Towards Prediction of Turbulent Flows at High Reynolds Numbers Using High Performance Computing Data and Deep Learning
In this paper, deep learning (DL) methods are evaluated in the context of turbulent flows. Various generative adversarial networks (GANs) are discussed with respect to their suitability for understanding and modeling turbulence. Wasserstein GANs (WGANs) are then chosen to generate small-scale turbulence. Highly resolved direct numerical simulation (DNS) turbulent data is used for training the WGANs and the effect of network parameters, such as learning rate and loss function, is studied. Qualitatively good agreement between DNS input data and generated turbulent structures is shown. A quantitative statistical assessment of the predicted turbulent fields is performed.
KeywordsTurbulence High Reynolds number Deep learning Wasserstein generative adversarial networks Direct numerical simulation
The authors gratefully acknowledge the computing time granted for the project JHPC55 by the JARA-HPC Vergabegremium and provided on the JARA-HPC Partition part of the supercomputer JURECA at Forschungszentrum Jülich. Also, the computing time granted for the projects HFG00/HFG02 on the supercomputer JUQUEEN  at Forschungszentrum Jülich is acknowledged. MG acknowledges financial support by Labex EMC3, under the grant VAVIDEN.
- 1.Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv:1701.07875v3 (2017)
- 7.Goodfellow, I.J., et al.: Generative adversarial networks. arXiv:1406.2661 (2014)
- 8.Jülich Supercomputing Centre: JUQUEEN: IBM Blue Gene/Q supercomputer system at the Jülich supercomputing centre. J. Large-Scale Res. Facil. 1 (2015)Google Scholar