Deep learning for fluid velocity field estimation: A review

粒子图像测速 运动估计 深度学习 运动(物理) 流体力学 运动场 流体力学 领域(数学) 矢量场 人工智能 计算机科学 机械 物理 数学 湍流 纯数学
作者
Changdong Yu,Xiaojun Bi,Yiwei Fan
出处
期刊:Ocean Engineering [Elsevier BV]
卷期号:271: 113693-113693 被引量:49
标识
DOI:10.1016/j.oceaneng.2023.113693
摘要

Deep learning technique, has made tremendous progress in fluid mechanics in recent years, because of its mighty feature extraction capacity from complicated and massive fluid data. Motion estimation and analysis of fluid data is one of the significant research topics in fluid mechanics. In this paper, we provide a comprehensive review of fluid motion (i.e., velocity field) estimation methods based on deep learning. Essentially, the fluid super-resolution (SR) reconstruction task can also be regarded as an velocity field estimation from low resolution to high resolution. To this end, we mainly give a review on two topics: fluid motion estimation and later velocity field super-resolution reconstruction. Specifically, we first introduce the basic principle and component of deep learning methods. We then review and analyze deep learning based methods on fluid motion estimation. Note we mainly investigate the commonly used fluid motion estimation approach here, particle image velocimetry (PIV) algorithm, which extract velocity field from successive particle images pair in a non-contact manner. In addition, SR reconstruction methods for velocity fields based on deep learning technique are also reviewed. Eventually, we give a discussion and possible routes for the future research works. To our knowledge, this paper are the first to give a review of deep learning-based approaches for fluid velocity field estimation.
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