Within turbulent boundary layers, the relationship between instantaneous surface momentum fluxes and streamwise velocities is more complicated than that between their ensemble averages described by the law of the wall. Although these fluxes need to be considered in large eddy simulations, the conventional approaches are not feasible. As an alternative, we have developed a deep neural network with the long short-term memory algorithmthat estimates instantaneous fluxes from a sequence of streamwise velocities. The velocities measured in a wind tunnel were used for training and validation. The trained deep neural network successfully estimates the instantaneous surface momentum flux with a suitable running average.

中文翻译：

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使用深度神经网络估算边界层中的瞬时表面动量通量

在湍流边界层内，瞬时表面动量通量和水流速度之间的关系比其壁面定律所描述的整体平均水平之间的关系更为复杂。尽管在大型涡流模拟中需要考虑这些通量，但是常规方法是不可行的。作为替代方案，我们开发了具有长短期记忆算法的深层神经网络，该算法可根据一系列流向速度估算瞬时通量。在风洞中测得的速度用于训练和验证。训练有素的深度神经网络以合适的运行平均值成功估算了瞬时表面动量通量。