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    Area of Science:

    • Aerodynamics
    • Optical Measurement Techniques
    • Data Mining

    Background:

    • Filtered Rayleigh scattering (FRS) is an optical technique used in aerodynamic measurements.
    • Accurate aerodynamic data is crucial for various engineering applications.
    • Potential biases in FRS measurements can impact the reliability of aerodynamic diagnostics.

    Purpose of the Study:

    • To identify and diagnose the sources of measurement biases in filtered Rayleigh scattering (FRS) data.
    • To analyze the agreement between FRS signal models and experimental measurements under known conditions.
    • To pinpoint the root causes of observed signal biases in aerodynamic applications.

    Main Methods:

    • Data mining of a filtered Rayleigh scattering (FRS) database.
    • Qualitative and quantitative bias analysis of FRS measurement configurations.
    • Root cause assessment of signal bias sources.

    Main Results:

    • Empirical evidence of a subset of FRS configurations where signal models disagree with measured signals.
    • Identification of specific aerothermodynamic states where biases are pronounced.
    • Confirmation that signal bias is linked to discrepancies at known gas states.

    Conclusions:

    • Uncertainty in the modeled laser Rayleigh scattering (LRS) signal is the key contributing factor to FRS measurement biases.
    • The identified biases are particularly relevant across the full range of experiment configurations for this optical technique.
    • Addressing LRS signal modeling uncertainties is critical for improving the accuracy of aerodynamic measurements using FRS.