Maximum spreading of droplet-particle collision covering a low Weber number regime and data-driven prediction model Article - 2022

Ikroh Yoon, Jalel Chergui, Damir Juric, Seungwon Shin

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, à paraître. ISSN 1070-6631

Abstract

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%.

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