Deep Learning Methods for Reynolds-Averaged Navier–Stokes Simulations of Airfoil Flows

This study investigates the accuracy of deep learning models for the inference of Reynolds-averaged Navier–Stokes (RANS) solutions. This study focuses on a modernized U-net architecture and evaluates a large number of trained neural networks with respect to their accuracy for the calculation of pressure and velocity distributions. In particular, it is illustrated how training data size and the number of weights influence the accuracy of the solutions. With the best models, this study arrives at a mean relative pressure and velocity error of less than 3% across a range of previously unseen airfoil shapes. In addition all source code is publicly available in order to ensure reproducibility and to provide a…

• HE
  H2020 European Research Council

  Award: 637014