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%.
Maximum spreading of droplet-particle collision covering a low Weber number regime and data-driven prediction model
Ikroh Yoon, Jalel Chergui, Damir Juric, Seungwon Shin; Maximum spreading of droplet-particle collision covering a low Weber number regime and data-driven prediction model. Physics of Fluids 1 October 2022; 34 (10): 102109. https://doi.org/10.1063/5.0117839
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