Dynamic Reconstruction Algorithm of Three-Dimensional Temperature Field Measurement by Acoustic Tomography

Area of Science:

• Thermometry
• Tomography
• Algorithm Development

Background:

• Acoustic tomography is crucial for temperature field measurement.
• Current algorithms rely on static models, limiting accuracy.
• Dynamic evolution of temperature fields is often overlooked.

Purpose of the Study:

• To develop a dynamic model for 3D temperature reconstruction using acoustic tomography.
• To propose a novel dynamic algorithm integrating measurement and evolution data.
• To enhance the accuracy and robustness of temperature field reconstruction.

Main Methods:

• Established a dynamic model for 3D temperature reconstruction.
• Developed a dynamic algorithm incorporating acoustic measurements and temperature field evolution.
• Constructed an objective function fusing measurement, spatial constraints, and dynamic evolution.
• Employed robust estimation, a tunneling algorithm, and local minimization.

Main Results:

• The dynamic reconstruction algorithm demonstrated superior image quality.
• Enhanced noise immunity was observed compared to static algorithms.
• Performance was validated against least squares, ART, and Tikhonov regularization.

Conclusions:

• The dynamic algorithm offers an effective approach for acoustic tomography temperature reconstruction.
• Integrating dynamic evolution information significantly improves reconstruction performance.
• This method provides a robust solution for complex temperature field mapping.