论文标题
基于机器学习的时空超级分辨率重建湍流
Machine learning based spatio-temporal super resolution reconstruction of turbulent flows
论文作者
论文摘要
我们提出了一种新的湍流数据重建方法，该方法具有受监督的机器学习技术，灵感来自超级分辨率和Inbetinging，该技术可以从时空和时间中从粗糙的粗流数据中恢复高分辨率的湍流。对于目前的基于机器学习的数据重建，我们使用基于卷积神经网络的下采样的跳过连接/多尺度模型，将流体流的多尺度性质纳入其网络结构中。作为一个初始示例，该模型应用于$ re_d $ = 100的二维圆柱唤醒。提出的方法通过提议的方法重构流场与通过直接数值模拟获得的参考证明非常一致。接下来，我们检查了所提出的模型对于二维衰减均质各向同性湍流的能力。根据湍流统计的评估，机器学习的模型可以遵循时空中的粗略输入数据的衰减演变。对于$re_τ$ = 180的三维域上的复杂的湍流通道流，进一步研究了提出的概念。本模型可以从空间中非常粗糙的输入数据中重建高分辨率的湍流，当适当选择时间间隔时，它也可以再现时间演变。还评估了基于时间两点相关系数的第一和最后框架之间训练快照和持续时间的依赖性，以揭示时空超级分辨率重建的能力和鲁棒性。这些结果表明，目前的方法可以符合一系列用于支持计算和实验努力的流动重建。
We present a new turbulent data reconstruction method with supervised machine learning techniques inspired by super resolution and inbetweening, which can recover high-resolution turbulent flows from grossly coarse flow data in space and time. For the present machine learning based data reconstruction, we use the downsampled skip-connection/multi-scale model based on a convolutional neural network to incorporate the multi-scale nature of fluid flows into its network structure. As an initial example, the model is applied to a two-dimensional cylinder wake at $Re_D$ = 100. The reconstructed flow fields by the proposed method show great agreement with the reference data obtained by direct numerical simulation. Next, we examine the capability of the proposed model for a two-dimensional decaying homogeneous isotropic turbulence. The machine-learned models can follow the decaying evolution from coarse input data in space and time, according to the assessment with the turbulence statistics. The proposed concept is further investigated for a complex turbulent channel flow over a three-dimensional domain at $Re_τ$ =180. The present model can reconstruct high-resolved turbulent flows from very coarse input data in space, and it can also reproduce the temporal evolution when the time interval is appropriately chosen. The dependence on the amount of training snapshots and duration between the first and last frames based on a temporal two-point correlation coefficient are also assessed to reveal the capability and robustness of spatio-temporal super resolution reconstruction. These results suggest that the present method can meet a range of flow reconstructions for supporting computational and experimental efforts.
