A prediction model for the effective thermal conductivity of nanofluids considering agglomeration and the radial distribution function of nanoparticles
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Conventional heat transfer fluids usually have low thermal conductivity, limiting their efficiency in many applications. Many experiments have shown that adding nanosize solid particles to conventional fluids can greatly enhance their thermal conductivity. To explain this anomalous phenomenon, many theoretical investigations have been conducted in recent years. Some of this research has indicated that the particle agglomeration effect that commonly occurs in nanofluids should play an important role in such enhancement of the thermal conductivity, while some have shown that the enhancement of the effective thermal conductivity might be accounted for by the structure of nanofluids, which can be described using the radial distribution function of particles. However, theoretical predictions from these studies are not in very good agreement with experimental results. This paper proposes a prediction model for the effective thermal conductivity of nanofluids, considering both the agglomeration effect and the radial distribution function of nanoparticles. The resulting theoretical predictions for several sets of nanofluids are highly consistent with experimental data.
KeywordsNanofluid Thermal conductivity Modeling Agglomeration Radial distribution function
This work was supported by the National Natural Science Foundation of China (Grants 11472313, 11232015, and 11572355) and the Guangdong Province Research Fund for Applied Research.
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