Vol. 2 (2026)

Articles

CFD-Guided Restoration of Occluded Schlieren Images for Supersonic Turbine Cascade Flow Diagnostics

Published 2026-03-04

  • Shihe Qu
    • ,
  • Yizhou Duan
    • ,
  • Zeyu Zheng
    • ,
  • Xiubin Li
    • ,
  • Yeming Lu
    • ,
  • Yiwen Chen
    • ,
  • Xiaomo Jiang
    • ,
  • Xiaofang Wang

Shihe Qu
Laboratory of Ocean Energy Utilization of Ministry of Education,School of Energy and Power Engineering, Dalian University of Technology, Dalian 116000, China

Yizhou Duan
Laboratory of Ocean Energy Utilization of Ministry of Education,School of Energy and Power Engineering, Dalian University of Technology, Dalian 116000, China

Zeyu Zheng
Laboratory of Ocean Energy Utilization of Ministry of Education,School of Energy and Power Engineering, Dalian University of Technology, Dalian 116000, China

Xiubin Li
Laboratory of Ocean Energy Utilization of Ministry of Education,School of Energy and Power Engineering, Dalian University of Technology, Dalian 116000, China

Yeming Lu
Laboratory of Ocean Energy Utilization of Ministry of Education,School of Energy and Power Engineering, Dalian University of Technology, Dalian 116000, China

Yiwen Chen
State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment, Deyang 618000, China

Xiaomo Jiang
Laboratory of Ocean Energy Utilization of Ministry of Education,School of Energy and Power Engineering, Dalian University of Technology, Dalian 116000, China

Xiaofang Wang
Laboratory of Ocean Energy Utilization of Ministry of Education,School of Energy and Power Engineering, Dalian University of Technology, Dalian 116000, China

Abstract

The operating environment of large-scale steam turbines is complex, and the flow characteristics around the long blades of the final stage are critical to both plant efficiency and safety. However, the fixed structure of ultra-thin blades causes occlusions in shadowgraph images, severely limiting precise analysis of flow characteristics. To this end, this paper proposes a physics-informed restoration framework specifically designed to overcome large-scale physical occlusions in ultra-thin blade cascade experiments. Distinct from general inpainting, our method achieves strict spatial coordinate synchronization between experimental and numerical domains via affine mapping, ensuring the reconstructed flow features are anchored to a consistent physical coordinate system. Combined with edge detection and feature recognition, occluded regions are localised, and numerical Schlieren information is utilised to reconstruct and repair these areas. Research findings demonstrate that this method effectively restores obscured regions, significantly improves the quality of the Schlieren image and provides reliable data support for in-depth analysis of flow characteristics of the final long blade cascade of steam turbines.

Keywords

Schlieren imaging; Supersonic cascade flow; CFD-assisted image restoration; Affine transformation; Edge detection.

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