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

Colak, Andac Batur2024-11-072024-11-0720201064-22852162-6561https://doi.org/10.1615/HeatTransRes.2020034724https://hdl.handle.net/11480/14164In 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.eninfo:eu-repo/semantics/closedAccessyttrium oxidenanofluidspecific heatartificial neural networkheat transferDEVELOPING 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 CORRELATIONArticle51171565158610.1615/HeatTransRes.20200347242-s2.0-85101935485Q3WOS:000618562300004Q2