Multi-depth Holographic Cross-correlation Elimination Technique for Velocity Characterization

Estimating the velocity of seeded or naturally occurring particles is critical for a variety of fluid, combustion, and multiphase flow applications. Prior work combining cross-correlation techniques with traditional digital inline holography (DIH) have demonstrated successful velocity estimation for sub-pixel droplets at multiple depths without the need for individual droplet identification. However, these results have also shown velocity biasing when multiple velocities are present at different depths in high density flow fields. In this work, we combine digital off-axis holography (DOH) with a novel multi-plane cross-correlation technique to remove distortions from the twin-image effect and recover multi-plane velocity distributions for sub-pixel droplets in dense field. In this work, the holography diagnostic techniques are first discussed followed by a description of the multi-plane cross-correlation algorithm. Then, comparisons of DIH and DOH simulations processed with traditional cross-correlation and multi-plane cross-correlation methods are shown. Finally, experimental data gathered from a droplet-filled axial jet is analyzed to show the velocity distribution at varying heights is illustrated. Overall, this work presents a novel multi-plane cross-correlation technique that, when paired with off-axis holography, allows for enhanced velocity measurements and multi-plane velocity recovery in challenging environments.