Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
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DOI：
10.1017/jfm.2020.948
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发表时间：
2021-02-25
影响因子：
3.7
通讯作者：
Taira, Kunihiko
中科院分区：
文献类型：
--
作者：
Fukami, Kai;Fukagata, Koji;Taira, Kunihiko
我们提出了一种新的数据重建方法与监督机器学习技术的灵感来自超分辨率和中间恢复高分辨率湍流从空间和时间上的粗糙流数据。对于目前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跳跃连接/多尺度模型，将流体流动的多尺度性质纳入其网络结构。作为初始例子，该模型被应用于二维圆柱尾迹。重建的流场与直接数值模拟得到的参考数据吻合较好。接下来，我们将当前模型应用于二维衰减均匀各向同性湍流。机器学习模型能够跟踪来自空间和时间粗输入数据的衰减演化。提出的概念进一步应用到一个复杂的湍流通道流动的三维区域在。本模型重建高分辨率的湍流从非常粗糙的输入数据在空间中，也再现了适当选择的时间间隔的时间演变。基于时间两点相关系数的训练快照的数量和第一和最后帧之间的持续时间的依赖性也被评估，以揭示时空超分辨率重建的能力和鲁棒性。这些结果表明，本方法可以进行一系列的流动重建，以支持计算和实验的努力。
We present a new data reconstruction method with supervised machine learning techniques inspired by super resolution and inbetweening to 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/multiscale model based on a convolutional neural network, incorporating the multiscale nature of fluid flows into its network structure. As an initial example, the model is applied to the two-dimensional cylinder wake at . The reconstructed flow fields by the present method show great agreement with the reference data obtained by direct numerical simulation. Next, we apply the current model to a two-dimensional decaying homogeneous isotropic turbulence. The machine-learned model is able to track the decaying evolution from spatial and temporal coarse input data. The proposed concept is further applied to a complex turbulent channel flow over a three-dimensional domain at . The present model reconstructs high-resolved turbulent flows from very coarse input data in space, and also reproduces the temporal evolution for appropriately chosen time interval. The dependence on the number 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 perform a range of flow reconstructions in support of computational and experimental efforts.