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流体的旋涡特征提取方法综述

  

  1. (1. 华北电力大学控制与计算机工程学院,河北 保定 071003; 2. 海军装备部装备项目管理中心,北京 100071)
  • 出版日期:2020-10-31 发布日期:2020-11-05
  • 作者简介:第一作者:邵绪强(1982?),男,山东泰安人,副教授,博士,硕士生导师。主要研究方向为计算机图形学、虚拟现实等。E-mail:shaoxuqiang@163.com
  • 基金资助:
    河北省自然科学基金项目(F2020502014);国家自然科学基金项目(61502168);中央高校基本科研业务费专项(2018MS068);北京市自 然科学基金项目(4182018)

A review of vortex feature extraction methods for fluid

  1. (1. School of Control and Computer Engineering, North China Electric Power University, Baoding Hebei 071003, China; 2. Equipment Project Management Center of Naval Equipment Department, Beijing 100071, China)
  • Online:2020-10-31 Published:2020-11-05
  • About author:First author:SHAO Xu-qiang (1982–), male, associate professor, Ph.D. His main research interests cover computer graphics and virtual reality. E-mail:shaoxuqiang@163.com
  • Supported by:
    Natural Science Foundation of Hebei Province (F2020502014); National Natural Science Foundation of China (61502168); Special Fund for Basic Scientific Research Business Expenses of Central University (2018MS068); Project Support of Beijing Natural Science Foundation (4182018)

摘要: 近年来,流体可视化已成为计算机图形学领域的一个研究热点,其最重要的目的 之一是旋涡特征的提取与可视化。由于目前仍未有一个通用的定义描述旋涡,导致文献对旋涡 是否存在的判断依据各不相同。为了对流体的旋涡特征提取方法进行较为系统的综述,首先对 旋涡提取研究方向的相关概念进行解释,回顾流体旋涡特征提取方法的发展情况再进行总结, 将常用的旋涡提取方法分为基于点、线、几何和基于机器学习的方法。对于新近提出的参考系 不变性,将旋涡提取方法分为伽利略不变性、旋转不变性和拉格朗日不变性。为了比较不同方 法的优势和缺陷,在综述每一类方法时分别给出若干经典方法,为研究者提供了一个清晰的研 究思路。最后总结每类方法存在的难点和问题,并指出今后的研究重点。

关键词: 旋涡提取方法, 特征可视化, 参考系不变性, 流体特征, 涡核

Abstract: In recent years, flow visualization has become a research hotspot in computer graphics, and one of its most important research goals is the extraction and visualization of vortex features. Since there is still no general definition of vortex, the evidences to determine whether vortex exists are different in the pertinent literature. In order to make a systematic review of the vortex feature extraction methods of fluid, the paper firstly explained the relevant research directions of vortex extraction, and then reviewed and summarized the development of the vortex feature extraction methods of fluids. The commonly used vortex extraction methods were classified into four categories, including point-based, line-based, geometry-based and machine learning-based methods. For the newly proposed invariance of reference frame, the vortex extraction methods were divided into three types, Galilean invariance, rotation invariance and Lagrange invariance. To compare the advantages and disadvantages of various methods, several classical methods of each type were given in the review, therefore providing a clear outline for future studies. Finally, the difficulties and problems of each method were concluded, and the possible future research focus were introduced.

Key words: vortex extraction method, feature visualization, reference frame invariance, flow feature; vortex core