论文标题
通过深度学习减少湍流模型
Turbulence model reduction by deep learning
论文作者
论文摘要
湍流理论的一个核心问题是为湍流产生预测模型。这些对湍流动态的几乎所有方面都有深远的影响。在磁性限制装置中，漂移波湍流通过波动之间的互相关产生异常通量。在这项工作中，我们引入了一种新的数据驱动方法，用于参数化这些通量。该方法使用深度监督的学习来从一组数值模拟中推断出减少的平均场模型。我们将该方法应用于简单的漂移湍流系统，并找到一个重大的新效果，该效果将粒子通量耦合到涡旋的局部\ emph {梯度}。值得注意的是，在这里，这种效果比经常弹性的剪切抑制作用要强得多。我们还通过简单的计算恢复结果。涡度梯度效应倾向于调节密度曲线。此外，我们的方法还通过负粘度恢复了自发性纬向流量产生的模型，该模型通过非线性和高度宣布的术语稳定。我们强调了对称性在实施新方法方面的重要作用。
A central problem of turbulence theory is to produce a predictive model for turbulent fluxes. These have profound implications for virtually all aspects of the turbulence dynamics. In magnetic confinement devices, drift-wave turbulence produces anomalous fluxes via cross-correlations between fluctuations. In this work, we introduce a new, data-driven method for parameterizing these fluxes. The method uses deep supervised learning to infer a reduced mean-field model from a set of numerical simulations. We apply the method to a simple drift-wave turbulence system and find a significant new effect which couples the particle flux to the local \emph{gradient} of vorticity. Notably, here, this effect is much stronger than the oft-invoked shear suppression effect. We also recover the result via a simple calculation. The vorticity gradient effect tends to modulate the density profile. In addition, our method recovers a model for spontaneous zonal flow generation by negative viscosity, stabilized by nonlinear and hyperviscous terms. We highlight the important role of symmetry to implementation of the new method.
