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Corresponding Author

Hazem M. Al-Bulqini

Subject Area

Mechanical Power Engineering

Article Type

Original Study

Abstract

Laser diagnostic techniques have played a crucial role in enhancing our understanding of combustion processes. Among these techniques, Planar Laser-Induced Fluorescence (PLIF) utilizing OH radicals has proven to be a powerful tool for investigating reaction zones, flame curvature, and flame surface density in diverse flame modes including premixed, non-premixed, and partially premixed flames. However, to fully harness the potential of experimental measurements and laser diagnostics in studying combustion processes, a comprehensive grasp of image processing techniques and tools is essential for effectively analyzing captured images and extracting valuable information. In this study, we present a detailed algorithm that facilitates the conversion of qualitative single-shot OH-PLIF images into quantitative data regarding flame structure under different conditions, encompassing flame curvature, flame surface density, and flame front angle. This methodology outlines the sequential steps necessary for extracting physical properties from images. Furthermore, we showcase results obtained using this algorithm, employing flames under varying conditions to validate its effectiveness, compare studied parameters across different flames, and demonstrate its applicability to a wide range of combustion processes. Otsu’s method of thresholding provides more accuracy in detecting the reaction zone over a wide range of conditions. Also the Gaussian smoothing method with standard deviation value of σ = 2 provides the most suitable way of smoothing without removing valuable data. The comparison between different flame results in higher value of zero curvature (P(κ)=3.25) and lower values of flame front angle (P(θ) ~ 0) when the flame shape has less turbulent nature and more vertical jet shape.

Keywords

Data processing; high speed images; flame curvature; flame surface density

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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