Mathematical monitoring of agglomeration effects on thermophysical properties of water-based nanofluids using MLP and RSM

In this study, a post-processing ANN and RSM-based method was used to study the relative thermal conductivity (RTC) and relative viscosity (RV) of alumina nanoparticles (Al2O3 NPs) in water as nanofluid (NF). The objective mathematical functions of RV and RTC coefficients were modeled using the RSM and MLP methods based on the experimental data available in the literature. The maximum MLP model error for RTC and RV predictions was in the range from + 2.2 to - 1.4% and from + 4 to - 2.4%, respectively. In the RSM model, error was in the ranges from + 1.8 to - 2.2% and from + 10 to - 5% for the NF RTC and RV models, respectively. The PH number and four various agglomeration modes were used along with volume fraction (VF) as independent input variables to predict the behaviors of TCR and RV. The effect of different agglomeration modes was also investigated on RTC and RV of NF. Based on results behavior of RTC and RV versus PH and VF will be changed in different agglomeration modes.

Automated summary

In this study, a post-processing ANN and RSM-based method was used to investigate the relative thermal conductivity and relative viscosity of alumina nanoparticles in water as nanofluid. Mathematical models were developed using RSM and MLP methods based on experimental data, with the impact of different agglomeration modes and input variables explored. The results show that the behaviors of RTC and RV vary with pH, volume fraction, and agglomeration modes.