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Turbulent flow consists of structures with a wide range of spatial andtemporal scales which are hard to resolve numerically. Classical numericalmethods as the Large Eddy Simulation (LES) are able to capture fine details ofturbulent structures but come at high computational cost. Applying generativeadversarial networks (GAN) for the synthetic modeling of turbulence is amathematically well-founded approach to overcome this issue. In this work, weinvestigate the generalization capabilites of GAN-based synthetic turbulencegenerators when geometrical changes occur in the flow configuration (e.g.aerodynamic geometric optimization of structures such as airfoils). As trainingdata, we use the flow around a low-pressure turbine (LPT) stator with periodicwake impact obtained from highly resolved LES. To simulate the flow around aLPT stator, we use the conditional deep convolutional GAN framework pix2pixHDconditioned on the position of a rotating wake in front of the stator. For thegeneralization experiments we exclude images of wake positions located atcertain regions from the training data and use the unseen data for testing. Weshow the abilities and limits of generalization for the conditional GAN byextending the regions of the extracted wake positions successively. Finally, weevaluate the statistical properties of the synthesized flow field by comparisonwith the corresponding LES results.

Generalization capabilities of conditional GAN for turbulent flow under changes of geometry