Flow field segmentation and classification help researchers to understand vortex structure and thus turbulent flow. Existing deep learning methods mainly based on global information and focused on 2D circumstance. Based on flow field theory, we propose novel flow field segmentation and classification deep learning methods in three-dimensional space. We construct segmentation criterion based on local velocity information and classification criterion based on the relationship between local vorticity and vortex wake, to identify vortex structure in 3D flow field, and further classify the type of vortex wakes accurately and rapidly. Simulation experiment results showed that, compared with existing methods, our segmentation method can identify the vortex area more accurately, while the time consumption is reduced more than 50\%; our classification method can reduce the time consumption by more than 90\% while maintaining the same classification accuracy level.

基于三维流场理论，提出了一种基于局部速度信息的分割标准和基于局部涡量与涡尾之间关系的分类标准的流场分割及分类深度学习方法，能更准确地识别涡旋结构，并在准确分类涡尾类型的同时，大幅度减少时间消耗。

3D流场分割和分类的新型深度学习方法