基于卷积神经网络的近壁流动高分辨率平均速度场预测方法

A high-resolution velocity field predicted method in near wall boundary layer by CNN

本文设计并验证了基于卷积神经网络的边界层近壁流动高分辨率平均速度场预测方法:首先采用示踪粒子图像对数据集训练卷积神经网络,通过调整神经网络参数可以预测示踪粒子在数据集上的平均跨帧位移;然后使用该卷积神经网络预测像素空间中各像素位置的单粒子位移,得到高分辨率的平均速度场信息。将该方法用于预测湍流脉动较小的边界层近壁区的平均流动,能够将空间分辨率提高到单像素精度。误差分析发现,该方法获得的测速精度略优于传统单像素系综平均互相关算法,且对粒子浓度和示踪粒子图像对数目的要求明显低于后者。

A high-resolution velocity field prediction method in the boundary layer based on CNN-PTV is designed and verified in this paper. This method includes two stages, training process and prediction process. In the training process, a CNN model is trained by the particle image pairs. By optimizing the parameters, the exact ensemble particle movements are predicted by the CNN model. In the prediction process, a synthetic image including just a single particle is imported to the CNN model to estimate the flow information at this particular pixel space. Thus,a high-resolution velocity field is predicted. Comparing with the single-pixel ensemble correlation method, the CNN-PTV method has a higher precision. And the results of CNN-PTV method is insensitive to the frame numbers and particle density.