Unbiased Filtered Rayleigh Scattering Measurement Model for Aerodynamic Flows

The filtered Rayleigh scattering (FRS) optical diagnostic has become an attractive technique for advanced aerodynamic measurements. The appeal of FRS is that it can simultaneously quantify density, temperature, and vector velocity. Additionally, it is entirely non-intrusive to the flow since the technique leverages how laser light scatters off of molecules naturally present in the gas. Acquired FRS data considered herein is in the form of a frequency spectrum. To process this data, a measurement model for the FRS spectrum is used, where inputs to this model are the flow field quantities of interest and the output is a representative FRS spectrum. An iterative procedure on these quantities is performed until the model spectrum matches the measured spectrum. However, as observed in certain applications of this technique, there is a range of measurement configurations where the standard methods to model this spectrum do not agree with measured spectra, even at known flow conditions. This disagreement causes large bias uncertainties in determined flow field quantities. This work leverages a data-driven approach to diagnose this disagreement by utilizing an extensive FRS database. Data analysis indicates that the widely used Tenti S6 model for the Rayleigh scattering lineshape is invalid in certain operating regions. A new Rayleigh lineshape modeling methodology, the Cabannes model, is introduced that vastly improves the agreement between measured and modeled FRS signals. Analysis of the Cabannes model indicates that one only needs to use this modeling methodology for FRS and not laser Rayleigh scattering (LRS). This improved measurement model can be used to mitigate bias uncertainties, and, in turn, improve the reliability of the FRS optical instrument.