Maximum spreading of droplet-particle collision covering a low Weber number regime and data-driven prediction model
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoon, I. | - |
dc.contributor.author | Chergui, J. | - |
dc.contributor.author | Juric, D. | - |
dc.contributor.author | Shin, S. | - |
dc.date.accessioned | 2022-11-07T03:40:10Z | - |
dc.date.available | 2022-11-07T03:40:10Z | - |
dc.date.created | 2022-11-07 | - |
dc.date.issued | 2022-10-01 | - |
dc.identifier.issn | 1070-6631 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/30523 | - |
dc.description.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). | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | American Institute of Physics Inc. | - |
dc.title | Maximum spreading of droplet-particle collision covering a low Weber number regime and data-driven prediction model | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shin, S. | - |
dc.identifier.doi | 10.1063/5.0117839 | - |
dc.identifier.scopusid | 2-s2.0-85140879371 | - |
dc.identifier.wosid | 000875616100001 | - |
dc.identifier.bibliographicCitation | Physics of Fluids, v.34, no.10 | - |
dc.relation.isPartOf | Physics of Fluids | - |
dc.citation.title | Physics of Fluids | - |
dc.citation.volume | 34 | - |
dc.citation.number | 10 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mechanics | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Mechanics | - |
dc.relation.journalWebOfScienceCategory | Physics, Fluids & Plasmas | - |
dc.subject.keywordPlus | CATALYTIC CRACKING REACTOR | - |
dc.subject.keywordPlus | FRONT-TRACKING | - |
dc.subject.keywordPlus | WETTING CHARACTERISTICS | - |
dc.subject.keywordPlus | DIRECT SIMULATION | - |
dc.subject.keywordPlus | MULTIPHASE FLOWS | - |
dc.subject.keywordPlus | INJECTION ZONE | - |
dc.subject.keywordPlus | IMPACT | - |
dc.subject.keywordPlus | FLUID | - |
dc.subject.keywordPlus | DYNAMICS | - |
dc.subject.keywordPlus | TIME | - |
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