Interface reconstruction of adhering droplets for distortion correction using glare points and deep learning (research data)

Most commonly, the velocity field inside adhering droplets is measured by means of particle image velocimetry (PIV), which requires a correction step to account for the distortion caused by the refraction of light at the curved gas-liquid interface. Current methods for distortion correction based on ray tracing are limited to low external flow velocities, for which the deformation of the droplet is insignificant and axisymmetry can be assumed.

However, the ray-tracing method can be extended straightforwardly to arbitrarily deformed droplet shapes if the instantaneous three-dimensional droplet interface could be obtained. In the present work, a previously introduced method for the image-based reconstruction of gas-liquid interfaces by means of deep learning is adapted to determine the instantaneous interface of adhering droplets in external shear flows. In this regard, a purposefully developed optical measurement technique based on the shadowgraphy method is employed that encodes additional three-dimensional (3D) information of the interface in the images via glare points from lateral light sources.