Accurate icing wind tunnel testing of aircraft under supercooled large droplets (SLD) conditions necessitates a precise understanding of the velocity slip between droplets and the freestream airflow. This study presents an experimental investigation into the size–velocity correlation in the Chengdu Aircraft Industrial Corporation icing wind tunnel, utilizing a high-speed split-type microscopic shadowgraphic particle tracking velocimetry system operating at 50 kHz. To facilitate automated analysis, a You Only Look Once (YOLOv11x-p2) deep-learning model is employed to automatically identify and filter defocused droplets. This approach enables high-precision sizing and tracking through Kalman filtering. Quantitative analysis reveals a significant inverse exponential correlation between droplet diameter and velocity. SLD demonstrate substantial velocity deficits due to inertial effects, asymptotically stabilizing at approximately 70% of the local freestream velocity. Through dimensionless analysis, the study establishes that the particle Reynolds number (Rep) and Weber number (We) scale positively with droplet diameter, spanning the transition regime from surface-tension dominance to inertia-dominated flow. A novel dimensionless parameter, DN, that incorporates a deformation-corrected drag coefficient is proposed, which successfully collapses velocity data across different wind speeds onto a single master curve. In addition, the KD number allows for the identification of a transition regime, at which the flow physics transitions from a transient dispersed regime to a stable equilibrium regime. These findings provide critical validation data for refining drag models in icing codes and improving collision efficiency predictions required by airworthiness standards.
Tang et al. (Mon,) studied this question.