DEVELOPING OPTIMAL ARTIFICIAL NEURAL NETWORK (ANN) TO PREDICT THE SPECIFIC HEAT OF WATER-BASED YTTRIUM OXIDE (Y2O3) NANOFLUID ACCORDING TO THE EXPERIMENTAL DATA AND PROPOSING NEW CORRELATION

In this study, the specific heat values of yttrium oxide-water nanofluid prepared in five different volumetric concentrations using Y2O3 nanoparticles were measured experimentally using the DTA method. Using the experimental results obtained, multilayer perceptron, feed-forward back-propagation artificial neural network with 15 neurons in its hidden layer was developed. Forty-two of the total 60 experimental data were used in the training phase, 12 in the validation phase, and 6 in the test phase. In addition, a new mathematical correlation has been proposed to calculate the specific heat values of yttrium oxide-water nanofluid. The artificial neural network has predicted the specific heat values of yttrium oxide-water nanofluid with an average error of -0.0007%. The error rate of the proposed new correlation was calculated as -0.011% on average.