Journal
POWDER TECHNOLOGY
Volume 386, Issue -, Pages 51-59Publisher
ELSEVIER
DOI: 10.1016/j.powtec.2021.03.028
Keywords
Graphene nano-sheets; Thermal conductivity; Sensitivity; Artificial neural network
Categories
Funding
- University of Science and Technology Beijing
- National Natural Science Foundation of China [11971142, 61673169]
- King Abdulaziz University, Jeddah, Saudi Arabia [KEP-17-130-41]
- DSR
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The study compared the thermal conductivity of graphene nano-sheets (GNs)/ethylene glycol (EG) nanofluid with EG alone, showing that the presence of nanoparticles enhances the thermal conductivity of EG. Additionally, loading GNs into EG reversed the temperature dependency of thermal conductivity, with nanofluid showing increased thermal conductivity as temperature rises. The positive effects of nanoparticles on thermal conductivity decreased with higher nanoparticle content, and adding GNs strengthened the impact on thermal conductivity with increasing temperature.
In this study, thermal conductivity of graphene nano-sheets (GNs)/ethylene glycol (EG) nanofluidwas compared with EG thermal conductivity at 25-70 degrees C and 0.005-0.5 wt% to examine the effects of GNs nanoparticles. For all samples, presence of nanoparticles intensifies EG thermal conductivity up to 54.6%. Moreover, loading GNs into EG inverts the dependency of the thermal conductivity to temperature. As the temperature rises, the thermal conductivity of the base fluid decreases, while for nanofluid, thermal conductivity increases. Based on the results, by incorporating more nanoparticles, the positive effects of nanoparticles on thermal conductivity s reduced. It was concluded that with increasing temperature, the effect of adding GNs on the thermal conductivity is strengthened. Neural network implementation showed that this method can forecast k(GNs)/EC/k(EG) with maximum error of less than 3%. (C) 2021 Elsevier B.V. All rights reserved.
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