Prediction of friction factor of pure water flowing inside vertical smooth and microfin tubes by using artificial neural networks

Cebi A., AKDOĞAN E., Celen A., DALKILIÇ A. S.

HEAT AND MASS TRANSFER, cilt.53, sa.2, ss.673-685, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 53 Sayı: 2
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1007/s00231-016-1850-1
  • Dergi Adı: HEAT AND MASS TRANSFER
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.673-685
  • Erzincan Binali Yıldırım Üniversitesi Adresli: Hayır

Özet

An artificial neural network (ANN) model of friction factor in smooth and microfin tubes under heating, cooling and isothermal conditions was developed in this study. Data used in ANN was taken from a vertically positioned heat exchanger experimental setup. Multi-layered feed-forward neural network with backpropagation algorithm, radial basis function networks and hybrid PSO-neural network algorithm were applied to the database. Inputs were the ratio of cross sectional flow area to hydraulic diameter, experimental condition number depending on isothermal, heating, or cooling conditions and mass flow rate while the friction factor was the output of the constructed system. It was observed that such neural network based system could effectively predict the friction factor values of the flows regardless of their tube types. A dependency analysis to determine the strongest parameter that affected the network and database was also performed and tube geometry was found to be the strongest parameter of all as a result of analysis.