Characterization and machine learning analysis of hybrid alumina-copper oxide nanoparticles in therminol 55 for medium temperature heat transfer fluid
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F25%3A00381931" target="_blank" >RIV/68407700:21220/25:00381931 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1038/s41598-025-92461-3" target="_blank" >https://doi.org/10.1038/s41598-025-92461-3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41598-025-92461-3" target="_blank" >10.1038/s41598-025-92461-3</a>
Alternative languages
Result language
angličtina
Original language name
Characterization and machine learning analysis of hybrid alumina-copper oxide nanoparticles in therminol 55 for medium temperature heat transfer fluid
Original language 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20303 - Thermodynamics
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Scientific Reports
ISSN
2045-2322
e-ISSN
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Volume of the periodical
15
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
24
Pages from-to
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UT code for WoS article
001442602500009
EID of the result in the Scopus database
2-s2.0-105000047864