Effect of specific surface area on convective heat transfer of graphene nanoplatelet aqueous nanofluids

In this work, an experimental heat transfer investigation was carried out to investigate the combined influence of both amorphous carbon (a-C) layer thickness and carbon nanofibers (CNFs) on the convective heat transfer behavior. Synthesis of these carbon nanostructures was achieved using catalytic chemical vapor deposition process on a 50 μm nickel wire at 650 °C. Due to their extremely high thermal conductivity, CNFs are used to augment/modify heat transfer surface. However, the inevitable layer of a-C that occurs during the synthesis of the CNFs layer exhibits low thermal conductivity which may result in insulating the surface. In contrast, the amorphous layer helps in supporting and mechanically stabilizing the CNFs layer attachment to the polycrystalline nickel (Ni270) substrate material. To better understand the influences of these two layers on heat transfer, the growth mechanism of the CNFs layer and the layer of carbon is investigated and growth model is proposed. The combined impact of both a-C and CNFs layers on heat transfer performance is studied on three different samples which were synthesized by varying the deposition period (16 min, 23 min, and 30 min). The microwire samples covered with CNF layers were subjected to a uniform flow from a nozzle. Heat transfer measurement was achieved by a controlled heat dissipation through the microwire to attain a constant temperature during the flow. This measurement technique is adopted from hot wire anemometry calibration method. Maximum heat transfer enhancement of 18% was achieved. This enhancement is mainly attributed to the surface roughness and surface area increase of the samples with moderate CNFs surface area coverage on the sample.

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A High-Accuracy Thermal Conductivity Model for Water-Based Graphene Nanoplatelet Nanofluids

Energies ◽

10.3390/en14165178 ◽

2021 ◽

Vol 14 (16) ◽

pp. 5178

Author(s):

Elif Begum Elcioglu

Keyword(s):

Thermal Conductivity ◽

Specific Surface ◽

Surface Area ◽

Specific Surface Area ◽

High Accuracy ◽

Conductivity Data ◽

Graphene Nanoplatelet ◽

Independent Variables ◽

Thermal Conductivity Data ◽

Thermal Conductivities

High energetic efficiency is a major requirement in industrial processes. The poor thermal conductivity of conventional working fluids stands as a limitation for high thermal efficiency in thermal applications. Nanofluids tackle this limitation by their tunable and enhanced thermal conductivities compared to their base fluid counterparts. In particular, carbon-based nanoparticles (e.g., carbon nanotubes, graphene nanoplatelets, etc.) have attracted attention since they exhibit thermal conductivities much greater than those of metal-oxide and metallic nanoparticles. In this work, thermal conductivity data from the literature are processed by employing rigorous statistical methodology. A high-accuracy regression equation is developed for the prediction of thermal conductivity of graphene nanoplatelet-water nanofluids, based on the temperature (15–60 °C), nanoparticle weight fraction (0.025–0.1 wt.%), and graphene nanoparticle specific surface area (300–750 m2/g). The strength of the impact of these variables on the graphene nanoplatelet thermal conductivity data can be sorted from the highest to lowest as temperature, nanoparticle loading, and graphene nanoplatelet specific surface area. The model developed by multiple linear regression with three independent variables has a determination coefficient of 97.1% and exhibits convenience for its ease of use from the existing prediction equations with two independent variables.

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