基于深度神经网络的流场时空重构方法

针对流场粒子图像测速实验中时间和空间高分辨率测量代价高的问题，研究了数据驱动的流场时空重构方法。为了对实验测得的低分辨率数据进行时空高分辨率重构，提出了一种基于深度神经网络的流场时空重构方法，并构建了一种基于卷积神经网络和长短时记忆神经网络的混合深度神经网络。该混合深度神经网络能够学习流场的时空演化特征，训练完成后可实现对实验数据的时空高分辨率重构。测试结果表明:只进行流场空间高分辨率重构时，重构出的流场与真实流场之间的均方根误差为0.0065左右，流场数据点数是原来的51倍；同时进行流场时间和空间高分辨率重构时，重构出的流场与真实流场之间的均方根误差可保持在0.065左右，流场时间维度的密度是原来的5倍，可极大提高实验效率，节约实验成本。

Spatio-temporal reconstruction method of flow field based on deep neural network

The flow field PIV measurement method cost a lot, but the measurement results have low spatial and temporal resolution. The spatio-temporal reconstruction method of flow field based on experimental and numerical simulation data is studied. In order to realize the high-resolution spatio-temporal reconstruction of the experimentally measured low-resolution data, a flow field spatio-temporal reconstruction method based on deep neural network is proposed. A hybrid deep neural network based on convolutional neural network and long-short-term memory neural network is constructed. This hybrid deep neural network is trained to learn the spatio-temporal evolution features of the flow field. After the training is completed, it can be used to reconstruct the experimental data into spatio-temporal high-resolution results. The test results show that when the spatial high-resolution reconstruction is performed alone, the mean square error between the reconstructed flow field and the ground truth flow field is about 0.0065, and the number of data points is 51 times more than that of the input field. When the flow field is reconstructed to high resolution in time and space at the same time, the mean square error be maintained at about 0.065, and the density in the time dimension is 5 times more than that of the input field. It is proved that this method can greatly improve the efficiency of the experiment and save the cost of the experiment.