Machine learning aided investigation of dynamics of immiscible droplet impingement on liquid pools: A study across varying pool depths and droplet viscosities

Abstract The interactions of an immiscible droplet impinging on liquid pools bear significant implications across a wide array of applications, as well as in natural phenomena. In this paper, the dynamics associated with an immiscible droplet impinging on a liquid pool/film of varying depths have been elucidated. The study encompasses the impact of silicone oil droplets of four different viscosities (1, 10, 100, and 1000 cSt) upon a water pool of three non-dimensional pool heights h* = 1, 2.5, and 5. The phenomenon of droplet impact at two Weber numbers (We = 50 and 100) is captured through high-speed videography. The dynamics of impingement, associated with the immiscible liquid combination, are delineated by employing Mask R-CNN machine learning (ML) model. ML model generated masks are used to ascertain the dynamics of various cavity parameter. Further insights into the phenomena have been developed through a detailed energy analysis carried out pre- and post-impact. The performance of ML model is compared with the manually annotated images, exhibiting impressive level of agreement. Results reveal that during the cavity formation phase, low viscosity droplets conform to the cavity shape during their descend into the pool. In contrast, high viscosity droplets maintain their shape during cavity formation, showing pinning at the oil-water interface. Energy analysis shows better energy transfer from droplet to the cavity for low viscosity droplets (> 90%), while less than 50% of the impact energy is transferred for higher viscosity droplets. This study is among the first to apply machine learning to this complex fluid phenomenon, offering insights into the physics and potential applications in multiphase flows.