Experimental study for predicting the specific heat of water based Cu‐Al ₂ O ₃ hybrid nanofluid using artificial neural network and proposing new correlation

In this study, an artificial neural network model has been created in order to estimate the specific heat of Cu-Al2O3/water hybrid nanofluid based on temperature (T) and volume concentration (φ). Specific heat values of the Cu-Al2O3/water hybrid nanofluid prepared in five-volume concentration were measured experimentally in the 20°C to 65°C temperature range. The dataset was reserved into three primary parts, with the inclusion of 901 (70%) for the training, 257 (20%) for the test and 129 (10%) for the validation. As a result of comparison with experimental values, it is concluded that this model predicts specific heat with R-value of 0.99994 and an average relative error of approximately 5.84e-9. In addition, a mathematical correlation has been developed to estimate the specific heat of the Cu-Al2O3/water hybrid nanofluid. The data acquired from the mathematical correlation, developed, were in great correlation with all the experimental values with an average deviation of −0.005%. This result has revealed that the developed mathematical correlation is an ideal design for estimating the specific heat of the Cu-Al2O3/water hybrid nanofluid.