Deep learning-based optical flow analysis of two-dimensional Rayleigh scattering imaging of high-speed flows

Velocity field quantification for high-speed flows is of fundamental importance to understand flow dynamics, turbulence, and flow–structure interactions. Optical velocimetry techniques commonly provide sparse information in the flows. Dense fields of velocity vectors with high spatial resolutions are indispensable for detailed analysis of complex motion patterns and accurate motion tracking within the field of view. In the present work, two-dimensional (2D) Rayleigh scattering imaging (RSI) at a rate of 10- to 100-kHz was utilized to quantify the high-speed flow velocity by employing deep learning-based optical flow analysis, along with density and temperature fields from Rayleigh scattering intensity profiles. High-speed Rayleigh scattering images are highly spatially resolved, have smooth gradients without intensity discontinuities, and precisely track key features of the flows. The deep learning-based optical flow method utilizes recurrent neural network architecture to extract the per-pixel features of both input images, calculate correlation from all pairs of the features, and get training by recurrently updating the optical flow. 2D instantaneous velocity fields of both nonreacting and reacting flows measured by RSI were obtained from deep learning-based optical flow analysis, thus extending RSI as a non-intrusive, nonseeded, and multiscalar measurement technique of high-speed nonreacting and reacting flows.