Maximum spreading of droplet-particle collision covering a low Weber number regime and data-driven prediction modelopen access
- Authors
- Yoon, I.; Chergui, J.; Juric, D.; Shin, S.
- Issue Date
- 1-Oct-2022
- Publisher
- American Institute of Physics Inc.
- Citation
- Physics of Fluids, v.34, no.10
- Journal Title
- Physics of Fluids
- Volume
- 34
- Number
- 10
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/30523
- DOI
- 10.1063/5.0117839
- 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 (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%. © 2022 Author(s).
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Collections - College of Engineering > Department of Mechanical and System Design Engineering > 1. Journal Articles
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