基于卷积神经网络的大型曲流河三维桥墩洪水预报
Prediction of three-dimensional flood-flow past bridge piers in a large-scale meandering river using convolutional neural networks
摘要：
大尺度河流湍流统计特性的预测是河流水流分析的重要内容。大涡模拟（LESs）为这类预测提供了强有力的工具，然而，它需要很长的采样时间和大量的计算能力来计算河流水流的湍流统计。在这项研究中，我们开发了编码器-解码器卷积神经网络（CNN）预测的一阶和二阶湍流统计的大规模弯曲河流的湍流。我们使用从具有三个桥墩的大型河流中的洪水流的LES获得的数据集来训练CNN，该数据集形成了训练测试床。随后，我们采用经过训练的CNN来预测具有不同桥墩布置的河流中洪水流量的湍流统计数据，从而形成验证测试平台。CNN预测的验证试验台河流流量进行了比较，一个单独执行LES的模拟结果，以评估开发的CNN的性能。结果表明，经过训练的CNN可以成功地产生大规模河流中洪水流量的湍流统计数据，例如用于验证试验台的数据。
英文摘要：
The prediction of statistical properties of turbulent flow in large-scale rivers is important for river flow analysis. Large-eddy simulations (LESs) provide a powerful tool for such predictions, however, they require a very long sampling time and significant computing power to calculate the turbulence statistics of riverine flows. In this study, we developed encoder-decoder convolutional neural networks (CNNs) to predict the first- and second-order turbulent statistics of the turbulent flow of large-scale meandering rivers. We trained the CNNs using a dataset obtained from the LES of the flood flow in a large-scale river with three bridge piers, which formed the training testbed. Subsequently, we employed the trained CNNs to predict the turbulent statistics of the flood flow in a river with different bridge pier arrangements, which formed the validation testbed. The CNN predictions for the validation testbed river flow were compared with the simulation results of a separately performed LES to evaluate the performance of the developed CNNs. The results showed that the trained CNNs can successfully produce turbulent statistics of the flood flow in large-scale rivers, such as that chosen for the validation testbed.
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