Hairpin Vortices Extraction in Turbulent Boundary Layer Flows

2026-06-02

AI Summary

This work proposes an automatic, parameter-free framework for extracting hairpin vortices in turbulent boundary layers, which are challenging to identify due to their irregular morphology, multiscale nature, and entanglement with other vortex structures. The method begins with an initial segmentation based on merge trees, followed by a bottom-up adaptive recombination strategy that integrates geometric and physical features to effectively mitigate under- and over-segmentation. Structural integrity is further validated through skeleton analysis combined with scalar criteria, and smooth envelope surfaces are generated for visualization. Experiments across multiple turbulent boundary layer datasets demonstrate that the proposed approach consistently outperforms existing techniques in terms of accuracy, computational efficiency, and robustness.

Abstract

Hairpin vortices are fundamental structures within turbulent boundary layers, playing a crucial role in energy dissipation, mixing, and momentum transport. However, accurately extracting these structures remains challenging due to their irregular shapes, varying scales, and entanglement with surrounding vortical structures. This paper presents a novel framework for the extraction of hairpin vortices from turbulent boundary layers. The method begins by identifying vortical regions and decomposing them into smaller segments using merge tree based segmentation. A novel bottom up rejoining approach is then introduced to group candidate segments according to the geometric and physical characteristics of hairpin vortices, resulting in regions that encompass complete hairpin vortex structures. These regions are subsequently refined and validated through skeleton analysis to detect the characteristic hairpin shape and are further confirmed using additional scalar based criteria. Finally, smooth enclosing surfaces are generated for effective visualization. To enable quantitative evaluation, reference hairpin vortices are extracted from several flow datasets and used as ground truth. Compared with existing approaches, the proposed method eliminates manual parameter tuning, reduces under and over segmentation, and significantly improves both accuracy and computational efficiency. Demonstrations on multiple turbulent flow cases show that the method is robust and effective for hairpin vortex extraction under varying boundary layer conditions.

Problem

• hairpin vortices
• turbulent boundary layer
• vortex extraction
• flow visualization
• vortical structures

Innovation

• hairpin vortex
• merge tree segmentation
• bottom-up rejoining
• skeleton analysis
• turbulent boundary layer