Prediction of Stability and Thermal conductivity of MgO Nanofluids using CCRD Statistical Design Analysis

Abstract

Magnesium oxide nanopowders were synthesized by chemical reduction method in which sodium hydroxide solution was used as a reducing agent. Magnesium nitrate (MgNO₃.6H₂O) precursor was used for the synthesis of MgO nanopowders. Solid state characterizations of synthesized nanopowders were carried out by infrared spectroscopy (FTIR) and X-ray diffraction (XRD) techniques. Using two step method, synthesized nanopowders were prepared as nanofluids by adding water and ethylene glycol (55:45). Thermal conductivity measurements of prepared nanofluids were studied using transient hot wire apparatus in which maximum thermal conductivity enhancement was observed in nanofluid. CCRD design has been applied to optimize the performance of nanofluid systems. In this regard, the performance was evaluated by measuring the stability and thermal conductivity ratio based on the critical independent variables such as temperature, particle volume fraction and the pH of the solution. A total of 20 experiments were accomplished for the construction of second-order polynomial equations for both target outputs. All the influential factors, their mutual effects and their quadratic terms were statistically validated by analysis of variance (ANOVA). The optimum stability and thermal conductivity of MgO nanofluids with various temperature, volume fraction and particle fraction were studied and compared with experimental reults. The results revealed that, at increase in particle concentration and pH of nanofluids at certain point would increase thermal conductivity and become stable at nominal temperature.  According to the results, the predicted values were in reasonable agreement with the experimental data as more than 95%  of the variation could be predicted by the CCRD model for thermal conductivity ratio and zeta potential.