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Diesel spray image segmentation with a likelihood ratio test.

J V Pastor, J Arrègle, A Palomares

    Applied Optics
    |March 22, 2008
    PubMed
    Summary

    Accurate Diesel spray geometry, including tip penetration and cone angle, is crucial for high-pressure injection systems. A new image segmentation algorithm using a likelihood ratio test improves accuracy, even in difficult conditions.

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    Area of Science:

    • Combustion Science
    • Fluid Dynamics
    • Optical Diagnostics

    Background:

    • Characterizing Diesel spray behavior is essential for optimizing high-pressure injection systems.
    • Existing literature correlations for spray geometry require validation for modern systems.
    • Accurate measurement of spray tip penetration and cone angle is critical.

    Purpose of the Study:

    • To accurately determine Diesel spray geometry, specifically tip penetration and cone angle.
    • To validate and extend existing literature correlations for high-pressure injection systems.
    • To present and validate a novel image segmentation algorithm for Diesel spray analysis.

    Main Methods:

    • Development of an image segmentation algorithm based on a likelihood ratio test.
    • Analysis of Diesel spray images to determine geometric parameters.
    • Comparison of the proposed algorithm with existing methods.
    • Validation under various experimental conditions.

    Main Results:

    • The proposed likelihood ratio test algorithm accurately segments Diesel spray images.
    • The algorithm demonstrates robustness even under adverse experimental conditions.
    • Validated spray geometry measurements for high-pressure injection systems.

    Conclusions:

    • The developed image segmentation algorithm provides accurate Diesel spray geometry characterization.
    • This method is suitable for validating and extending existing correlations for modern injection systems.
    • The study contributes to improved understanding and modeling of Diesel spray dynamics.

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