Abstract
Artificial neural network/kriging interpolation method optimization method which is evaluated concerned the hybrid nanofluid composed of iron oxide (Fe2O3) and aluminum oxide (Al2O3) nano-powders to improve the thermal properties of 10w40 engine oil at different amounts of volume fraction and temperature. An input-target dataset contains 30 input-target pairs. The proposed model is examined at non-trained inputs throughout the investigated intervals beside the first derivatives of the thermal conductivity with respect to temperature and nanoparticle mass fraction. It can be seen that the obtained results have suitable smoothness and continuity. Accordingly, the kriging method shows the acceptable outcomes through the experimental prediction of Al2O3/Fe2O3 nanoparticles dispersed in 10w40 engine oil.
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