Abstract
Stability and thermophysical properties of water-based magnetite (Fe3O4) material coated on multiwalled carbon nanotubes hybrid nanofluids was investigated. The in-situ growth approach was coupled with the chemical reduction method to make Fe3O4 coated multiwalled carbon nanotubes, and X-ray diffraction, vibrating sample magnetometer, and scanning electron microscopy were used to validate these findings. The experiments were conducted for different particle volume loadings (0.05% to 0.3%). Highest stability value of -48 my was achieved for phi = 0.05%. At, phi = 0.3% of nanofluid, the thermal conductivity was improved to 13.78%, and 28.33% at temperatures of 20 degrees C and 60 degrees C against water. Similarly, at phi = 0.3% of hybrid nanofluid, the viscosity has enhanced to 27.83%, and 50% at temperatures of 20 degrees C and 60 degrees C against water. Using the experimental data, sensitivity analysis was used to build Multi-Layer Perceptron Artificial Neural Networks (MLP-ANN) with appropriate topologies and training techniques. MLP-ANN was employed to establish the relationship between the inputs (temperature and mixture concentration) and the outputs (density, thermal conductivity, viscosity and, specific heat) for water-based magnetite (Fe3O4) material coated on multiwalled carbon nanotubes hybrid nanofluids. The model performances were evaluated using the coefficient of correlation (0.9938-0.9999), coefficient of determination (0.9854-0.9996), root mean squared error (0.0072-0.2626), mean absolute percentage error (0.001%-2.09%), and Nash-Sutcliffe efficiency (0.9856-0.9999). The model's uncertainty was measured with Theil's U2 (0.035-0.267). The results revealed that the MLP-ANN could consistently emulate the experimental testing conditions proficiently, even for diverse temperatures and concentrations, with significant accuracy. (C) 2021 Elsevier B.V. All rights reserved.