使用神经网络直接位移场估计进行流体图像测速
Image Velocimetry using Direct Displacement Field estimation with Neural Networks for Fluids
AI总结：
本研究提出一种基于神经网络和光流方程的流体图像测速新方法，无需预训练即可在任意图像对上预测全分辨率位移场，并在合成与实验图像上验证了其准确性与适用性。
摘要：
实验流体力学研究的重要工具是粒子图像测速（PIV）。已有多种稳健的方法被提出用于从图像中估计速度场，但仍需要替代方法来提高结果的空间分辨率。本研究提出了一种新方法，利用神经网络和光流方程预测连续图像间的位移向量，从而估计流体流场。其结果是位移的连续表示，可以在图像的全空间分辨率上进行评估。该方法在合成图像和实验图像上进行了验证。在瞬时速度场估计、确定的时间平均湍流量和功率谱密度方面均获得了准确的结果。所提方法不同于以往将机器学习用于此任务的尝试：它不需要任何先前的训练，可以直接用于任意一对图像。
英文摘要：
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.
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