Quantitative gas property measurements by filtered Rayleigh scattering: a review

Abstract

Filtered Rayleigh scattering (FRS) is a laser-based diagnostic technique used to nonintrusively quantify various thermodynamic properties of a light-scattering gas. The backbone of FRS is the molecular filtering of Rayleigh scattered light. This concept was initially introduced by the atmospheric LIDAR community before being adopted within the aerospace research field in the early 1990s. Since then, FRS has matured into a versatile quantitative diagnostic tool and has found use in a variety of flow regimes ranging from sub- to supersonic speeds in both reacting and nonreacting environments. This adoption can be attributed to the wealth of information that can be obtained via FRS, including the gas density, pressure, temperature, velocity, species composition, or, in some cases, several of these properties at once. This article reviews the current state of FRS methodology in recovering such gas properties. As knowledge of the fundamentals of Rayleigh scattering and spectral light filtering is crucial to the design of an FRS experiment, we begin by briefly reviewing these areas. Subsequently, we conduct a survey of experimental design strategies, assumptions, and data reduction methods used to measure different gas properties using FRS. We conclude the review with a short discussion on quantification of experimental uncertainty and future trends in FRS.

Measurement Science and Technology’s second century underway

“…Looking back over the 2023 issues, review articles have remained prominent with articles covering smart optical coordinate and surface metrology [3], linear and nonlinear dimensionality reduction from fluid mechanics to machine learning [4], guided ultrasonic wave propagation imaging [5], mechanical ventilation based on machine learning [6], deep learning-based methods in structural reliability analysis [7], 3D optical measurement techniques [8], the application of Josephson voltage standards [9], combustion of metal particles [10], heterogeneous sensing for target tracking [11], quantitative gas property measurements by filtered Rayleigh scattering [12], optical fiber reflectometry detecting static and dynamic Rayleigh spectra [13], stimulated emission depletion microscopy [14], mechanical fault diagnosis based on deep transfer learning [15] and advanced combustion dynamics [16].…”