  Turbulent flow consists of structures with a wide range of spatial and
temporal scales which are hard to resolve numerically. Classical numerical
methods as the Large Eddy Simulation (LES) are able to capture fine details of
turbulent structures but come at high computational cost. Applying generative
adversarial networks (GAN) for the synthetic modeling of turbulence is a
mathematically well-founded approach to overcome this issue. In this work, we
investigate the generalization capabilites of GAN-based synthetic turbulence
generators when geometrical changes occur in the flow configuration (e.g.
aerodynamic geometric optimization of structures such as airfoils). As training
data, we use the flow around a low-pressure turbine (LPT) stator with periodic
wake impact obtained from highly resolved LES. To simulate the flow around a
LPT stator, we use the conditional deep convolutional GAN framework pix2pixHD
conditioned on the position of a rotating wake in front of the stator. For the
generalization experiments we exclude images of wake positions located at
certain regions from the training data and use the unseen data for testing. We
show the abilities and limits of generalization for the conditional GAN by
extending the regions of the extracted wake positions successively. Finally, we
evaluate the statistical properties of the synthesized flow field by comparison
with the corresponding LES results.
