Statistical Analysis of Thermal Conductivity Experimentally Measured in Ethylene Glycol - Based Nanofluids

TL;DR

This study analyzes experimental data on thermal conductivity of ethylene glycol-based nanofluids, revealing linear relationships with concentration and temperature, and highlighting data scatter as a key challenge for nanofluid application.

Contribution

It provides a comprehensive statistical analysis of existing data, confirming theoretical models and identifying factors influencing thermal conductivity in nanofluids.

Findings

Thermal conductivity increases linearly with concentration.

Carbon nanotubes show temperature-dependent conductivity.

Data scatter complicates precise characterization.

Abstract

We collected literature data of thermal conductivity experimentally measured in ethylene glycol-based nanofluids and investigated the influence of concentration, temperature and nanoparticle size. We implemented statistical linear regression analysis of all data points and examined four separate nanoparticle materials - alumina, titania, copper oxide and carbon-nanotubes. We found that the statistical correlations are in good agreement with Maxwell's effective medium theory, despite large scatter in the data. The thermal conductivity increases linearly with concentration, and in the case of carbon-nanotubes with temperature, whereas the nanoparticle size shows significant influence for alumina and titania. The large scatter in the experimental data is one of the main problems. We suggest that there is a need for careful, detailed characterizations and measurements to quantify the potential of nanofluids.