论文标题
壳模型湍流闭合的数值证明
A Numerical Proof of Shell Model Turbulence Closure
论文作者
论文摘要
湍流封闭模型的发展，参数化小型非分辨量表对大型解决方案动力学的影响，是一个杰出的理论挑战，具有广泛的应用相关性。我们提出了一个基于深层复发性神经网络的关闭，该封闭在统计误差，欧拉和拉格朗日结构函数以及能量级联的间歇统计范围内定量再现了，包括亚网格磁通量。为了达到高阶统计准确性，因此是严格的统计检验，我们采用了湍流模型。我们的结果鼓励开发3D Navier-Stokes湍流的类似方法。
The development of turbulence closure models, parametrizing the influence of small non-resolved scales on the dynamics of large resolved ones, is an outstanding theoretical challenge with vast applicative relevance. We present a closure, based on deep recurrent neural networks, that quantitatively reproduces, within statistical errors, Eulerian and Lagrangian structure functions and the intermittent statistics of the energy cascade, including those of subgrid fluxes. To achieve high-order statistical accuracy, and thus a stringent statistical test, we employ shell models of turbulence. Our results encourage the development of similar approaches for 3D Navier-Stokes turbulence.
