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In this study, we investigate the energy conversion and dissipation mechanisms of spreading droplets on a solid surface at a low Weber number regime, which neither conventional energy-balance-based theories nor empirical scaling laws can completely explain. The energetic analysis presented in this study shows that on a hydrophilic surface, the actual primary energy source driving the spreading process is the initial surface energy not the initial kinetic energy. The conventional energy-balance-based approaches are found to be valid only for the spreading process on a hydrophobic surface. Particular attention is also paid to the roles of the capillary waves. The capillary waves are found to play significant roles in all of the important flow physics, that is, the interfacial structure, the oscillatory motions and the rapid collapse of the liquid film, the onset of the viscous regime, and the energy loss mechanism. It is also shown that the energy dissipation caused by the capillary-wave-induced phenomena can be estimated to be 25%–35% and 55%–65% of the total energy loss for a hydrophilic and a hydrophobic surface, respectively, at the low Weber number regime. [ABSTRACT FROM AUTHOR]

In the present study, the maximum spreading diameter of a droplet impacting with a spherical particle is numerically studied for a wide range of impact conditions: Weber number (We) 0–110, Ohnesorge number (Oh) 0.001 3–0.786 9, equilibrium contact angle (θeqi) 20°–160°, and droplet-to-particle size ratio (Ω) 1/10–1/2. A total of 2600 collision cases are simulated to enable a systematic analysis and prepare a large dataset for the training of a data-driven prediction model. The effects of four impact parameters (We, Oh, θeqi, and Ω) on the maximum spreading diameter (β*max) are comprehensively analyzed, and particular attention is paid to the difference of β*max between the low and high Weber number regimes. A universal model for the prediction of β*max, as a function of We, Oh, θeqi, and Ω, is also proposed based on a deep neural network. It is shown that our data-driven model can predict the maximum spreading diameter well, showing an excellent agreement with the existing experimental results as well as our simulation dataset within a deviation range of ±10%. [ABSTRACT FROM AUTHOR]