Non-Linear POD-based Reduced Order Models for Steady Turbulent Aerodynamics

Kurzfassung

A reduced order modelling approach for predicting steady aerodynamic flows based on Computational Fluid Dynamics (CFD) and global Proper Orthogonal Decomposition (POD) using a suitable data transformation for obtaining problem-adapted global basis modes is presented. Model-order reduction is achieved by parameter space sampling, reduced solution space representation via global POD and restriction of a CFD solver to the reduced POD subspace. Solving the governing equations of fluid dynamics is replaced by solving a non-linear least-squares optimization problem. The method is demonstrated by computing reduced-order solutions to the compressible Navier-Stokes equations for an industrial aircraft conguration based on a set of subsonic CFD solutions. Results are shown to be more accurate than those obtained by POD-based interpolation using Kriging and the Thin Plate Spline method (TPS) when compared to reference CFD solutions. The cost of computing the reduced-order solution is two orders of magnitude lower than that of computing a CFD solution.