Prediction of Viscosity of Cobalt Ferrite/SAE50 Engine Oil based Nanofluids using well Trained Artificial Neutral Network (ANN) and Response Surface Methodology (RSM)
Heat transmission by ordinary fluids such as pure water, oil, and ethylene glycol is inefficient due to their low viscosity. To boost the efficiency of conventional fluids, very small percent of nanoparticles are added to the base fluids to prepare nanofluid. The impact of changing in viscosity can be used to investigate the rheological properties of nanofluids. In this paper, (CoFe2O4)/engine oil based nanofluids were prepared using two steps standard methodology. In first step, CoFe2O4 (CF) were synthesized using the sol-gel wet chemical process. The crystalline structure and morphology were confirmed using X-Ray diffraction analysis (XRD) and scanning electron microscopy (SEM), respectively. In second step, the standard procedure was adapted by taking several solid volume fractions of CF as Ø = 0, 0.25, 0.50, 0.75, and 1.0 %. Such percent of concentrations were dispersed in appropriate volume of engine oil using the ultrasonication for 5 h. After date, the viscosity of prepared five different nanofluids were determined at temperatures ranging from 40 to 80 °C. According to the findings, the viscosity of nanofluids (µnf) decreased as temperature increased while increased when the volume percentage of nanofluids Ø raised. Furthermore, total 25 experimental observations were considered to predict viscosity using an artificial neural network (ANN) and response surface methodology (RSM). The algorithm for building the ideal ANN architecture has been recommended in order to predict the fluid velocity of the CF/SAE-50 oil based nanofluid using MATLAB software. In order to determine the correctness of the predicted model, the mean square error (MSE) was calculated 0.0136.
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