论文标题
使用高性能计算数据和深度学习来预测高雷诺数的湍流
Towards prediction of turbulent flows at high Reynolds numbers using high performance computing data and deep learning
论文作者
论文摘要
在本文中，深度学习（DL）方法是在湍流中评估的。讨论了各种生成对抗网络（GAN），讨论了它们的适合理解和建模湍流。然后选择Wasserstein Gans（WGAN）以产生小规模的湍流。研究了高度分辨的直接数值模拟（DNS）湍流数据用于训练WGAN，并且研究了网络参数的效果，例如学习率和损失函数。显示了DNS输入数据和生成的湍流结构之间的良好一致性。对预测的湍流场进行定量统计评估。
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.
