Linear multispectral absorption tomography based on regularized iterative methods.

Area of Science:

• Chemical Engineering
• Optical Diagnostics
• Computational Modeling

Background:

• Accurate measurement of temperature and concentration is crucial for combustion analysis.
• Multispectral absorption tomography (MAT) offers a non-intrusive method for mapping these properties.
• Classical iterative algorithms for MAT can be limited by noise and reconstruction accuracy.

Purpose of the Study:

• To develop and validate a novel regularization approach for iterative algorithms in MAT.
• To improve the accuracy of reconstructing two-dimensional temperature and concentration distributions.
• To demonstrate the effectiveness of the proposed method using numerical simulations and experimental data.

Main Methods:

• Proposed a regularization approach by incorporating a secondary prior into classical iterative algorithms.
• Implemented the regularized iterative algorithm for two-dimensional temperature and concentration reconstruction.
• Validated the method through numerical studies with varying noise levels and spectral lines.
• Tested the algorithm using experimental data from a McKenna burner flame.

Main Results:

• The regularized iterative algorithm demonstrated superior reconstruction accuracy compared to classical and superiorized methods.
• Performance improvements were observed under various noisy conditions.
• The method showed effectiveness with different numbers of spectral lines.
• Reconstructed temperature profiles closely matched experimental thermocouple measurements.

Conclusions:

• The proposed regularized iterative method significantly enhances the accuracy of temperature and concentration reconstruction in MAT.
• This approach provides a robust and reliable tool for optical diagnostics in combustion research.
• The validated method holds promise for advanced applications in chemical process monitoring and analysis.