标题： 基于神经网络的直接位移场估计的流体图像测速法 显示英文标题

标题： Image Velocimetry using Direct Displacement Field estimation with Neural Networks for Fluids

摘要： 实验流体力学研究的重要工具是粒子图像测速技术（PIV）。 已经提出了几种稳健的方法来从图像中估计速度场，然而，仍需要其他方法以提高结果的空间分辨率。 本工作提出了一种新方法，使用神经网络和光学流动方程来预测连续图像之间的位移矢量。 结果是对位移的连续表示，可以在图像的完整空间分辨率上进行评估。 该方法在合成和实验图像上进行了验证。 在瞬时速度场的估计以及确定的时间平均湍流量和功率谱密度方面获得了准确的结果。 所提出的方法与之前尝试使用机器学习完成此任务的方法不同：它不需要任何先前的训练，并且可以直接用于任何一对图像。 显示英文摘要

摘要： An important tool for experimental fluids mechanics research is Particle Image Velocimetry (PIV). Several robust methodologies have been proposed to perform the estimation of velocity field from the images, however, alternative methods are still needed to increase the spatial resolution of the results. This work presents a novel approach for estimating fluid flow fields using neural networks and the optical flow equation to predict displacement vectors between sequential images. The result is a continuous representation of the displacement, that can be evaluated on the full spatial resolution of the image. The methodology was validated on synthetic and experimental images. Accurate results were obtained in terms of the estimation of instantaneous velocity fields, and of the determined time average turbulence quantities and power spectral density. The methodology proposed differs of previous attempts of using machine learning for this task: it does not require any previous training, and could be directly used in any pair of images.