Data-Driven Aerodynamic Modeling Using the DLR SMARTy Toolbox

Abstract

From aircraft design to certification a huge amount of aerodynamic data is needed for the entire flight envelope including pressure and shear stress distributions, global coefficients as well as derivatives. The goal of data-driven methods is to provide aerodynamic data based on various data-sources but with lower evaluation time and storage than the original models. These data-sources might include flight tests, wind tunnel experiments or numerical simulations, and they are often available at various levels of fidelity, ranging from simple hand book methods over high-fidelity numerical simulations to in-flight measurements. Within the past few years, the demand for efficient exploitation and exploration of these data sets became evident to further enhance existing designs and approaches, evaluate new technical capabilities and foster the availability of high-fidelity aerodynamic data in closely related disciplines. The German Aerospace Center is continuously developing the Surrogate Modeling for Aero-Data Toolbox in python (SMARTy) with the aim of providing state-of-the-art data-driven techniques for both, developers and practical engineers. SMARTy is designed following an Application Programming Interface approach that enables easy combination of different modules into larger, complex applications. Moreover, integration into multi-disciplinary analysis and optimization workflows is possible relying on the FlowSimulator. The SMARTy capabilities are highlighted herein by means of several application cases. This includes surrogate modeling, multi-fidelity modeling, data fusion, reduced order modeling, deep learning as well as highly integrated tasks such as surrogate-based robust design, intrusive reduced order modeling for unsteady responses or data-driven turbulence modeling.