Detached-Eddy simulations (DES).