Maximum spreading of droplet-particle collision covering a low Weber number regime and data-driven prediction model

Ikroh Yoon , Jalel Chergui (1) , Damir Juric (2, 1) , Seungwon Shin

1 LISN - Laboratoire Interdisciplinaire des Sciences du Numérique
2 COMET - COuplages Multiphysiques Et Transferts

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 0–110, Ohnesorge number 0.0013–0.7869, equilibrium contact angle 20°–160°, and droplet-to-particle size ratio 1/10–1/2. A total of 2600 collision cases are simulated to enable a systematic analysis and prepare a large dataset for training of a data-driven prediction model. The effects of four impact parameters on the maximum spreading diameter are comprehensively and quantitatively analyzed, focusing on the low Weber number regime. A universal model for prediction of β*max 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%.

Keywords: droplet impact; spherical particle; maximum spreading; multiphase flow; numerical simulation; artificial intelligence; deep neural network; data-driven prediction

Domains: Engineering Sciences [physics]; Nonlinear Sciences [physics]; Computer Science [cs]

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, In press, ⟨10.1063/5.0117839⟩. ⟨hal-03780424⟩