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: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/160562 |
| _version_ | 1824455572176502784 |
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| author | Tanis-Kanbur, Melike Begum Kumtepeli, Volkan Kanbur, Baris Burak Ren, Junheng Duan, Fei |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Tanis-Kanbur, Melike Begum Kumtepeli, Volkan Kanbur, Baris Burak Ren, Junheng Duan, Fei |
| author_sort | Tanis-Kanbur, Melike Begum |
| collection | NTU |
| description | 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 algorithm by using a single data collection point via a top-view camera to predict the transient drying patterns of aluminum oxide (Al2O3) nanoparticle-laden sessile droplets with three cases according to particle sizes of 5 and 40 nm and Al2O3 concentrations of 0.1 and 0.2 wt %. Dynamic mode decomposition is used as the data-driven learning model to recognize each nanoparticle-laden droplet as an individual system and then apply the transfer learning procedure. Along 270 s of droplet drying experiments, the training period of the first 100 s is selected, and then the rest of the 170 s is predicted with less than a 10% error between the predicted and the actual droplet images. The developed data-driven approach has also achieved the acceptable prediction for the droplet diameter with less than 0.13% error and a coffee-ring thickness over a range of 2.0 to 6.7 μm. Moreover, the proposed machine learning algorithm can recognize the volume of the droplet liquid and the transition of the drying regime from one to another according to the predicted contact line and the droplet height. |
| first_indexed | 2025-02-19T03:40:20Z |
| format | Journal Article |
| id | ntu-10356/160562 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:40:20Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1605622022-07-26T08:37:56Z Transient prediction of nanoparticle-laden droplet drying patterns through dynamic mode decomposition Tanis-Kanbur, Melike Begum Kumtepeli, Volkan Kanbur, Baris Burak Ren, Junheng Duan, Fei School of Mechanical and Aerospace Engineering Energy Research Institute @ NTU (ERI@N) Engineering::Mechanical engineering Liquids Particle Size 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 algorithm by using a single data collection point via a top-view camera to predict the transient drying patterns of aluminum oxide (Al2O3) nanoparticle-laden sessile droplets with three cases according to particle sizes of 5 and 40 nm and Al2O3 concentrations of 0.1 and 0.2 wt %. Dynamic mode decomposition is used as the data-driven learning model to recognize each nanoparticle-laden droplet as an individual system and then apply the transfer learning procedure. Along 270 s of droplet drying experiments, the training period of the first 100 s is selected, and then the rest of the 170 s is predicted with less than a 10% error between the predicted and the actual droplet images. The developed data-driven approach has also achieved the acceptable prediction for the droplet diameter with less than 0.13% error and a coffee-ring thickness over a range of 2.0 to 6.7 μm. Moreover, the proposed machine learning algorithm can recognize the volume of the droplet liquid and the transition of the drying regime from one to another according to the predicted contact line and the droplet height. 2022-07-26T08:37:56Z 2022-07-26T08:37:56Z 2021 Journal Article Tanis-Kanbur, M. B., Kumtepeli, V., Kanbur, B. B., Ren, J. & Duan, F. (2021). Transient prediction of nanoparticle-laden droplet drying patterns through dynamic mode decomposition. Langmuir, 37(8), 2787-2799. https://dx.doi.org/10.1021/acs.langmuir.0c03546 0743-7463 https://hdl.handle.net/10356/160562 10.1021/acs.langmuir.0c03546 33577318 2-s2.0-85101397406 8 37 2787 2799 en Langmuir © 2021 American Chemical Society. All rights reserved. |
| spellingShingle | Engineering::Mechanical engineering Liquids Particle Size Tanis-Kanbur, Melike Begum Kumtepeli, Volkan Kanbur, Baris Burak Ren, Junheng Duan, Fei Transient prediction of nanoparticle-laden droplet drying patterns through dynamic mode decomposition |
| title | Transient prediction of nanoparticle-laden droplet drying patterns through dynamic mode decomposition |
| title_full | Transient prediction of nanoparticle-laden droplet drying patterns through dynamic mode decomposition |
| title_fullStr | Transient prediction of nanoparticle-laden droplet drying patterns through dynamic mode decomposition |
| title_full_unstemmed | Transient prediction of nanoparticle-laden droplet drying patterns through dynamic mode decomposition |
| title_short | Transient prediction of nanoparticle-laden droplet drying patterns through dynamic mode decomposition |
| title_sort | transient prediction of nanoparticle laden droplet drying patterns through dynamic mode decomposition |
| topic | Engineering::Mechanical engineering Liquids Particle Size |
| url | https://hdl.handle.net/10356/160562 |
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