Transient prediction of nanoparticle-laden droplet drying patterns through dynamic mode decomposition

• Main Authors: Tanis-Kanbur, Melike Begum, Kumtepeli, Volkan, Kanbur, Baris Burak, Ren, Junheng, Duan, Fei
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Mechanical engineering; Liquids; Particle Size
• Online Access: https://hdl.handle.net/10356/160562
• Institution: Nanyang Technological University

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...