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
Küçük Resim Yok
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Begell House Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
yttrium oxide, nanofluid, specific heat, artificial neural network, heat transfer
Kaynak
Heat Transfer Research
WoS Q Değeri
Q2
Scopus Q Değeri
Q3
Cilt
51
Sayı
17
