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<p>Schlieren imaging is a widely used technique to visualize the structure of supersonic flow field, which is usually dominated by shock waves. Precise identification of shock waves in schlieren image provides critical insights for flow diagnostics, especially for supersonic inlet whose performance is highly associated with that of the whole flight. However, conventional shock wave identification methods have limited accuracy in segmenting the shock wave. To overcome the limitation, we proposed an automated shock wave identification method (SW-Segment) that can attain high resolution and automatic shock wave segmentation by integrating correlation-based feature extraction with graph search. We demonstrated the efficacy of SW-Segment via the identification of shock waves in simulatively and experimentally obtained schlieren image. The results proved that SW-Segment showed a shock wave identification accuracy of 95.24% in the numerical schlieren image and an accuracy of 88.33% in the experimental image, clearly demonstrating its reliability. SW-Segment holds broad applicability for shock wave detection in diverse schlieren imaging scenarios, offering robust data support for flow field analysis and supersonic flight design.</p>

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SW-Segment: Automatic segmentation of shock waves in schlieren images based on image correlation and graph search

  • 期刊论文

  • 发表日期：

    2026年1月9日

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  • Qinglong Yin,
  • Yuan Tian,
  • Yizhu Wang,
  • Liang Chen,
  • Feng Xing,
  • Liwei Su,
  • Yue Zhang,
  • Huijun Tan,
  • Depeng Wang

摘要

Schlieren imaging is a widely used technique to visualize the structure of supersonic flow field, which is usually dominated by shock waves. Precise identification of shock waves in schlieren image provides critical insights for flow diagnostics, especially for supersonic inlet whose performance is highly associated with that of the whole flight. However, conventional shock wave identification methods have limited accuracy in segmenting the shock wave. To overcome the limitation, we proposed an automated shock wave identification method (SW-Segment) that can attain high resolution and automatic shock wave segmentation by integrating correlation-based feature extraction with graph search. We demonstrated the efficacy of SW-Segment via the identification of shock waves in simulatively and experimentally obtained schlieren image. The results proved that SW-Segment showed a shock wave identification accuracy of 95.24% in the numerical schlieren image and an accuracy of 88.33% in the experimental image, clearly demonstrating its reliability. SW-Segment holds broad applicability for shock wave detection in diverse schlieren imaging scenarios, offering robust data support for flow field analysis and supersonic flight design.