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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.

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