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Arabian Journal for Science and Engineering

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05.09.2020 | Research Article-Mechanical Engineering

A Novel Correlation to Calculate Thermal Conductivity of Aqueous Hybrid Graphene Oxide/Silicon Dioxide Nanofluid: Synthesis, Characterizations, Preparation, and Artificial Neural Network Modeling

verfasst von: Quyen Nguyen, Reza Rizvandi, Arash Karimipour, Omid Malekahmadi, Quang-Vu Bach

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 11/2020

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Abstract

Graphene oxide is generally used in hydrogen storage, energy conversion, lens, and flexible rechargeable battery electrode. Silica is one of the most plentiful families of materials, which has potential to be an excellent choice for industrial applications due to its low-cost production, high specific surface area, and also its hydrophilicity. Hybrid nanofluid (HN) is one of nanofluid types in which more than one solid particle dispersed in a fluid. In this paper, after preparation of graphene oxide/silicon dioxide/water hybrid nanofluid, thermal conductivity (TC) was studied and numerically modeled. Then, to study phase and structural analysis, X-ray diffraction analysis and dynamic light scattering analysis were employed. After that, scanning electron microscope was used to study microstructural observation of nanoparticles. TC measurements of HN were taken at volume fractions of 0.05–1.0% and at temperature ranges of 25–50 °C. Thermal conductivity enhancement of 26.93% was measured at 1.0 vol.% fraction in 50 °C temperature. For numerical modeling, new correlation has been offered (R² = 0.9), and further, artificial neural network has been modeled (R² = 0.999). For offered correlation, 1.48% deviation and for trained model, 1.26% deviation were calculated. Totally, GO–SiO₂–H₂O HN has acceptable heat transfer potential.

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Titel: A Novel Correlation to Calculate Thermal Conductivity of Aqueous Hybrid Graphene Oxide/Silicon Dioxide Nanofluid: Synthesis, Characterizations, Preparation, and Artificial Neural Network Modeling
verfasst von: Quyen Nguyen
Reza Rizvandi
Arash Karimipour
Omid Malekahmadi
Quang-Vu Bach
Publikationsdatum: 05.09.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Arabian Journal for Science and Engineering / Ausgabe 11/2020
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-020-04885-w

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