Towards prediction of turbulent flows at high Reynolds numbers using high performance computing data and deep learning
Fluid Dynamics
2022-10-31 v1 Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2210.16110,
title = {Towards prediction of turbulent flows at high Reynolds numbers using high performance computing data and deep learning},
author = {Mathis Bode and Michael Gauding and Jens Henrik Göbbert and Baohao Liao and Jenia Jitsev and Heinz Pitsch},
journal= {arXiv preprint arXiv:2210.16110},
year = {2022}
}