论文标题
使用3D卷积神经网络揭示近壁湍流中动态关键区域
Uncovering dynamically critical regions in near-wall turbulence using 3D Convolutional Neural Networks
论文作者
论文摘要
墙壁结合的湍流中的近壁区域经历了慢速移动的流体数据包的间歇性弹射，从墙壁和更快地移动流体向墙壁上移开。这些极端事件在调节边界层的能源预算方面起着核心作用，并在三维（3D）卷积神经网络（CNN）的帮助下进行了分析。对CNN进行了从周期性通道流的直接数值模拟数据进行培训，以推断出此类极端事件的强度，更重要的是，揭示了流动中连续的三维显着结构，该结构是由网络自主确定的，对于射血事件的形成和演变至关重要。这些明显的区域使用多层梯度加权类激活映射（GARGCAM）技术进行重建，这与爆发的条纹和相干流体数据包良好相关。对网络学到的关联的可解释解释的关注还表明，弹出与湍流动能（TKE）生产的区域无关，而与负面产量相比，阳性产量极低的区域和阳性产量的趋势明显高于负生产。这是该研究的关键发现，并表明CNN可以使用提供的单个标量值计量度量来帮助揭示动态重要的三维显着区域，以此为兴趣量，在当前情况下，这是弹出强度。尽管目前的工作提出了分析与近壁爆发相关的非线性空间相关性的另一种方法，但提出的框架足够一般，以至于可以扩展到其他场景，在其他情况下，基本的空间动力学不知道A-Priori。
Near-wall regions in wall-bounded turbulent flows experience intermittent ejection of slow-moving fluid packets away from the wall and sweeps of faster moving fluid towards the wall. These extreme events play a central role in regulating the energy budget of the boundary layer, and are analyzed here with the help of a three-dimensional (3D) Convolutional Neural Network (CNN). A CNN is trained on Direct Numerical Simulation data from a periodic channel flow to deduce the intensity of such extreme events, and more importantly, to reveal contiguous three-dimensional salient structures in the flow that are determined autonomously by the network to be critical to the formation and evolution of ejection events. These salient regions, reconstructed using a multilayer Gradient-weighted Class Activation Mapping (GradCAM) technique proposed here, correlate well with bursting streaks and coherent fluid packets being ejected away from the wall. The focus on explainable interpretation of the network's learned associations also reveals that ejections are not associated with regions where turbulent kinetic energy (TKE) production reaches a maximum, but instead with regions that entail extremely low dissipation and a significantly higher tendency for positive TKE production than negative production. This is a key finding of the study, and indicates that CNNs can help reveal dynamically important three-dimensional salient regions using a single scalar-valued metric provided as the quantity of interest, which in the present case is the ejection intensity. While the current work presents an alternate means of analyzing nonlinear spatial correlations associated with near-wall bursts, the framework presented is sufficiently general so as to be extendable to other scenarios where the underlying spatial dynamics are not known a-priori.
