Transient prediction of nanoparticle-laden droplet drying patterns through dynamic mode decomposition
Nanoparticle-laden sessile droplet drying has a wide impact on applications. However, the complexity affected by the droplet evaporation dynamics and particle self-assembly behavior leads to challenges in the accurate prediction of the drying patterns. We initiate a data-driven machine learning algo...
Main Authors: | Tanis-Kanbur, Melike Begum, Kumtepeli, Volkan, Kanbur, Baris Burak, Ren, Junheng, Duan, Fei |
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Other Authors: | School of Mechanical and Aerospace Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2022
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/160562 |
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