Data-Driven Diagnosis Of Measurement Biases For Filtered Rayleigh Scattering

Abstract

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.