Novel deep learning methods for 3D flow field segmentation and classification
This addresses the problem of analyzing vortex structures in turbulent flows for researchers, offering significant speed improvements but is incremental as it builds on existing deep learning approaches.
The paper tackled 3D flow field segmentation and classification by proposing novel deep learning methods based on local velocity and vorticity criteria, resulting in segmentation time reduced by over 50% and classification time reduced by over 90% while maintaining accuracy.
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.