Current Optics and Photonics

Optical Society of Korea (한국광학회)
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Analyzing the Influence of Spatial Sampling Rate on Three-dimensional Temperature-field Reconstruction

  • Shenxiang Feng (The State Key Laboratory of Dynamic Measurement Technology, North University of China) ;
  • Xiaojian Hao (The State Key Laboratory of Dynamic Measurement Technology, North University of China) ;
  • Tong Wei (The State Key Laboratory of Dynamic Measurement Technology, North University of China) ;
  • Xiaodong Huang (The State Key Laboratory of Dynamic Measurement Technology, North University of China) ;
  • Pan Pei (The State Key Laboratory of Dynamic Measurement Technology, North University of China) ;
  • Chenyang Xu (The State Key Laboratory of Dynamic Measurement Technology, North University of China)
  • Received : 2024.01.25
  • Accepted : 2024.04.29
  • Published : 2024.06.25
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Abstract

In aerospace and energy engineering, the reconstruction of three-dimensional (3D) temperature distributions is crucial. Traditional methods like algebraic iterative reconstruction and filtered back-projection depend on voxel division for resolution. Our algorithm, blending deep learning with computer graphics rendering, converts 2D projections into light rays for uniform sampling, using a fully connected neural network to depict the 3D temperature field. Although effective in capturing internal details, it demands multiple cameras for varied angle projections, increasing cost and computational needs. We assess the impact of camera number on reconstruction accuracy and efficiency, conducting butane-flame simulations with different camera setups (6 to 18 cameras). The results show improved accuracy with more cameras, with 12 cameras achieving optimal computational efficiency (1.263) and low error rates. Verification experiments with 9, 12, and 15 cameras, using thermocouples, confirm that the 12-camera setup as the best, balancing efficiency and accuracy. This offers a feasible, cost-effective solution for real-world applications like engine testing and environmental monitoring, improving accuracy and resource management in temperature measurement.

Keywords

Acknowledgement

The authors thank all reviewers, editors, and contributors for their contributions and suggestions, as well as all members of the OSEC Laboratory.

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