Characterization and machine learning analysis of hybrid alumina-copper oxide nanoparticles in therminol 55 for medium temperature heat transfer fluid
Efficient heat dissipation is crucial for various industrial and technological applications, ensuring system reliability and performance. Advanced thermal management systems rely on materials with superior thermal conductivity and stability for effective heat transfer. This study investigates the th...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2025
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| Online Access: | http://umpir.ump.edu.my/id/eprint/45079/ http://umpir.ump.edu.my/id/eprint/45079/1/Characterization%20and%20machine%20learning%20analysis%20of%20hybrid%20alumina-copper%20oxide.pdf |
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| author | Kadirgama, Ganesaan Ramasamy, Devarajan Kadirgama, Kumaran Samylingam, Lingenthiran Aslfattahi, Navid Qazani, Mohammad Reza Chalak Kok, Chee Kuang Yusaf, Talal F. Schmirler, Michal |
| author_facet | Kadirgama, Ganesaan Ramasamy, Devarajan Kadirgama, Kumaran Samylingam, Lingenthiran Aslfattahi, Navid Qazani, Mohammad Reza Chalak Kok, Chee Kuang Yusaf, Talal F. Schmirler, Michal |
| author_sort | Kadirgama, Ganesaan |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Efficient heat dissipation is crucial for various industrial and technological applications, ensuring system reliability and performance. Advanced thermal management systems rely on materials with superior thermal conductivity and stability for effective heat transfer. This study investigates the thermal conductivity, viscosity, and stability of hybrid Al2O3-CuO nanoparticles dispersed in Therminol 55, a medium-temperature heat transfer fluid. The nanofluid formulations were prepared with CuO-Al2O3 mass ratios of 10:90, 20:80, and 30:70 and tested at nanoparticle concentrations ranging from 0.1 wt% to 1.0 wt%. Experimental results indicate that the hybrid nanofluids exhibit enhanced thermal conductivity, with a maximum improvement of 32.82% at 1.0 wt% concentration, compared to the base fluid. However, viscosity increases with nanoparticle loading, requiring careful optimization for practical applications. To further analyze and predict thermal conductivity, a Type-2 Fuzzy Neural Network (T2FNN) was employed, demonstrating a correlation coefficient of 96.892%, ensuring high predictive accuracy. The integration of machine learning enables efficient modeling of complex thermal behavior, reducing experimental costs and facilitating optimization. These findings provide insights into the potential application of hybrid nanofluids in solar thermal systems, heat exchangers, and industrial cooling applications. |
| first_indexed | 2025-11-15T03:57:44Z |
| format | Article |
| id | ump-45079 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:57:44Z |
| publishDate | 2025 |
| publisher | Nature Publishing Group |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-450792025-07-14T08:37:32Z http://umpir.ump.edu.my/id/eprint/45079/ Characterization and machine learning analysis of hybrid alumina-copper oxide nanoparticles in therminol 55 for medium temperature heat transfer fluid Kadirgama, Ganesaan Ramasamy, Devarajan Kadirgama, Kumaran Samylingam, Lingenthiran Aslfattahi, Navid Qazani, Mohammad Reza Chalak Kok, Chee Kuang Yusaf, Talal F. Schmirler, Michal TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TP Chemical technology Efficient heat dissipation is crucial for various industrial and technological applications, ensuring system reliability and performance. Advanced thermal management systems rely on materials with superior thermal conductivity and stability for effective heat transfer. This study investigates the thermal conductivity, viscosity, and stability of hybrid Al2O3-CuO nanoparticles dispersed in Therminol 55, a medium-temperature heat transfer fluid. The nanofluid formulations were prepared with CuO-Al2O3 mass ratios of 10:90, 20:80, and 30:70 and tested at nanoparticle concentrations ranging from 0.1 wt% to 1.0 wt%. Experimental results indicate that the hybrid nanofluids exhibit enhanced thermal conductivity, with a maximum improvement of 32.82% at 1.0 wt% concentration, compared to the base fluid. However, viscosity increases with nanoparticle loading, requiring careful optimization for practical applications. To further analyze and predict thermal conductivity, a Type-2 Fuzzy Neural Network (T2FNN) was employed, demonstrating a correlation coefficient of 96.892%, ensuring high predictive accuracy. The integration of machine learning enables efficient modeling of complex thermal behavior, reducing experimental costs and facilitating optimization. These findings provide insights into the potential application of hybrid nanofluids in solar thermal systems, heat exchangers, and industrial cooling applications. Nature Publishing Group 2025 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/45079/1/Characterization%20and%20machine%20learning%20analysis%20of%20hybrid%20alumina-copper%20oxide.pdf Kadirgama, Ganesaan and Ramasamy, Devarajan and Kadirgama, Kumaran and Samylingam, Lingenthiran and Aslfattahi, Navid and Qazani, Mohammad Reza Chalak and Kok, Chee Kuang and Yusaf, Talal F. and Schmirler, Michal (2025) Characterization and machine learning analysis of hybrid alumina-copper oxide nanoparticles in therminol 55 for medium temperature heat transfer fluid. Scientific Reports, 15 (1). pp. 1-24. ISSN 2045-2322. (Published) https://doi.org/10.1038/s41598-025-92461-3 https://doi.org/10.1038/s41598-025-92461-3 |
| spellingShingle | TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TP Chemical technology Kadirgama, Ganesaan Ramasamy, Devarajan Kadirgama, Kumaran Samylingam, Lingenthiran Aslfattahi, Navid Qazani, Mohammad Reza Chalak Kok, Chee Kuang Yusaf, Talal F. Schmirler, Michal Characterization and machine learning analysis of hybrid alumina-copper oxide nanoparticles in therminol 55 for medium temperature heat transfer fluid |
| title | Characterization and machine learning analysis of hybrid alumina-copper oxide nanoparticles in therminol 55 for medium temperature heat transfer fluid |
| title_full | Characterization and machine learning analysis of hybrid alumina-copper oxide nanoparticles in therminol 55 for medium temperature heat transfer fluid |
| title_fullStr | Characterization and machine learning analysis of hybrid alumina-copper oxide nanoparticles in therminol 55 for medium temperature heat transfer fluid |
| title_full_unstemmed | Characterization and machine learning analysis of hybrid alumina-copper oxide nanoparticles in therminol 55 for medium temperature heat transfer fluid |
| title_short | Characterization and machine learning analysis of hybrid alumina-copper oxide nanoparticles in therminol 55 for medium temperature heat transfer fluid |
| title_sort | characterization and machine learning analysis of hybrid alumina-copper oxide nanoparticles in therminol 55 for medium temperature heat transfer fluid |
| topic | TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TP Chemical technology |
| url | http://umpir.ump.edu.my/id/eprint/45079/ http://umpir.ump.edu.my/id/eprint/45079/ http://umpir.ump.edu.my/id/eprint/45079/ http://umpir.ump.edu.my/id/eprint/45079/1/Characterization%20and%20machine%20learning%20analysis%20of%20hybrid%20alumina-copper%20oxide.pdf |