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Data-Driven Diagnosis Of Measurement Biases For Filtered Rayleigh Scattering

Evan Warner, Gwibo Byun, Todd Lowe

Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States


This work focuses on the data mining of a filtered Rayleigh scattering (FRS) database to diagnose the source(s) of measurement biases experienced in these applied measurements in harsh environments. Qualitative statistical analysis is performed to assess modeled-to-measured signal disagreements. This qualitative analysis shows that the signal model converges to measured signals as the non-dimensional thermodynamic parameter, termed the Y-parameter for Rayleigh scattering analysis, decreases. To perform a quantitative assessment, these FRS datasets were processed for Y-parameter and compared to known values derived from reference measurements of the corresponding flow fields. Residuals between the FRS results and the reference values were plotted as a histogram, which showed that there is a region of "least-bias" corresponding to Y-parameter less than 1. Root cause assessment of bias sources suggests that the key contributing factor is uncertainty in the modeled Rayleigh-Brillouin scattering signal for air when Y-parameter is greater than 1.

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